Preliminary study of AI-assisted diagnosis using FDG-PET/CT for axillary lymph node metastasis in patients with breast cancer
Background To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[ 18 F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. Materials and methods Two clinicians and the new AI...
Saved in:
Published in | EJNMMI research Vol. 11; no. 1; p. 10 |
---|---|
Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
25.01.2021
Springer Nature B.V SpringerOpen |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Background
To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[
18
F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies.
Materials and methods
Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[
18
F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy.
Results
Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged.
Conclusions
It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[
18
F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy. |
---|---|
AbstractList | BACKGROUNDTo improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[18F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. MATERIALS AND METHODSTwo clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[18F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy. RESULTSAlthough the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged. CONCLUSIONSIt is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[18F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy. Abstract Background To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[ 18 F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. Materials and methods Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[ 18 F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy. Results Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged. Conclusions It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[ 18 F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy. Abstract Background To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[18F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. Materials and methods Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[18F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy. Results Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged. Conclusions It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[18F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy. To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[ F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[ F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy. Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged. It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[ F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy. Background To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[ 18 F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. Materials and methods Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[ 18 F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy. Results Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged. Conclusions It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[ 18 F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy. |
ArticleNumber | 10 |
Author | Kudo, Kohsuke Takenaka, Junki Miyoshi, Yasuo Ogawa, Takahiro Hirata, Kenji Kitajima, Kazuhiro Togo, Ren Li, Zongyao Haseyama, Miki |
