An expandable informatics framework for enhancing central cancer registries with digital pathology specimens, computational imaging tools, and advanced mining capabilities
Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is rep...
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| Published in | Journal of pathology informatics Vol. 13; no. 1; p. 5 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.01.2022
Wolters Kluwer India Pvt. Ltd Medknow Publications & Media Pvt. Ltd Wolters Kluwer - Medknow Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI’s Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features).
As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated.
Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics.
To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures. |
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| AbstractList | Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI’s Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features).
As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated.
Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics.
To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures. Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI's Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as ). As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated. Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics. To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures. Background: Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI’s Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features). Materials and methods: As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated. Results: Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics. Conclusion: To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures. Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI's Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features).BACKGROUNDPopulation-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI's Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features).As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated.MATERIALS AND METHODSAs part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated.Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics.RESULTSOur team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics.To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.CONCLUSIONTo meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures. |
| ArticleNumber | 100167 |
| Author | Harris, Gerald Gu, Annie Wang, Feiqiao Samaras, Dimitris Saltz, Joel H. Durbin, Eric B. Schymura, Maria Li, Nan Ward, Kevin Stroup, Antoinette M. Chen, Wenjin Banerjee, Imon DiPrima, Tammy Hands, Isaac Sadimin, Evita Kurc, Tahsin Abousamra, Shahira Foran, David J. Balsamo, Joseph Gupta, Rajarsi Sharma, Ashish Bremer, Erich |
| AuthorAffiliation | 6 Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA 2 Department of Pathology and Laboratory Medicine, Rutgers-Robert Wood Johnson Medical School, Piscataway, NJ, USA 10 Georgia State Cancer Registry, Georgia Department of Public Health, Atlanta, GA, USA 9 Department of Computer Science, Stony Brook University, Stony Brook, NY, USA 7 New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA 3 Kentucky Cancer Registry, Markey Cancer Center, University of Kentucky, Lexington, KY, USA 4 Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, Lexington, KY, USA 8 New York State Cancer Registry, New York State Department of Health, Albany, NY, USA 1 Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA 5 Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA |
| AuthorAffiliation_xml | – name: 10 Georgia State Cancer Registry, Georgia Department of Public Health, Atlanta, GA, USA – name: 2 Department of Pathology and Laboratory Medicine, Rutgers-Robert Wood Johnson Medical School, Piscataway, NJ, USA – name: 6 Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA – name: 9 Department of Computer Science, Stony Brook University, Stony Brook, NY, USA – name: 1 Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA – name: 3 Kentucky Cancer Registry, Markey Cancer Center, University of Kentucky, Lexington, KY, USA – name: 4 Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, Lexington, KY, USA – name: 8 New York State Cancer Registry, New York State Department of Health, Albany, NY, USA – name: 5 Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA – name: 7 New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA |
| Author_xml | – sequence: 1 givenname: David J. surname: Foran fullname: Foran, David J. email: foran@cinj.rutgers.edu organization: Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA – sequence: 2 givenname: Eric B. surname: Durbin fullname: Durbin, Eric B. organization: Kentucky Cancer Registry, Markey Cancer Center, University of Kentucky, Lexington, KY, USA – sequence: 3 givenname: Wenjin surname: Chen fullname: Chen, Wenjin organization: Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA – sequence: 4 givenname: Evita surname: Sadimin fullname: Sadimin, Evita organization: Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA – sequence: 5 givenname: Ashish surname: Sharma fullname: Sharma, Ashish organization: Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA – sequence: 6 givenname: Imon surname: Banerjee fullname: Banerjee, Imon organization: Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA – sequence: 7 givenname: Tahsin surname: Kurc fullname: Kurc, Tahsin organization: Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA – sequence: 8 givenname: Nan surname: Li fullname: Li, Nan organization: Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA – sequence: 9 givenname: Antoinette M. surname: Stroup fullname: Stroup, Antoinette M. organization: New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA – sequence: 10 givenname: Gerald surname: Harris fullname: Harris, Gerald organization: New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA – sequence: 11 givenname: Annie surname: Gu fullname: Gu, Annie organization: Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA – sequence: 12 givenname: Maria surname: Schymura fullname: Schymura, Maria organization: New York State Cancer Registry, New York State Department of Health, Albany, NY, USA – sequence: 13 givenname: Rajarsi surname: Gupta fullname: Gupta, Rajarsi organization: Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA – sequence: 14 givenname: Erich surname: Bremer fullname: Bremer, Erich organization: Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA – sequence: 15 givenname: Joseph surname: Balsamo fullname: Balsamo, Joseph organization: Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA – sequence: 16 givenname: Tammy surname: DiPrima fullname: DiPrima, Tammy organization: Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA – sequence: 17 givenname: Feiqiao surname: Wang fullname: Wang, Feiqiao organization: Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA – sequence: 18 givenname: Shahira surname: Abousamra fullname: Abousamra, Shahira organization: Department of Computer Science, Stony Brook University, Stony Brook, NY, USA – sequence: 19 givenname: Dimitris surname: Samaras fullname: Samaras, Dimitris organization: Department of Computer Science, Stony Brook University, Stony Brook, NY, USA – sequence: 20 givenname: Isaac surname: Hands fullname: Hands, Isaac organization: Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, Lexington, KY, USA – sequence: 21 givenname: Kevin surname: Ward fullname: Ward, Kevin organization: Georgia State Cancer Registry, Georgia Department of Public Health, Atlanta, GA, USA – sequence: 22 givenname: Joel H. surname: Saltz fullname: Saltz, Joel H. organization: Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35136672$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_ejmech_2025_117535 crossref_primary_10_32604_or_2022_024892 |