Author_xml | – sequence: 1 givenname: Zongyao surname: Li fullname: Li, Zongyao organization: Graduate School of Information Science and Technology, Hokkaido University – sequence: 2 givenname: Kazuhiro surname: Kitajima fullname: Kitajima, Kazuhiro organization: Department of Radiology, Division of Nuclear Medicine and PET Center, Hyogo College of Medicine – sequence: 3 givenname: Kenji orcidid: 0000-0003-0036-8975 surname: Hirata fullname: Hirata, Kenji email: khirata@med.hokudai.ac.jp organization: Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University – sequence: 4 givenname: Ren surname: Togo fullname: Togo, Ren organization: Education and Research Center for Mathematical and Data Science, Hokkaido University – sequence: 5 givenname: Junki surname: Takenaka fullname: Takenaka, Junki organization: Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University – sequence: 6 givenname: Yasuo surname: Miyoshi fullname: Miyoshi, Yasuo organization: Department of Breast and Endocrine Surgery, Hyogo College of Medicine – sequence: 7 givenname: Kohsuke surname: Kudo fullname: Kudo, Kohsuke organization: Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University – sequence: 8 givenname: Takahiro surname: Ogawa fullname: Ogawa, Takahiro organization: Faculty of Information Science and Technology, Hokkaido University – sequence: 9 givenname: Miki surname: Haseyama fullname: Haseyama, Miki organization: Faculty of Information Science and Technology, Hokkaido University |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33492478$$D View this record in MEDLINE/PubMed |
BookMark | eNp9Ustu1DAUtVARfdAfYIEssWET6mceG6RqaMtIlehikNhZTnyT8SixBzsBZsG_1zMppWWBZfl1zz334XOKjpx3gNAbSj5QWuYXkXIpSUYYzQgpJM3EC3TCaEWztHw7enI-RucxbkgaksqKl6_QMeeiYqIoT9DvuwC9HazTYYfjOJkd9i2-XGY6RhtHMNhY3TmfLniK1nX4-tNNdne1uliscOsD1r9s3--d-92wXWPnDeABRh3TTD7W4a0eLbgx4p92XOM6QLLhRrsGwmv0stV9hPOH_Qx9vb5aLT5nt19ulovL26zJSTFmrBZ12UJuNGOkYroSLBUAphWCU02ozmVeyqZuWgChNc0LImthQBamgFZzfoaWM6_xeqO2wQ4pY-W1VYcHHzqlw2ibHpQRDGohOXCZC0ZZzQgtRa2BU8NTjMT1cebaTvUApkmlBd0_I31ucXatOv9DFSWXrNgn8_6BIPjvE8RRDTY2kLrowE9RMVGmD-U5JQn67h_oxk_BpVYdUFJSWhUJxWZUE3yMAdrHZChRe7GoWSwqiUUdxKJEcnr7tIxHlz_SSAA-A2IyuQ7C39j_ob0HtjLNLg |
CitedBy_id | crossref_primary_10_2174_1573405619666230126093806 crossref_primary_10_4103_jmp_jmp_181_23 crossref_primary_10_1088_1361_6560_acfade crossref_primary_10_3389_fradi_2023_928639 crossref_primary_10_1007_s12149_021_01693_6 crossref_primary_10_1186_s12885_023_11638_z crossref_primary_10_7759_cureus_28945 crossref_primary_10_3389_fonc_2021_740336 crossref_primary_10_1016_j_semcancer_2023_09_001 crossref_primary_10_1186_s40644_022_00492_0 crossref_primary_10_3390_jimaging9100222 crossref_primary_10_1007_s41666_023_00144_3 crossref_primary_10_1007_s00330_022_09270_9 crossref_primary_10_3390_cancers15205088 crossref_primary_10_3390_jcm13102908 crossref_primary_10_3390_jpm11101029 crossref_primary_10_3390_ijms232113409 crossref_primary_10_1053_j_semnuclmed_2022_02_003 crossref_primary_10_2967_jnumed_121_262567 crossref_primary_10_3390_jcm12030968 crossref_primary_10_3390_molecules29122716 |
Cites_doi | 10.1258/ar.2012.110635 10.1002/cncr.21659 10.1186/s13550-017-0260-9 10.1136/svn-2017-000101 10.1007/s00259-009-1145-6 10.1146/annurev-bioeng-071516-044442 10.1007/s10549-010-0771-9 10.1200/JCO.2004.04.148 10.2196/15154 10.1016/j.crad.2016.12.001 10.1001/jama.2018.18932 10.1007/s11263-019-01228-7 10.1016/j.breast.2011.07.002 10.1016/j.media.2017.07.005 10.1007/s11604-015-0515-1 10.3322/caac.21551 10.1186/1471-2407-8-165 10.1109/CVPR.2016.90 10.1245/s10434-017-5860-0 10.1109/CVPR.2019.01096 10.1109/CVPR.2016.319 |
ContentType | Journal Article |
Copyright | The Author(s) 2021 The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: The Author(s) 2021 – notice: The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | C6C NPM AAYXX CITATION 3V. 7X7 7XB 8AO 8FE 8FG 8FI 8FJ 8FK ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO FYUFA GHDGH HCIFZ K9. M0S P5Z P62 PIMPY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
DOI | 10.1186/s13550-021-00751-4 |
DatabaseName | SpringerOpen PubMed CrossRef ProQuest Central (Corporate) ProQuest_Health & Medical Collection ProQuest Central (purchase pre-March 2016) ProQuest Pharma Collection ProQuest SciTech Collection ProQuest Technology Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection ProQuest Central Essentials AUTh Library subscriptions: ProQuest Central Technology Collection ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) SciTech Premium Collection (Proquest) (PQ_SDU_P3) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
DatabaseTitle | PubMed CrossRef Publicly Available Content Database Technology Collection ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Pharma Collection ProQuest Central China ProQuest Central Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) Advanced Technologies & Aerospace Database ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest One Academic ProQuest Central (Alumni) MEDLINE - Academic |
DatabaseTitleList | MEDLINE - Academic CrossRef Publicly Available Content Database PubMed |
Database_xml | – sequence: 1 dbid: C6C name: SpringerOpen url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine |
EISSN | 2191-219X |
EndPage | 10 |
ExternalDocumentID | oai_doaj_org_article_d42eb453e3564212b20184bae31d35cb 10_1186_s13550_021_00751_4 33492478 |
Genre | Journal Article |
GrantInformation_xml | – fundername: Hokkaido University grantid: Grants-in-Aid for Cross-departmental Young Researcher Grants funderid: http://dx.doi.org/10.13039/501100005946 – fundername: Japan Society for the Promotion of Science grantid: JP20K08015; JP17H01744; JP20K19857 funderid: http://dx.doi.org/10.13039/501100001691 – fundername: Japan Society for the Promotion of Science grantid: JP20K08015 – fundername: Japan Society for the Promotion of Science grantid: JP20K19857 – fundername: Hokkaido University grantid: Grants-in-Aid for Cross-departmental Young Researcher Grants – fundername: Japan Society for the Promotion of Science grantid: JP17H01744 – fundername: ; grantid: JP20K08015; JP17H01744; JP20K19857 – fundername: ; grantid: Grants-in-Aid for Cross-departmental Young Researcher Grants |
GroupedDBID | -A0 0R~ 3V. 40G 53G 5VS 7X7 8AO 8FE 8FG 8FI 8FJ AAFWJ AAJSJ AAKKN AAPBV AAYZJ ABDBF ABUWG ACACY ACGFS ACIHN ADBBV ADINQ AEAQA AENEX AFGXO AFKRA AFNRJ AFPKN AHBXF AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS BAPOH BCNDV BENPR BFQNJ BGLVJ BPHCQ BVXVI C24 C6C CCPQU EBS FYUFA GROUPED_DOAJ GX1 HCIFZ HMCUK HYE HZ~ IAO KQ8 M48 M~E OK1 P62 PIMPY PQQKQ PROAC RBZ RNS RPM RSV SMD SOJ U2A UKHRP ABEEZ ACULB ALIPV EBLON ITC NPM PGMZT AAYXX CITATION 7XB 8FK AZQEC DWQXO K9. PQEST PQUKI PRINS 7X8 5PM |
ID | FETCH-LOGICAL-c607t-2b4b8fe6da22092a942349edf4431a01a65685cbcfee4aa16705b4de57d7efa33 |
IEDL.DBID | RPM |
ISSN | 2191-219X |
IngestDate | Tue Oct 22 15:15:20 EDT 2024 Tue Sep 17 21:10:58 EDT 2024 Sat Aug 17 03:09:54 EDT 2024 Thu Oct 10 17:15:56 EDT 2024 Thu Sep 26 18:50:35 EDT 2024 Sat Sep 28 08:39:51 EDT 2024 Sat Dec 16 12:10:28 EST 2023 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 1 |
Keywords | Breast cancer Axillary lymph node AI-assisted diagnosis 2- Deep convolutional neural network f]FDG-PET/CT 2-[18f]FDG-PET/CT |
Language | English |
License | Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c607t-2b4b8fe6da22092a942349edf4431a01a65685cbcfee4aa16705b4de57d7efa33 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ORCID | 0000-0003-0036-8975 |
OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7835273/ |
PMID | 33492478 |
PQID | 2480551197 |
PQPubID | 2034773 |
PageCount | 1 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_d42eb453e3564212b20184bae31d35cb pubmedcentral_primary_oai_pubmedcentral_nih_gov_7835273 proquest_miscellaneous_2480753610 proquest_journals_2480551197 crossref_primary_10_1186_s13550_021_00751_4 pubmed_primary_33492478 springer_journals_10_1186_s13550_021_00751_4 |