| Cites_doi | 10.1186/1471-2105-15-287 10.5858/arpa.2012-0033-OA 10.3389/fmed.2020.00245 10.1136/amiajnl-2011-000170 10.1038/ncomms12474 10.1016/j.jtho.2016.10.017 10.1038/s41597-020-0528-1 10.1038/srep32706 10.1186/1746-1596-7-42 10.1186/1471-2105-13-232 10.1200/CCI.20.00001 10.1109/4233.897058 10.1111/his.12284 10.1097/JTO.0b013e318288dbd8 10.1038/s41592-018-0261-2 10.1016/j.ajpath.2020.03.012 10.5858/arpa.2017-0496-CP 10.1093/annonc/mdr029 10.1016/j.tranon.2014.07.007 10.1016/j.celrep.2018.03.086 10.4103/jpi.jpi_52_18 10.1111/his.12993 10.15265/IY-2017-041 10.1093/ajcp/105.1.23 10.1093/annonc/mdp273 10.4103/jpi.jpi_82_18 10.1002/1097-0142(19930515)71:10<2971::AID-CNCR2820711014>3.0.CO;2-E 10.1158/0008-5472.CAN-17-0316 10.1109/JBHI.2020.2991043 10.1007/s10044-007-0066-x 10.2478/v10019-012-0009-z 10.1109/RBME.2009.2034865 10.1183/09031936.00219211 10.1016/S1470-2045(19)30154-8 10.1007/s40620-019-00638-7 10.1038/s41379-020-0561-9 10.1016/j.cmpb.2005.03.006 10.5858/arpa.2020-0034-OA 10.3109/03009734.2012.659294 10.1016/j.humpath.2015.09.012 10.1053/hupa.2001.21135 10.1016/j.ejca.2015.10.014 10.1093/bioinformatics/btx723 10.1136/amiajnl-2012-001538 10.1080/14712598.2017.1309387 10.4103/2153-3539.101782 10.1182/blood-2007-05-091850 10.1038/modpathol.2009.190 10.1016/j.media.2019.101563 10.4103/jpi.jpi_69_18 10.1016/j.urology.2010.08.031 10.1002/ajh.2830180108 10.1158/0008-5472.CAN-17-0339 10.1016/j.media.2016.06.037 10.1007/s11684-020-0782-9 10.1093/bioinformatics/btt623 10.1016/j.ajpath.2019.05.007 10.1109/TITB.2008.2008801 10.1126/scitranslmed.3002564 10.1016/j.immuni.2019.08.004 10.1016/j.ajpath.2012.01.040 10.1186/s12859-015-0831-6 10.1186/1746-1596-8-S1-S29 10.18632/oncotarget.14632 10.1016/S0046-8177(85)80106-4 10.1017/S1431927615015342 10.1097/DAD.0000000000000888 10.1046/j.1468-0734.2002.00301.x 10.1177/1176935117694349 10.5858/arpa.2018-0343-RA 10.1186/1746-1596-9-S1-S12 |
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| Keywords | Cancer registries Digital pathology Deep-learning Computational imaging deep-learning computational imaging digital pathology |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-sa/4.0 Copyright: © 2022 Journal of Pathology Informatics. This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
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| References | Foran, Chen, Chu (bb0240) 2017; 16 Jögi, Vaapil, Johansson, Påhlman (bb0165) 2012; 117 Roggli, Vollmer, Greenberg, McGavran, Spjut, Yesner (bb0050) 1985; 16 Yang, Qi, Xing, Kurc, Saltz, Foran (bb0410) 2014; 30 Tille, Vieira, Saint-Martin (bb0205) 2020; 33 Matasar, Shi, Silberstien (bb0025) 2012; 23 Cima, Brunelli, Parwani (bb0390) 2018; 9 Abousamra, Hou, Gupta (bb0280) 2019 Qi, Kim, Xing, Parashar, Foran, Yang (bb0425) 2012; 3 Falk, Mai, Bensch (bb0325) 2019; 16 Qi, Wang, Rodero (bb0405) 2014; 15 Pantanowitz, Michelow, Hazelhurst (bb0375) 2021; 145 Tuzel, Yang, Meer, Foran (bb0395) 2007; 10 Gabril, Yousef (bb0090) 2010; 23 Girolami, Gambaro, Ghimenton (bb0130) 2020; 33 Niazi, Parwani, Gurcan (bb0265) 2019; 20 Allsbrook, Mangold, Johnson, Lane, Lane, Epstein (bb0005) 2001; 32 Sharma, Tarbox, Kurc (bb0235) 2020; 4 Bozorgtabar, Mahapatra, Zlobec, Rau, Thiran (bb0255) 2020; 7 Wedman, Aladhami, Beste (bb0095) 2015; 21 Lee, Jedrych, Pantanowitz, Ho (bb0370) 2018; 40 Goodfellow, Pouget-Abadie, Mirza (bb0330) 2014 Saltz, Gupta, Hou (bb0285) 2018; 23 Panayides, Amini, Filipovic (bb0275) 2020; 24 Le, Samaras, Kurc, Gupta, Shroyer, Saltz (bb0290) 2019 Berney, Algaba, Camparo (bb0010) 2014; 64 Simonyan, Zisserman (bb0310) 2014 Warth, Stenzinger, von Brünneck (bb0060) 2012; 40 Netto, Eisenberger, Epstein (bb0040) 2011; 77 Yang, Tuzel, Chen (bb0105) 2009; 13 Chen, Wu, Huang (bb0155) 2010; 23 Chen, Schmidt, Parashar, Reiss, Foran (bb0415) 2007; 2 Zito Marino, Ascierto, Rossi (bb0225) 2017; 17 Gurcan, Boucheron, Can, Madabhushi, Rajpoot, Yener (bb0270) 2009; 2 Deng, Zhang, Yan (bb0260) 2020; 14 van Griethuysen, Fedorov, Parmar (bb0340) 2017; 77 Zarella, Bowman, Aeffner (bb0360) 2019; 143 Beck, Sangoi, Leung (bb0160) 2011; 3 Bueno-de-Mesquita, Nuyten, Wesseling, van Tinteren, Linn, van de Vijver (bb0015) 2010; 21 Thorsson, Gibbs, Brown (bb0200) 2019; 51 Brunelli, Beccari, Colombari (bb0350) 2014; 9 Rizzardi, Johnson, Vogel (bb0045) 2012; 7 Wang, Yang, Rong, Zhan, Xiao (bb0250) 2019; 189 Ojansivu, Linder, Rahtu (bb0170) 2013; 8 Yoon, Kim, Goo, Kim, Hahn (bb0070) 2016; 53 Yang, Chen, Meer, Salaru, Feldman, Foran (bb0420) 2007; 10 Ren, Karagoz, Gatza (bb0120) 2018; 5 Muenzel, Engels, Bruegel, Kehl, Rummeny, Metz (bb0030) 2012; 46 Yu, Zhang, Berry (bb0135) 2016; 7 Hou, Gupta, Van Arnam (bb0305) 2020; 7 Leo, Lee, Shih, Elliott, Feldman, Madabhushi (bb0145) 2016; 3 Chen, Meer, Georgescu, He, Goodell, Foran (bb0125) 2005; 79 Romo-Bucheli, Janowczyk, Gilmore, Romero, Madabhushi (bb0140) 2016; 6 Luo, Zang, Yang (bb0190) 2017; 12 Foran, Comaniciu, Meer, Goodell (bb0100) 2000; 4 Chennubhotla, Clarke, Fedorov (bb0180) 2017; 26 Le, Gupta, Hou (bb0295) 2020; 190 Madabhushi, Lee (bb0245) 2016; 33 Eccher, Neil, Ciangherotti (bb0385) 2016; 47 Colen, Foster, Gatenby (bb0185) 2014; 7 Hou, Agarwal, Samaras, Kurc, Gupta, Saltz (bb0300) 2019; 2019 He, Zhang, Ren, Sun (bb0320) 2016 Pantanowitz, Sharma, Carter, Kurc, Sussman, Saltz (bb0345) 2018; 9 Cooper, Gutman, Chisolm (bb0150) 2012; 180 Eriksen, Sørensen, Lindebjerg (bb0220) 2018; 11 Head, Savage, Cerezo (bb0080) 1985; 18 Sørensen, Hirsch, Gazdar, Olsen (bb0055) 1993; 71 Baumann, Nenninger, Harms (bb0085) 1996; 105 Nakazato, Maeshima, Ishikawa (bb0035) 2013; 8 Wilkins, Erber, Bareford (bb0065) 2008; 111 Cheng, Mo, Wang, Parwani, Feng, Huang (bb0175) 2018; 34 Wang, Pécot, Zynger, Machiraju, Shapiro, Huang (bb0195) 2013; 20 Szegedy, Ioffe, Vanhoucke, Alemi (bb0315) 2017 Aeffner, Zarella, Buchbinder (bb0365) 2019; 10 Foran, Yang, Chen (bb0110) 2011; 18 Koh, Kim, Kim, Go, Jeon, Chung (bb0215) 2017; 8 Kurc, Qi, Wang (bb0115) 2015; 16 Chen, McGee, Chen (bb0430) 2014; 9 Amgad, Stovgaard, Balslev (bb0210) 2020; 6 Graham, Vu, Raza (bb0335) 2019; 58 Griffin, Treanor (bb0355) 2017; 70 Grilley-Olson, Hayes, Moore (bb0020) 2013; 137 Saltz, Sharma, Iyer (bb0230) 2017; 77 Bennett (bb0075) 2002; 6 Cukierski, Nandy, Gudla (bb0400) 2012; 13 Evans, Bauer, Bui (bb0380) 2018; 142 Netto (10.4103/jpi.jpi_31_21_bb0040) 2011; 77 Wang (10.4103/jpi.jpi_31_21_bb0250) 2019; 189 Tille (10.4103/jpi.jpi_31_21_bb0205) 2020; 33 Cukierski (10.4103/jpi.jpi_31_21_bb0400) 2012; 13 Niazi (10.4103/jpi.jpi_31_21_bb0265) 2019; 20 He (10.4103/jpi.jpi_31_21_bb0320) 2016 Thorsson (10.4103/jpi.jpi_31_21_bb0200) 2019; 51 Ren (10.4103/jpi.jpi_31_21_bb0120) 2018; 5 Le (10.4103/jpi.jpi_31_21_bb0295) 