PublicationCentury | 2000 |
PublicationDate | 2021-01-25 |
PublicationDateYYYYMMDD | 2021-01-25 |
PublicationDate_xml | – month: 01 year: 2021 text: 2021-01-25 day: 25 |
PublicationDecade | 2020 |
PublicationPlace | Berlin/Heidelberg |
PublicationPlace_xml | – name: Berlin/Heidelberg – name: Germany – name: Heidelberg |
PublicationTitle | EJNMMI research |
PublicationTitleAbbrev | EJNMMI Res |
PublicationTitleAlternate | EJNMMI Res |
PublicationYear | 2021 |
Publisher | Springer Berlin Heidelberg Springer Nature B.V SpringerOpen |
Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V – name: SpringerOpen |
References | Riegger, Koeninger, Hartung, Otterbach, Kimmig, Forsting (CR4) 2012; 53 Asan, Bayrak, Choudhury (CR17) 2020; 22 CR6 Arriagada, Le, Dunant, Tubiana, Contesso (CR2) 2006; 106 Jiang, Jiang, Zhi, Dong, Li, Ma (CR19) 2017; 2 Robertson, Hand, Kell (CR8) 2011; 20 CR16 CR15 Shen, Wu, Suk (CR14) 2017; 19 Siegel, Miller, Jemal (CR1) 2019; 69 Peare, Staff, Heys (CR7) 2010; 123 CR20 Litjens, Kooi, Bejnord, Setio, Ciompi, Ghafoorian (CR10) 2017; 4 Selvaraju, Cogswell, Das, Vedantam, Parikh, Batra (CR21) 2020; 128 Heusner, Kuemmel, Hahn, Koeninger, Otterbach, Hamami (CR3) 2009; 36 Liang, Yu, Wen, Xie, Cai, Yang (CR5) 2017; 72 Wang, Zhou, Li, Chen, Lu, Wang (CR11) 2017; 7 Kitajima, Fukushima, Miyoshi, Katsuura, Igarashi, Kawanaka (CR9) 2016; 34 Ueda, Tsuda, Asakawa, Omata, Fukatsu, Kondo (CR13) 2007; 8 Wahl, Siegel, Coleman, Gatsonis (CR12) 2004; 22 Maddox, Rumsfeld, Payne (CR18) 2019; 321 O Asan (751_CR17) 2020; 22 TM Maddox (751_CR18) 2019; 321 751_CR16 D Shen (751_CR14) 2017; 19 TA Heusner (751_CR3) 2009; 36 S Ueda (751_CR13) 2007; 8 G Litjens (751_CR10) 2017; 4 R Peare (751_CR7) 2010; 123 R Arriagada (751_CR2) 2006; 106 H Wang (751_CR11) 2017; 7 IJ Robertson (751_CR8) 2011; 20 RL Siegel (751_CR1) 2019; 69 F Jiang (751_CR19) 2017; 2 RR Selvaraju (751_CR21) 2020; 128 C Riegger (751_CR4) 2012; 53 RL Wahl (751_CR12) 2004; 22 K Kitajima (751_CR9) 2016; 34 751_CR20 751_CR6 751_CR15 X Liang (751_CR5) 2017; 72 |
References_xml | – volume: 53 start-page: 1092 year: 2012 end-page: 1098 ident: CR4 article-title: Comparison of the diagnostic value of FDG-PET/CT and axillary ultrasound for the detection of lymph node metastases in breast cancer patients publication-title: Acta Radiol doi: 10.1258/ar.2012.110635 contributor: fullname: Forsting – volume: 106 start-page: 743 year: 2006 end-page: 750 ident: CR2 article-title: Twenty-five years of follow-up in patients with operable breast carcinoma: correlation between clinicopathologic factors and the risk of death in each 5-year period publication-title: Cancer doi: 10.1002/cncr.21659 contributor: fullname: Contesso – volume: 7 start-page: 11 year: 2017 ident: CR11 article-title: Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18 F-FDG PET/CT images publication-title: EJNMMI Res doi: 10.1186/s13550-017-0260-9 contributor: fullname: Wang – volume: 2 start-page: 230 issue: 4 year: 2017 end-page: 243 ident: CR19 article-title: Artificial intelligence in healthcare: past, present and future publication-title: Stroke Vasc Neurol doi: 10.1136/svn-2017-000101 contributor: fullname: Ma – volume: 36 start-page: 1543 year: 2009 end-page: 1550 ident: CR3 article-title: Diagnostic value of full-dose FDG PET/CT for axillary lymph node staging in breast cancer patients publication-title: Eur J Nucl Med Mol Imaging doi: 10.1007/s00259-009-1145-6 contributor: fullname: Hamami – volume: 19 start-page: 221 year: 2017 end-page: 248 ident: CR14 article-title: Deep learning in medical image analysis publication-title: Annu Rev Biomed Eng doi: 10.1146/annurev-bioeng-071516-044442 contributor: fullname: Suk – ident: CR15 – ident: CR16 – volume: 123 start-page: 281 year: 2010 end-page: 290 