2020; 190 Wang (10.4103/jpi.jpi_31_21_bb0195) 2013; 20 Gabril (10.4103/jpi.jpi_31_21_bb0090) 2010; 23 Bueno-de-Mesquita (10.4103/jpi.jpi_31_21_bb0015) 2010; 21 Madabhushi (10.4103/jpi.jpi_31_21_bb0245) 2016; 33 Deng (10.4103/jpi.jpi_31_21_bb0260) 2020; 14 Kurc (10.4103/jpi.jpi_31_21_bb0115) 2015; 16 Brunelli (10.4103/jpi.jpi_31_21_bb0350) 2014; 9 Gurcan (10.4103/jpi.jpi_31_21_bb0270) 2009; 2 Chen (10.4103/jpi.jpi_31_21_bb0415) 2007; 2 Head (10.4103/jpi.jpi_31_21_bb0080) 1985; 18 Ojansivu (10.4103/jpi.jpi_31_21_bb0170) 2013; 8 Yang (10.4103/jpi.jpi_31_21_bb0420) 2007; 10 Zarella (10.4103/jpi.jpi_31_21_bb0360) 2019; 143 Graham (10.4103/jpi.jpi_31_21_bb0335) 2019; 58 Qi (10.4103/jpi.jpi_31_21_bb0425) 2012; 3 Colen (10.4103/jpi.jpi_31_21_bb0185) 2014; 7 Amgad (10.4103/jpi.jpi_31_21_bb0210) 2020; 6 Abousamra (10.4103/jpi.jpi_31_21_bb0280) 2019 Qi (10.4103/jpi.jpi_31_21_bb0405) 2014; 15 Wedman (10.4103/jpi.jpi_31_21_bb0095) 2015; 21 Rizzardi (10.4103/jpi.jpi_31_21_bb0045) 2012; 7 Berney (10.4103/jpi.jpi_31_21_bb0010) 2014; 64 Griffin (10.4103/jpi.jpi_31_21_bb0355) 2017; 70 Bennett (10.4103/jpi.jpi_31_21_bb0075) 2002; 6 Luo (10.4103/jpi.jpi_31_21_bb0190) 2017; 12 Beck (10.4103/jpi.jpi_31_21_bb0160) 2011; 3 Cheng (10.4103/jpi.jpi_31_21_bb0175) 2018; 34 Foran (10.4103/jpi.jpi_31_21_bb0110) 2011; 18 Foran (10.4103/jpi.jpi_31_21_bb0100) 2000; 4 Foran (10.4103/jpi.jpi_31_21_bb0240) 2017; 16 Pantanowitz (10.4103/jpi.jpi_31_21_bb0345) 2018; 9 Aeffner (10.4103/jpi.jpi_31_21_bb0365) 2019; 10 Zito Marino (10.4103/jpi.jpi_31_21_bb0225) 2017; 17 Pantanowitz (10.4103/jpi.jpi_31_21_bb0375) 2021; 145 Romo-Bucheli (10.4103/jpi.jpi_31_21_bb0140) 2016; 6 Roggli (10.4103/jpi.jpi_31_21_bb0050) 1985; 16 Eriksen (10.4103/jpi.jpi_31_21_bb0220) 2018; 11 van Griethuysen (10.4103/jpi.jpi_31_21_bb0340) 2017; 77 Cima (10.4103/jpi.jpi_31_21_bb0390) 2018; 9 Goodfellow (10.4103/jpi.jpi_31_21_bb0330) 2014 Chen (10.4103/jpi.jpi_31_21_bb0430) 2014; 9 Panayides (10.4103/jpi.jpi_31_21_bb0275) 2020; 24 Cooper (10.4103/jpi.jpi_31_21_bb0150) 2012; 180 Evans (10.4103/jpi.jpi_31_21_bb0380) 2018; 142 Szegedy (10.4103/jpi.jpi_31_21_bb0315) 2017 Saltz (10.4103/jpi.jpi_31_21_bb0285) 2018; 23 Girolami (10.4103/jpi.jpi_31_21_bb0130) 2020; 33 Wilkins (10.4103/jpi.jpi_31_21_bb0065) 2008; 111 Saltz (10.4103/jpi.jpi_31_21_bb0230) 2017; 77 Jögi (10.4103/jpi.jpi_31_21_bb0165) 2012; 117 Falk (10.4103/jpi.jpi_31_21_bb0325) 2019; 16 Yang (10.4103/jpi.jpi_31_21_bb0410) 2014; 30 Sharma (10.4103/jpi.jpi_31_21_bb0235) 2020; 4 Yoon (10.4103/jpi.jpi_31_21_bb0070) 2016; 53 Matasar (10.4103/jpi.jpi_31_21_bb0025) 2012; 23 Baumann (10.4103/jpi.jpi_31_21_bb0085) 1996; 105 Lee (10.4103/jpi.jpi_31_21_bb0370) 2018; 40 Hou (10.4103/jpi.jpi_31_21_bb0305) 2020; 7 Warth (10.4103/jpi.jpi_31_21_bb0060) 2012; 40 Allsbrook (10.4103/jpi.jpi_31_21_bb0005) 2001; 32 Simonyan (10.4103/jpi.jpi_31_21_bb0310) 2014 Chennubhotla (10.4103/jpi.jpi_31_21_bb0180) 2017; 26 Koh (10.4103/jpi.jpi_31_21_bb0215) 2017; 8 Muenzel (10.4103/jpi.jpi_31_21_bb0030) 2012; 46 Chen (10.4103/jpi.jpi_31_21_bb0155) 2010; 23 Nakazato (10.4103/jpi.jpi_31_21_bb0035) 2013; 8 Bozorgtabar (10.4103/jpi.jpi_31_21_bb0255) 2020; 7 Le (10.4103/jpi.jpi_31_21_bb0290) 2019 Grilley-Olson (10.4103/jpi.jpi_31_21_bb0020) 2013; 137 Leo (10.4103/jpi.jpi_31_21_bb0145) 2016; 3 Yu (10.4103/jpi.jpi_31_21_bb0135) 2016; 7 Chen (10.4103/jpi.jpi_31_21_bb0125) 2005; 79 Tuzel (10.4103/jpi.jpi_31_21_bb0395) 2007; 10 Sørensen (10.4103/jpi.jpi_31_21_bb0055) 1993; 71 Yang (10.4103/jpi.jpi_31_21_bb0105) 2009; 13 Eccher (10.4103/jpi.jpi_31_21_bb0385) 2016; 47 Hou (10.4103/jpi.jpi_31_21_bb0300) 2019; 2019 |
| References_xml | – volume: 20 start-page: e253 year: 2019 end-page: e261 ident: bb0265 article-title: Digital pathology and artificial intelligence publication-title: Lancet Oncol – volume: 6 start-page: 330 year: 2002 end-page: 334 ident: bb0075 article-title: The FAB/MIC/WHO proposals for the classification of the chronic lymphoid leukemias publication-title: Rev Clin Exp Hematol – year: 2014 ident: bb0310 article-title: Very deep convolutional networks for large-scale image recognition publication-title: arXiv – volume: 8 start-page: 1 year: 2013 end-page: 4 ident: bb0170 article-title: Automated classification of breast cancer morphology in histopathological images publication-title: Diagn Pathol – volume: 23 start-page: 1213 year: 2010 end-page: 1220 ident: bb0155 article-title: Molecular subtype can predict the response and outcome of Chinese locally advanced breast cancer patients treated with preoperative therapy publication-title: Oncol Rep – volume: 47 start-page: 115 year: 2016 end-page: 120 ident: bb0385 article-title: Digital reporting of whole-slide images is safe and suitable for assessing organ quality in preimplantation renal biopsies publication-title: Hum Pathol – volume: 4 start-page: 265 year: 2000 end-page: 273 ident: bb0100 article-title: Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy publication-title: IEEE Trans Inf Technol Biomed – start-page: 541 year: 2019 end-page: 549 ident: bb0290 article-title: Pancreatic cancer detection in whole slide images using noisy label annotations publication-title: Medical Image Computing and Computer Assisted Intervention (MICCAI), October 13-17, 2019 – volume: 6 start-page: 32706 year: 2016 ident: bb0140 article-title: Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images publication-title: Sci Rep – volume: 7 start-page: 185 year: 2020 ident: bb0305 article-title: Dataset of segmented nuclei in hematoxylin and eosin stained histopathology images of ten cancer types publication-title: Sci Data – volume: 13 start-page: 291 year: 2009 end-page: 299 ident: bb0105 article-title: Pathminer: A web-based tool for computer-assisted diagnostics in pathology publication-title: IEEE Trans Inf Technol Biomed – volume: 9 start-page: 40 year: 2018 ident: bb0345 article-title: Twenty years of digital pathology: An overview of the road travelled, what is on the horizon, and the emergence of vendorneutral archives publication-title: J Pathol Inform – volume: 2 start-page: 147 year: 2009 end-page: 171 ident: bb0270 article-title: Histopathological image analysis: A review publication-title: IEEE Rev Biomed Eng – volume: 16 start-page: 569 year: 1985 end-page: 579 ident: bb0050 article-title: Lung cancer heterogeneity: A blinded and randomized study of 100 consecutive cases publication-title: Hum Pathol – volume: 34 start-page: 1024 year: 2018 end-page: 1030 ident: bb0175 article-title: Identification of topological features in renal tumor microenvironment associated with patient survival publication-title: Bioinformatics – volume: 23 start-page: 349 year: 2010 end-page: 358 ident: bb0090 article-title: Informatics for practicing anatomical pathologists: Marking a new era in pathology practice publication-title: Mod Pathol – volume: 70 start-page: 134 year: 2017 end-page: 145 ident: bb0355 article-title: Digital pathology in clinical use: Where are we now and what is holding us back? publication-title: Histopathology – volume: 18 start-page: 403 year: 2011 end-page: 415 ident: bb0110 article-title: Imageminer: A software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology publication-title: J Am Med Inform Assoc – volume: 10 start-page: 9 year: 2019 ident: bb0365 article-title: Introduction to digital image analysis in wholeslide imaging: A white paper from the Digital Pathology Association publication-title: J Pathol Inform – volume: 5 year: 2018 ident: bb0120 article-title: Recurrence analysis on prostate cancer patients with Gleason score 7 using integrated histopathology whole-slide images and genomic data through deep neural networks publication-title: J Med Imaging (Bellingham) – volume: 40 start-page: 17 year: 2018 end-page: 23 ident: bb0370 article-title: Validation of digital pathology for primary histopathological diagnosis of routine, inflammatory dermatopathology cases publication-title: Am J Dermatopathol – volume: 13 start-page: 232 year: 2012 ident: bb0400 article-title: Ranked retrieval of segmented nuclei for objective assessment of cancer gene repositioning publication-title: BMC Bioinform – volume: 16 start-page: 399 year: 2015 ident: bb0115 article-title: Scalable analysis of big pathology image data cohorts using efficient methods and high-performance computing strategies publication-title: BMC Bioinform – volume: 190 start-page: 1491 year: 2020 end-page: 1504 ident: bb0295 article-title: Utilizing automated breast cancer detection to identify spatial distributions of tumor-infiltrating lymphocytes in invasive breast cancer publication-title: Am J Pathol – volume: 105 start-page: 23 year: 1996 end-page: 30 ident: bb0085 article-title: Image analysis detects lineage-specific morphologic markers in leukemic blast cells publication-title: Am J Clin Pathol – volume: 4 start-page: 491 year: 2020 end-page: 499 ident: bb0235 article-title: PRISM: A platform for imaging in precision medicine publication-title: JCO Clin Cancer Inform – volume: 3 year: 2016 ident: bb0145 article-title: Evaluating stability of histomorphometric features across scanner and staining variations: Prostate cancer diagnosis from whole slide images publication-title: J Med Imaging (Bellingham) – volume: 9 start-page: 34 year: 2018 ident: bb0390 article-title: Validation of remote digital frozen sections for cancer and transplant intraoperative services publication-title: J Pathol Inform – volume: 7 start-page: 12474 year: 2016 ident: bb0135 article-title: Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features publication-title: Nat Commun – volume: 189 start-page: 1686 year: 2019 end-page: 1698 ident: bb0250 article-title: Pathology image analysis using segmentation deep learning algorithms publication-title: Am J Pathol – start-page: 770 year: 2016 end-page: 778 ident: bb0320 article-title: Deep residual learning for image recognition publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV – volume: 180 start-page: 2108 year: 2012 end-page: 2119 ident: bb0150 article-title: The tumor microenvironment strongly impacts master transcriptional regulators and gene expression class of glioblastoma publication-title: Am J Pathol – volume: 16 year: 2017 ident: bb0240 article-title: Roadmap to a comprehensive clinical data warehouse for precision medicine applications in oncology publication-title: Cancer Inform – volume: 3 start-page: 108ra113 year: 2011 ident: bb0160 article-title: Systematic analysis of breast cancer morphology uncovers stromal features associated with survival publication-title: Sci Transl Med – volume: 2 start-page: 373 year: 2007 end-page: 388 ident: bb0415 article-title: Decentralized data sharing of tissue microarrays for investigative research in oncology publication-title: Cancer Inform – volume: 142 start-page: 1383 year: 2018 end-page: 1387 ident: bb0380 article-title: US Food and Drug Administration approval of whole slide imaging for primary diagnosis: A key milestone is reached and new questions are raised publication-title: Arch Pathol Lab Med – volume: 33 start-page: 170 year: 2016 end-page: 175 ident: bb0245 article-title: Image analysis and machine learning in digital pathology: Challenges and opportunities publication-title: Med Image Anal – volume: 8 start-page: 13762 year: 2017 end-page: 13769 ident: bb0215 article-title: Prognostic implications of intratumoral CD103+ tumor-infiltrating lymphocytes in pulmonary squamous cell carcinoma publication-title: Oncotarget – volume: 64 start-page: 405 year: 2014 end-page: 411 ident: bb0010 article-title: The reasons behind variation in Gleason grading of prostatic biopsies: Areas of agreement and misconception among 266 European pathologists publication-title: Histopathology – volume: 77 start-page: e79 year: 2017 end-page: e82 ident: bb0230 article-title: A containerized software system for generation, management, and exploration of features from whole slide tissue images publication-title: Cancer Res – year: 2019 ident: bb0280 article-title: Learning from thresholds: Fully automated classification of tumor infiltrating lymphocytes for multiple cancer types publication-title: arXiv – volume: 18 start-page: 47 year: 1985 end-page: 57 ident: bb0080 article-title: Reproducibility of the French-American-British classification of acute leukemia: The Southwest Oncology Group Experience publication-title: Am J Hematol – volume: 12 start-page: 501 year: 2017 end-page: 509 ident: bb0190 article-title: Comprehensive computational pathological image analysis predicts lung cancer prognosis publication-title: J Thorac Oncol – volume: 14 start-page: 470 year: 2020 end-page: 487 ident: bb0260 article-title: Deep learning in digital pathology image analysis: A survey publication-title: Front Med – volume: 40 start-page: 1221 year: 2012 end-page: 1227 ident: bb0060 article-title: Interobserver variability in the application of the novel IASLC/ATS/ERS classification for pulmonary adenocarcinomas publication-title: Eur Respir J – volume: 145 start-page: 359 year: 2021 end-page: 364 ident: bb0375 article-title: A digital pathology solution to resolve the tissue floater conundrum publication-title: Arch Pathol Lab Med – volume: 143 start-page: 222 year: 2019 end-page: 234 ident: bb0360 article-title: A practical guide to whole slide imaging: A white paper from the digital pathology association publication-title: Arch Pathol Lab Med – volume: 17 start-page: 735 year: 2017 end-page: 746 ident: bb0225 article-title: Are tumor-infiltrating lymphocytes protagonists or background actors in patient selection for cancer immunotherapy? publication-title: Exp Opin Biol Ther – volume: 79 start-page: 59 year: 2005 end-page: 72 ident: bb0125 article-title: Image mining for investigative pathology using optimized feature extraction and data fusion publication-title: Comput Methods Programs Biomed – volume: 6 start-page: 16 year: 2020 ident: bb0210 article-title: International Immuno-Oncology Biomarker Working Group. Report on computational assessment of tumor infiltrating lymphocytes from the International Immuno-Oncology Biomarker Working Group. NPJ publication-title: Breast Cancer – volume: 7 start-page: 556 year: 2014 end-page: 569 ident: bb0185 article-title: NCI workshop report: Clinical and computational requirements for correlating imaging phenotypes with genomics signatures publication-title: Transl Oncol – volume: 21 start-page: 1573 year: 2015 end-page: 1581 ident: bb0095 article-title: A new image analysis method based on morphometric and fractal parameters for rapid evaluation of publication-title: Microsc Microanal – volume: 