ident: CR7 article-title: The use of FDG-PET in assessing axillary lymph node status in breast cancer: a systematic review and meta-analysis of the literature publication-title: Breast Cancer Res Treat doi: 10.1007/s10549-010-0771-9 contributor: fullname: Heys – volume: 22 start-page: 277 year: 2004 end-page: 285 ident: CR12 article-title: Prospective multicenter study of axillary nodal staging by positron emission tomography in breast cancer: a report of the staging breast cancer with PET Study Group publication-title: J Clin Oncol doi: 10.1200/JCO.2004.04.148 contributor: fullname: Gatsonis – volume: 22 start-page: e15154 issue: 6 year: 2020 ident: CR17 article-title: Artificial intelligence and human trust in healthcare: focus on clinicians publication-title: J Med Internet Res doi: 10.2196/15154 contributor: fullname: Choudhury – volume: 72 start-page: 295 year: 2017 end-page: 301 ident: CR5 article-title: MRI and FDG-PET/CT based assessment of axillary lymph node metastasis in early breast cancer: a meta-analysis publication-title: Clin Radiol doi: 10.1016/j.crad.2016.12.001 contributor: fullname: Yang – volume: 321 start-page: 31 issue: 1 year: 2019 end-page: 32 ident: CR18 article-title: Questions for artificial intelligence in health care publication-title: JAMA doi: 10.1001/jama.2018.18932 contributor: fullname: Payne – volume: 128 start-page: 336 year: 2020 end-page: 359 ident: CR21 article-title: Grad-CAM: visual explanations from deep networks via gradient-based localization publication-title: Int J Comput Vis doi: 10.1007/s11263-019-01228-7 contributor: fullname: Batra – ident: CR6 – volume: 20 start-page: 491 year: 2011 end-page: 494 ident: CR8 article-title: FDG-PET/CT in the staging of local/regional metastases in breast cancer publication-title: The Breast doi: 10.1016/j.breast.2011.07.002 contributor: fullname: Kell – volume: 4 start-page: 60 year: 2017 end-page: 88 ident: CR10 article-title: A survey on deep learning in medical image analysis publication-title: Med Image Anal doi: 10.1016/j.media.2017.07.005 contributor: fullname: Ghafoorian – volume: 34 start-page: 220 year: 2016 end-page: 228 ident: CR9 article-title: Diagnostic and prognostic value of 18 F-FDG PET/CT for axillary lymph node staging in patients with breast cancer publication-title: Jpn J Radiol doi: 10.1007/s11604-015-0515-1 contributor: fullname: Kawanaka – volume: 69 start-page: 7 year: 2019 end-page: 34 ident: CR1 article-title: Cancer statistics, 2019 publication-title: CA Cancer J Clin doi: 10.3322/caac.21551 contributor: fullname: Jemal – volume: 8 start-page: 165 year: 2007 ident: CR13 article-title: Utility of 18F-fluoro-deoxyglucose emission tomography/computed tomography fusion imaging (18F-FDG PET/CT) in combination with ultrasonography for axillary staging in primary breast cancer publication-title: BMC Cancer doi: 10.1186/1471-2407-8-165 contributor: fullname: Kondo – ident: CR20 – volume: 22 start-page: 277 year: 2004 ident: 751_CR12 publication-title: J Clin Oncol doi: 10.1200/JCO.2004.04.148 contributor: fullname: RL Wahl – volume: 36 start-page: 1543 year: 2009 ident: 751_CR3 publication-title: Eur J Nucl Med Mol Imaging doi: 10.1007/s00259-009-1145-6 contributor: fullname: TA Heusner – volume: 4 start-page: 60 year: 2017 ident: 751_CR10 publication-title: Med Image Anal doi: 10.1016/j.media.2017.07.005 contributor: fullname: G Litjens – volume: 72 start-page: 295 year: 2017 ident: 751_CR5 publication-title: Clin Radiol doi: 10.1016/j.crad.2016.12.001 contributor: fullname: X Liang – volume: 53 start-page: 1092 year: 2012 ident: 751_CR4 publication-title: Acta Radiol doi: 10.1258/ar.2012.110635 contributor: fullname: C Riegger – volume: 2 start-page: 230 issue: 4 year: 2017 ident: 751_CR19 publication-title: Stroke Vasc