117 start-page: 217 year: 2012 end-page: 224 ident: bb0165 article-title: Cancer cell differentiation heterogeneity and aggressive behavior in solid tumors publication-title: Ups J Med Sci – volume: 9 year: 2014 ident: bb0430 article-title: Identification of druggable cancer driver genes amplified across TCGA datasets publication-title: PLoS One – volume: 10 start-page: 617 year: 2007 end-page: 625 ident: bb0420 article-title: High throughput analysis of breast cancer specimens on the grid publication-title: Med Image Comput Comput Assist Interv – start-page: 31 year: 2017 ident: bb0315 article-title: Inception-v4, Inception-ResNet and the impact of residual connections on learning publication-title: AAAI – volume: 51 start-page: 411 year: 2019 end-page: 412 ident: bb0200 article-title: Cancer Genome Atlas Research Network. The immune landscape of cancer publication-title: Immunity – volume: 10 start-page: 277 year: 2007 end-page: 290 ident: bb0395 article-title: Classification of hematologic malignancies using texton signatures publication-title: Pattern Anal Appl – volume: 33 start-page: 167 year: 2020 end-page: 176 ident: bb0130 article-title: Pre-implantation kidney biopsy: Value of the expertise in determining histological score and comparison with the whole organ on a series of discarded kidneys publication-title: J Nephrol – volume: 53 start-page: 5 year: 2016 end-page: 15 ident: bb0070 article-title: Observer variability in RECIST-based tumour burden measurements: A meta-analysis publication-title: Eur J Cancer – volume: 26 start-page: 110 year: 2017 end-page: 119 ident: bb0180 article-title: An assessment of imaging informatics for precision medicine in cancer publication-title: Yearb Med Inform – volume: 58 year: 2019 ident: bb0335 article-title: HoVer-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images publication-title: Med Image Anal – volume: 46 start-page: 8 year: 2012 end-page: 18 ident: bb0030 article-title: Intra- and inter-observer variability in measurement of target lesions: Implication on response evaluation according to RECIST 1.1 publication-title: Radiol. Oncol – volume: 16 start-page: 67 year: 2019 end-page: 70 ident: bb0325 article-title: U-net: Deep learning for cell counting, detection, and morphometry publication-title: Nat Methods – volume: 77 start-page: 1155 year: 2011 end-page: 1160 ident: bb0040 article-title: TAX 3501 Trial Investigators. Interobserver variability in histologic evaluation of radical prostatectomy between central and local pathologists: Findings of TAX 3501 multinational clinical trial publication-title: Urology – volume: 9 start-page: S12 year: 2014 ident: bb0350 article-title: iPathology cockpit diagnostic station: Validation according to College of American Pathologists Pathology and Laboratory Quality Center recommendation at the hospital trust and University of Verona publication-title: Diagn Pathol – volume: 30 start-page: 996 year: 2014 end-page: 1002 ident: bb0410 article-title: Parallel content-based sub-image retrieval using hierarchical searching publication-title: Bioinformatics – volume: 8 start-page: 736 year: 2013 end-page: 743 ident: bb0035 article-title: Interobserver agreement in the nuclear grading of primary pulmonary adenocarcinoma publication-title: J Thorac Oncol – volume: 32 start-page: 81 year: 2001 end-page: 88 ident: bb0005 article-title: Interobserver reproducibility of Gleason grading of prostatic carcinoma: General pathologist publication-title: Hum Pathol – year: 2014 ident: bb0330 article-title: Generative adversarial networks publication-title: arXiv – volume: 23 start-page: 159 year: 2012 end-page: 166 ident: bb0025 article-title: Expert second-opinion pathology review of lymphoma in the era of the World Health Organization classification publication-title: Ann Oncol – volume: 2019 start-page: 8533 year: 2019 end-page: 8542 ident: bb0300 article-title: Robust histopathology image analysis: To label or to synthesize? publication-title: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit – volume: 7 start-page: 42 year: 2012 ident: bb0045 article-title: Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring publication-title: Diagn Pathol – volume: 20 start-page: 680 year: 2013 end-page: 687 ident: bb0195 article-title: Identifying survival associated morphological features of triple negative breast cancer using multiple datasets publication-title: J Am Med Inform Assoc – volume: 7 start-page: 245 year: 2020 ident: bb0255 article-title: Editorial: Computational pathology publication-title: Front Med (Lausanne) – volume: 23 year: 2018 ident: bb0285 article-title: Cancer Genome Atlas Research Network. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images publication-title: Cell Rep – volume: 111 start-page: 60 year: 2008 end-page: 70 ident: bb0065 article-title: Bone marrow pathology in essential thrombocythemia: Interobserver reliability and utility for identifying disease subtypes publication-title: Blood – volume: 33 start-page: 2198 year: 2020 end-page: 2207 ident: bb0205 article-title: Tumor-infiltrating lymphocytes are associated with poor prognosis in invasive lobular breast carcinoma publication-title: Mod Pathol – volume: 3 start-page: 33 year: 2012 ident: bb0425 article-title: The analysis of image feature robustness using CometCloud publication-title: J Pathol Inform – volume: 71 start-page: 2971 year: 1993 end-page: 2976 ident: bb0055 article-title: Interobserver variability in histopathologic subtyping and grading of pulmonary adenocarcinoma publication-title: Cancer – volume: 15 start-page: 287 year: 2014 ident: bb0405 article-title: Content-based histopathology image retrieval using CometCloud publication-title: BMC Bioinform – volume: 137 start-page: 32 year: 2013 end-page: 40 ident: bb0020 article-title: Validation of interobserver agreement in lung cancer assessment: Hematoxylin-eosin diagnostic reproducibility for non-small cell lung cancer: The 2004 World Health Organization classification and therapeutically relevant subsets publication-title: Arch Pathol Lab Med – volume: 77 start-page: e104 year: 2017 end-page: e107 ident: bb0340 article-title: Computational radiomics system to decode the radiographic phenotype publication-title: Cancer Res – volume: 21 start-page: 40 year: 2010 end-page: 47 ident: bb0015 article-title: The impact of inter-observer variation in pathological assessment of node-negative breast cancer on clinical risk assessment and patient selection for adjuvant systemic treatment publication-title: Ann Oncol – volume: 11 start-page: 979 year: 2018 end-page: 987 ident: bb0220 article-title: The prognostic value of tumor-infiltrating lymphocytes in stage II colon cancer publication-title: A nationwide population-based study. Transl Oncol – volume: 24 start-page: 1837 year: 2020 end-page: 1857 ident: bb0275 article-title: AI in medical imaging informatics: Current challenges and future directions publication-title: IEEE J Biomed Health Inform – volume: 15 start-page: 287 year: 2014 ident: 10.4103/jpi.jpi_31_21_bb0405 article-title: Content-based histopathology image retrieval using CometCloud publication-title: BMC Bioinform doi: 10.1186/1471-2105-15-287 – volume: 137 start-page: 32 year: 2013 ident: 10.4103/jpi.jpi_31_21_bb0020 article-title: Validation of interobserver agreement in lung cancer assessment: Hematoxylin-eosin diagnostic reproducibility for non-small cell lung cancer: The 2004 World Health Organization classification and therapeutically relevant subsets publication-title: Arch Pathol Lab Med doi: 10.5858/arpa.2012-0033-OA – volume: 23 start-page: 1213 year: 2010 ident: 10.4103/jpi.jpi_31_21_bb0155 article-title: Molecular subtype can predict the response and outcome of Chinese locally advanced breast cancer patients treated with preoperative therapy publication-title: Oncol Rep – volume: 7 start-page: 245 year: 2020 ident: 10.4103/jpi.jpi_31_21_bb0255 article-title: Editorial: Computational pathology publication-title: Front Med (Lausanne) doi: 10.3389/fmed.2020.00245 – volume: 18 start-page: 403 year: 2011 ident: 10.4103/jpi.jpi_31_21_bb0110 article-title: Imageminer: A software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology publication-title: J Am Med Inform Assoc doi: 10.1136/amiajnl-2011-000170 – volume: 7 start-page: 12474 year: 2016 ident: 10.4103/jpi.jpi_31_21_bb0135 article-title: Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features publication-title: Nat Commun doi: 10.1038/ncomms12474 – year: 2019 ident: 10.4103/jpi.jpi_31_21_bb0280 article-title: Learning from thresholds: Fully automated classification of tumor infiltrating lymphocytes for multiple cancer types publication-title: arXiv – volume: 12 start-page: 501 year: 2017 ident: 10.4103/jpi.jpi_31_21_bb0190 article-title: Comprehensive computational pathological image analysis predicts lung cancer prognosis publication-title: J Thorac Oncol doi: 10.1016/j.jtho.2016.10.017 – volume: 7 start-page: 185 year: 2020 ident: 10.4103/jpi.jpi_31_21_bb0305 article-title: Dataset of segmented nuclei in hematoxylin and eosin stained histopathology images of ten cancer types publication-title: Sci Data doi: 10.1038/s41597-020-0528-1 – start-page: 770 year: 2016 ident: 10.4103/jpi.jpi_31_21_bb0320 article-title: Deep residual learning for image recognition – volume: 6 start-page: 32706 year: 2016 ident: 10.4103/jpi.jpi_31_21_bb0140 article-title: Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images publication-title: Sci Rep doi: 10.1038/srep32706 – volume: 7 start-page: 42 year: 2012 ident: 10.4103/jpi.jpi_31_21_bb0045 article-title: Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring publication-title: Diagn Pathol doi: 10.1186/1746-1596-7-42 – volume: 5 year: 2018 ident: 10.4103/jpi.jpi_31_21_bb0120 article-title: Recurrence analysis on prostate cancer patients with Gleason score 7 using integrated histopathology whole-slide images and genomic data through deep neural networks publication-title: J Med Imaging (Bellingham) – volume: 13 start-page: 232 year: 2012 ident: 10.4103/jpi.jpi_31_21_bb0400 article-title: Ranked retrieval of segmented nuclei for objective assessment of cancer gene repositioning publication-title: BMC Bioinform doi: 10.1186/1471-2105-13-232 – volume: 4 start-page: 491 year: 2020 ident: 10.4103/jpi.jpi_31_21_bb0235 article-title: PRISM: A platform for imaging in precision medicine publication-title: JCO Clin Cancer Inform doi: 10.1200/CCI.20.00001 – volume: 2019 start-page: 8533 year: 2019 ident: 10.4103/jpi.jpi_31_21_bb0300 article-title: Robust histopathology image analysis: To label or to synthesize? publication-title: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit – volume: 4 start-page: 265 year: 2000 ident: 10.4103/jpi.jpi_31_21_bb0100 article-title: Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy publication-title: IEEE Trans Inf Technol Biomed doi: 10.1109/4233.897058 – volume: 64 start-page: 405 year: 2014 ident: 10.4103/jpi.jpi_31_21_bb0010 article-title: The reasons behind variation in Gleason grading of prostatic biopsies: Areas of agreement and misconception among 266 European pathologists publication-title: Histopathology doi: 10.1111/his.12284 – volume: 8 start-page: 736 year: 2013 ident: 10.4103/jpi.jpi_31_21_bb0035 article-title: Interobserver agreement in the nuclear grading of primary pulmonary adenocarcinoma publication-title: J Thorac Oncol doi: 10.1097/JTO.0b013e318288dbd8 – volume: 16 start-page: 67 year: 2019 ident: 10.4103/jpi.jpi_31_21_bb0325 article-title: U-net: Deep learning for cell counting, detection, and morphometry publication-title: Nat Methods doi: 10.1038/s41592-018-0261-2 – volume: 190 start-page: 1491 year: 2020 ident: 10.4103/jpi.jpi_31_21_bb0295 article-title: Utilizing automated breast cancer detection to identify spatial distributions of tumor-infiltrating lymphocytes in invasive breast cancer publication-title: Am J Pathol doi: 10.1016/j.ajpath.2020.03.012 – volume: 142 start-page: 1383 year: 2018 ident: 10.4103/jpi.jpi_31_21_bb0380 article-title: US Food and Drug Administration approval of whole slide imaging for primary diagnosis: A key milestone is reached and new questions are raised publication-title: Arch Pathol Lab Med doi: 10.5858/arpa.2017-0496-CP – volume: 23 start-page: 159 year: 2012 ident: 10.4103/jpi.jpi_31_21_bb0025 article-title: Expert second-opinion pathology review of lymphoma in the era of the World Health Organization classification publication-title: Ann Oncol doi: 10.1093/annonc/mdr029 – volume: 7 start-page: 556 year: 2014 ident: 10.4103/jpi.jpi_31_21_bb0185 article-title: NCI workshop report: Clinical and computational requirements for correlating imaging phenotypes with genomics signatures publication-title: Transl Oncol doi: 10.1016/j.tranon.2014.07.007 – volume: 23 year: 2018 ident: 10.4103/jpi.jpi_31_21_bb0285 article-title: Cancer Genome Atlas Research Network. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images publication-title: Cell Rep doi: 10.1016/j.celrep.2018.03.086 – volume: 9 start-page: 34 year: 2018 ident: 10.4103/jpi.jpi_31_21_bb0390 article-title: Validation of remote digital frozen sections for cancer and transplant intraoperative services publication-title: J Pathol Inform doi: 10.4103/jpi.jpi_52_18 – volume: 10 start-page: 617 year: 2007 ident: 10.4103/jpi.jpi_31_21_bb0420 article-title: High throughput analysis of breast cancer specimens on the grid publication-title: Med Image Comput Comput Assist Interv – volume: 70 start-page: 134 year: 2017 ident: 10.4103/jpi.jpi_31_21_bb0355 article-title: Digital pathology in clinical use: Where are we now and what is holding us back? publication-title: Histopathology doi: 10.1111/his.12993 – volume: 26 start-page: 110 year: 2017 ident: 10.4103/jpi.jpi_31_21_bb0180 article-title: An assessment of imaging informatics for precision medicine in cancer publication-title: Yearb Med Inform doi: 10.15265/IY-2017-041 – volume: 105 start-page: 23 year: 1996 ident: 10.4103/jpi.jpi_31_21_bb0085 article-title: Image analysis detects lineage-specific morphologic markers in leukemic blast cells publication-title: Am J Clin Pathol doi: 10.1093/ajcp/105.1.23 – volume: 21 start-page: 40 year: 2010 ident: 10.4103/jpi.jpi_31_21_bb0015 article-title: The impact of inter-observer variation in pathological assessment of node-negative breast cancer on clinical risk assessment and patient selection for adjuvant systemic treatment publication-title: Ann Oncol doi: 10.1093/annonc/mdp273 – volume: 10 start-page: 9 year: 2019 ident: 10.4103/jpi.jpi_31_21_bb0365 article-title: Introduction to digital image analysis in wholeslide imaging: A white paper from the Digital Pathology Association publication-title: J Pathol Inform doi: 10.4103/jpi.jpi_82_18 – volume: 71 start-page: 2971 year: 1993 ident: 10.4103/jpi.jpi_31_21_bb0055 article-title: Interobserver variability in histopathologic subtyping and grading of pulmonary adenocarcinoma publication-title: Cancer doi: 10.1002/1097-0142(19930515)71:10<2971::AID-CNCR2820711014>3.0.CO;2-E – volume: 77 start-page: e79 year: 2017 ident: 10.4103/jpi.jpi_31_21_bb0230 article-title: A containerized software system for generation, management, and exploration of features from whole slide tissue images publication-title: Cancer Res doi: 10.1158/0008-5472.CAN-17-0316 – year: 2014 ident: 10.4103/jpi.jpi_31_21_bb0310 article-title: Very deep convolutional networks for large-scale image recognition publication-title: arXiv – volume: 24 start-page: 1837 year: 2020 ident: 10.4103/jpi.jpi_31_21_bb0275 article-title: AI in medical imaging informatics: Current challenges and future directions publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2020.2991043 – volume: 10 start-page: 277 year: 2007 ident: 10.4103/jpi.jpi_31_21_bb0395 article-title: Classification of hematologic malignancies using texton signatures publication-title: Pattern Anal Appl doi: 10.1007/s10044-007-0066-x – volume: 46 start-page: 8 year: 2012 ident: 10.4103/jpi.jpi_31_21_bb0030 article-title: Intra- and inter-observer variability in measurement of target lesions: Implication on response evaluation according to RECIST 1.1 publication-title: Radiol. Oncol doi: 10.2478/v10019-012-0009-z – volume: 9 year: 2014 ident: 10.4103/jpi.jpi_31_21_bb0430 article-title: Identification of druggable cancer driver genes amplified across TCGA datasets publication-title: PLoS One – volume: 2 start-page: 147 year: 2009 ident: 10.4103/jpi.jpi_31_21_bb0270 article-title: Histopathological image analysis: A review publication-title: IEEE Rev Biomed Eng doi: 10.1109/RBME.2009.2034865 – volume: 2 start-page: 373 year: 2007 ident: 10.4103/jpi.jpi_31_21_bb0415 article-title: Decentralized data sharing of tissue microarrays for investigative research in oncology publication-title: Cancer Inform – volume: 40 start-page: 1221 year: 2012 ident: 10.4103/jpi.jpi_31_21_bb0060 article-title: Interobserver variability in the application of the novel IASLC/ATS/ERS classification for pulmonary adenocarcinomas publication-title: Eur Respir J doi: 10.1183/09031936.00219211 – volume: 20 start-page: e253 year: 2019 ident: 10.4103/jpi.jpi_31_21_bb0265 article-title: Digital pathology and artificial intelligence publication-title: Lancet Oncol doi: 10.1016/S1470-2045(19)30154-8 – volume: 33 start-page: 167 year: 2020 ident: 10.4103/jpi.jpi_31_21_bb0130 article-title: Pre-implantation kidney biopsy: Value of the expertise in determining histological score and comparison with the whole organ on a series of discarded kidneys publication-title: J Nephrol doi: 10.1007/s40620-019-00638-7 – volume: 33 start-page: 2198 year: 2020 ident: 10.4103/jpi.jpi_31_21_bb0205 article-title: Tumor-infiltrating lymphocytes are associated with poor prognosis in invasive lobular breast carcinoma publication-title: Mod Pathol doi: 10.1038/s41379-020-0561-9 – volume: 79 start-page: 59 year: 2005 ident: 10.4103/jpi.jpi_31_21_bb0125 article-title: Image mining for investigative pathology using optimized feature extraction and data fusion publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2005.03.006 – volume: 145 start-page: 359 year: 2021 ident: 10.4103/jpi.jpi_31_21_bb0375 article-title: A digital pathology solution to resolve the tissue floater conundrum publication-title: Arch Pathol Lab Med doi: 10.5858/arpa.2020-0034-OA – volume: 117 start-page: 217 year: 2012 ident: 10.4103/jpi.jpi_31_21_bb0165 article-title: Cancer cell differentiation heterogeneity and aggressive behavior in solid tumors publication-title: Ups J Med Sci doi: 10.3109/03009734.2012.659294 – volume: 47 start-page: 115 year: 2016 ident: 10.4103/jpi.jpi_31_21_bb0385 article-title: Digital reporting of whole-slide images is safe and suitable for assessing organ quality in preimplantation renal biopsies publication-title: Hum Pathol doi: 10.1016/j.humpath.2015.09.012 – volume: 32 start-page: 81 year: 2001 ident: 10.4103/jpi.jpi_31_21_bb0005 article-title: Interobserver reproducibility of Gleason grading of prostatic carcinoma: General pathologist publication-title: Hum Pathol doi: 10.1053/hupa.2001.21135 – start-page: 31 year: 2017 ident: 10.4103/jpi.jpi_31_21_bb0315 article-title: Inception-v4, Inception-ResNet and the impact of residual connections on learning publication-title: AAAI – volume: 53 start-page: 5 year: 2016 ident: 10.4103/jpi.jpi_31_21_bb0070 article-title: Observer variability in RECIST-based tumour burden measurements: A meta-analysis publication-title: Eur J Cancer doi: 10.1016/j.ejca.2015.10.014 – volume: 34 start-page: 1024 year: 2018 ident: 10.4103/jpi.jpi_31_21_bb0175 article-title: Identification of topological features in renal tumor microenvironment associated with patient survival publication-title: Bioinformatics doi: 10.1093/bioinformatics/btx723 – volume: 20 start-page: 680 year: 2013 ident: 10.4103/jpi.jpi_31_21_bb0195 article-title: Identifying survival associated morphological features of triple negative breast cancer using multiple datasets publication-title: J Am Med Inform Assoc doi: 10.1136/amiajnl-2012-001538 – volume: 17 start-page: 735 year: 2017 ident: 10.4103/jpi.jpi_31_21_bb0225 article-title: Are tumor-infiltrating lymphocytes protagonists or background actors in patient selection for cancer immunotherapy? publication-title: Exp Opin Biol Ther doi: 10.1080/14712598.2017.1309387 – volume: 3 start-page: 33 year: 2012 ident: 10.4103/jpi.jpi_31_21_bb0425 article-title: The analysis of image feature robustness using CometCloud publication-title: J Pathol Inform doi: 10.4103/2153-3539.101782 – volume: 6 start-page: 16 year: 2020 ident: 10.4103/jpi.jpi_31_21_bb0210 article-title: International Immuno-Oncology Biomarker Working Group. Report on computational assessment of tumor infiltrating lymphocytes from the International Immuno-Oncology Biomarker Working Group. NPJ publication-title: Breast Cancer – volume: 111 start-page: 60 year: 2008 ident: 10.4103/jpi.jpi_31_21_bb0065 article-title: Bone marrow pathology in essential thrombocythemia: Interobserver reliability and utility for identifying disease subtypes publication-title: Blood doi: 10.1182/blood-2007-05-091850 – volume: 23 start-page: 349 year: 2010 ident: 10.4103/jpi.jpi_31_21_bb0090 article-title: Informatics for practicing anatomical pathologists: Marking a new era in pathology practice publication-title: Mod Pathol doi: 10.1038/modpathol.2009.190 – start-page: 541 year: 2019 ident: 10.4103/jpi.jpi_31_21_bb0290 article-title: Pancreatic cancer detection in whole slide images using noisy label annotations – volume: 58 year: 2019 ident: 10.4103/jpi.jpi_31_21_bb0335 article-title: HoVer-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images publication-title: Med Image Anal doi: 10.1016/j.media.2019.101563 – volume: 9 start-page: 40 year: 2018 ident: 10.4103/jpi.jpi_31_21_bb0345 article-title: Twenty years of digital pathology: An overview of the road travelled, what is on the horizon, and the emergence of vendorneutral archives publication-title: J Pathol Inform doi: 10.4103/jpi.jpi_69_18 – volume: 77 start-page: 1155 year: 2011 ident: 10.4103/jpi.jpi_31_21_bb0040 article-title: TAX 3501 Trial Investigators. Interobserver variability in histologic evaluation of radical prostatectomy between central and local pathologists: Findings of TAX 3501 multinational clinical trial publication-title: Urology doi: 10.1016/j.urology.2010.08.031 – volume: 18 start-page: 47 year: 1985 ident: 10.4103/jpi.jpi_31_21_bb0080 article-title: Reproducibility of the French-American-British classification of acute leukemia: The Southwest Oncology Group Experience publication-title: Am J Hematol doi: 10.1002/ajh.2830180108 – volume: 77 start-page: e104 year: 2017 ident: 10.4103/jpi.jpi_31_21_bb0340 article-title: Computational radiomics system to decode the radiographic phenotype publication-title: Cancer Res doi: 10.1158/0008-5472.CAN-17-0339 – volume: 3 year: 2016 ident: 10.4103/jpi.jpi_31_21_bb0145 article-title: Evaluating stability of histomorphometric features across scanner and staining variations: Prostate cancer diagnosis from whole slide images publication-title: J Med Imaging (Bellingham) – volume: 33 start-page: 170 year: 2016 ident: 10.4103/jpi.jpi_31_21_bb0245 article-title: Image analysis and machine learning in digital pathology: Challenges and opportunities publication-title: Med Image Anal doi: 10.1016/j.media.2016.06.037 – year: 2014 ident: 10.4103/jpi.jpi_31_21_bb0330 article-title: Generative adversarial networks publication-title: arXiv – volume: 14 start-page: 470 year: 2020 ident: 10.4103/jpi.jpi_31_21_bb0260 article-title: Deep learning in digital pathology image analysis: A survey publication-title: Front Med doi: 10.1007/s11684-020-0782-9 – volume: 30 start-page: 996 year: 2014 ident: 10.4103/jpi.jpi_31_21_bb0410 article-title: Parallel content-based sub-image retrieval using hierarchical searching publication-title: Bioinformatics doi: 10.1093/bioinformatics/btt623 – volume: 189 start-page: 1686 year: 2019 ident: 10.4103/jpi.jpi_31_21_bb0250 article-title: Pathology image analysis using segmentation deep learning algorithms publication-title: Am J Pathol doi: 10.1016/j.ajpath.2019.05.007 – volume: 13 start-page: 291 year: 2009 ident: 10.4103/jpi.jpi_31_21_bb0105 article-title: Pathminer: A web-based tool for computer-assisted diagnostics in pathology publication-title: IEEE Trans Inf Technol Biomed doi: 10.1109/TITB.2008.2008801 – volume: 3 start-page: 108ra113 year: 2011 ident: 10.4103/jpi.jpi_31_21_bb0160 article-title: Systematic analysis of breast cancer morphology uncovers stromal features associated with survival publication-title: Sci Transl Med doi: 10.1126/scitranslmed.3002564 – volume: 51 start-page: 411 year: 2019 ident: 10.4103/jpi.jpi_31_21_bb0200 article-title: Cancer Genome Atlas Research Network. The immune landscape of cancer publication-title: Immunity doi: 10.1016/j.immuni.2019.08.004 – volume: 180 start-page: 2108 year: 2012 ident: 10.4103/jpi.jpi_31_21_bb0150 article-title: The tumor microenvironment strongly impacts master transcriptional regulators and gene expression class of glioblastoma publication-title: Am J Pathol doi: 10.1016/j.ajpath.2012.01.040 – volume: 16 start-page: 399 year: 2015 ident: 10.4103/jpi.jpi_31_21_bb0115 article-title: Scalable analysis of big pathology image data cohorts using efficient methods and high-performance computing strategies publication-title: BMC Bioinform doi: 10.1186/s12859-015-0831-6 – volume: 8 start-page: 1 year: 2013 ident: 10.4103/jpi.jpi_31_21_bb0170 article-title: Automated classification of breast cancer morphology in histopathological images publication-title: Diagn Pathol doi: 10.1186/1746-1596-8-S1-S29 – volume: 8 start-page: 13762 year: 2017 ident: 10.4103/jpi.jpi_31_21_bb0215 article-title: Prognostic implications of intratumoral CD103+ tumor-infiltrating lymphocytes in pulmonary squamous cell carcinoma publication-title: Oncotarget doi: 10.18632/oncotarget.14632 – volume: 16 start-page: 569 year: 1985 ident: 10.4103/jpi.jpi_31_21_bb0050 article-title: Lung cancer heterogeneity: A blinded and randomized study of 100 consecutive cases publication-title: Hum Pathol doi: 10.1016/S0046-8177(85)80106-4 – volume: 21 start-page: 1573 year: 2015 ident: 10.4103/jpi.jpi_31_21_bb0095 article-title: A new image analysis method based on morphometric and fractal parameters for rapid evaluation of in situ mammalian mast cell status publication-title: Microsc Microanal doi: 10.1017/S1431927615015342 – volume: 40 start-page: 17 year: 2018 ident: 10.4103/jpi.jpi_31_21_bb0370 article-title: Validation of digital pathology for primary histopathological diagnosis of routine, inflammatory dermatopathology cases publication-title: Am J Dermatopathol doi: 10.1097/DAD.0000000000000888 – volume: 11 start-page: 979 year: 2018 ident: 10.4103/jpi.jpi_31_21_bb0220 article-title: The prognostic value of tumor-infiltrating lymphocytes in stage II colon cancer publication-title: A nationwide population-based study. Transl Oncol – volume: 6 start-page: 330 year: 2002 ident: 10.4103/jpi.jpi_31_21_bb0075 article-title: The FAB/MIC/WHO proposals for the classification of the chronic lymphoid leukemias publication-title: Rev Clin Exp Hematol doi: 10.1046/j.1468-0734.2002.00301.x – volume: 16 year: 2017 ident: 10.4103/jpi.jpi_31_21_bb0240 article-title: Roadmap to a comprehensive clinical data warehouse for precision medicine applications in oncology publication-title: Cancer Inform doi: 10.1177/1176935117694349 – volume: 143 start-page: 222 year: 2019 ident: 10.4103/jpi.jpi_31_21_bb0360 article-title: A practical guide to whole slide imaging: A white paper from the digital pathology association publication-title: Arch Pathol Lab Med doi: 10.5858/arpa.2018-0343-RA – volume: 9 start-page: S12 issue: suppl 1 year: 2014 ident: 10.4103/jpi.jpi_31_21_bb0350 article-title: iPathology cockpit diagnostic station: Validation according to College of American Pathologists Pathology and Laboratory Quality Center recommendation at the hospital trust and University of Verona publication-title: Diagn Pathol doi: 10.1186/1746-1596-9-S1-S12 |
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| SubjectTerms | Algorithms Automation Cancer Cancer registries Computational imaging Deep learning Demographics Digital imaging Digital pathology Digitization Epidemiology Image quality Informatics Lymphocytes Machine learning Medical imaging Original Pathology Population (statistical) Repositories Searching Software Tumors |
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| Title | An expandable informatics framework for enhancing central cancer registries with digital pathology specimens, computational imaging tools, and advanced mining capabilities |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S2153353922007611 https://dx.doi.org/10.4103/jpi.jpi_31_21 http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2022;volume=13;issue=1;spage=5;epage=5;aulast=Foran;type=0 https://www.ncbi.nlm.nih.gov/pubmed/35136672 https://www.proquest.com/docview/2625442425 https://www.proquest.com/docview/2627135426 https://pubmed.ncbi.nlm.nih.gov/PMC8794027 https://doaj.org/article/4bec4fa770934b85818907c54120c1d5 |
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