Neurol doi: 10.1136/svn-2017-000101 contributor: fullname: F Jiang – volume: 321 start-page: 31 issue: 1 year: 2019 ident: 751_CR18 publication-title: JAMA doi: 10.1001/jama.2018.18932 contributor: fullname: TM Maddox – volume: 106 start-page: 743 year: 2006 ident: 751_CR2 publication-title: Cancer doi: 10.1002/cncr.21659 contributor: fullname: R Arriagada – volume: 123 start-page: 281 year: 2010 ident: 751_CR7 publication-title: Breast Cancer Res Treat doi: 10.1007/s10549-010-0771-9 contributor: fullname: R Peare – ident: 751_CR15 doi: 10.1109/CVPR.2016.90 – volume: 128 start-page: 336 year: 2020 ident: 751_CR21 publication-title: Int J Comput Vis doi: 10.1007/s11263-019-01228-7 contributor: fullname: RR Selvaraju – ident: 751_CR6 doi: 10.1245/s10434-017-5860-0 – ident: 751_CR16 doi: 10.1109/CVPR.2019.01096 – volume: 69 start-page: 7 year: 2019 ident: 751_CR1 publication-title: CA Cancer J Clin doi: 10.3322/caac.21551 contributor: fullname: RL Siegel – volume: 22 start-page: e15154 issue: 6 year: 2020 ident: 751_CR17 publication-title: J Med Internet Res doi: 10.2196/15154 contributor: fullname: O Asan – volume: 8 start-page: 165 year: 2007 ident: 751_CR13 publication-title: BMC Cancer doi: 10.1186/1471-2407-8-165 contributor: fullname: S Ueda – volume: 20 start-page: 491 year: 2011 ident: 751_CR8 publication-title: The Breast doi: 10.1016/j.breast.2011.07.002 contributor: fullname: IJ Robertson – volume: 19 start-page: 221 year: 2017 ident: 751_CR14 publication-title: Annu Rev Biomed Eng doi: 10.1146/annurev-bioeng-071516-044442 contributor: fullname: D Shen – volume: 7 start-page: 11 year: 2017 ident: 751_CR11 publication-title: EJNMMI Res doi: 10.1186/s13550-017-0260-9 contributor: fullname: H Wang – ident: 751_CR20 doi: 10.1109/CVPR.2016.319 – volume: 34 start-page: 220 year: 2016 ident: 751_CR9 publication-title: Jpn J Radiol doi: 10.1007/s11604-015-0515-1 contributor: fullname: K Kitajima |
SSID | ssj0000515938 |
Score | 2.3802464 |
Snippet | Background
To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[
18
F]FDG-PET/CT, we constructed an... To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[ F]FDG-PET/CT, we constructed an artificial... Abstract Background To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[ 18 F]FDG-PET/CT, we... BackgroundTo improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[18F]FDG-PET/CT, we constructed an... BACKGROUNDTo improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[18F]FDG-PET/CT, we constructed an... Abstract Background To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[18F]FDG-PET/CT, we constructed... |
SourceID | doaj pubmedcentral proquest crossref pubmed springer |
SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
StartPage | 10 |
SubjectTerms | 2-[18f]FDG-PET/CT Accuracy Advanced Image Analysis (Artificial Intelligence AI-assisted diagnosis Artificial intelligence Artificial neural networks Axillary lymph node Biopsy Breast cancer Cardiac Imaging Computed tomography Deep convolutional neural network Diagnosis Diagnostic systems Fluorine isotopes Imaging Learning Lymphatic system Mastectomy Medicine Medicine & Public Health Metastasis Model accuracy Nuclear Medicine Oncology Original Research Orthopedics Radiology Radiomics |
SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQD4gLAsoj0CIjcQNrE7_iHNvSbUEq6mEr9WbZ8RhWKlm0m0r0wH9n7GSXLg9x4RrHkjUz9nzjGX9DyOu6iq5B18GkxthEttwwp1VkjRPctU0MIXdrOPuoTy_kh0t1eavVV6oJG-iBB8FNguTgpRIgVHqTyT16LCO9A1EFoVqfT99K3QqmRlZv1QizfiVj9GRVoWctWapISG4SA6ctT5QJ-_-EMn8vlvwlY5od0fQBuT8iSHowrPwhuQPdI3L3bMyR75Lv50u4yr26ljc0s8fSRaQH7xnC5KTTQMNQXjdf0VT1_olO352w8-PZ5GhGEcJS9y11IsLJVzeoatotAtAv0DvEkWnOvKMjGeuKpltc6lNde0_bZD_Lx-Riejw7OmVjkwXW6rLuGffSmwg6OM7LhrsG8ZVsIESJ0MKVFWpOG5RzGwGkc5WuS-VlAFWHGqIT4gnZ6RYdPCO0BBejTolL9HmtaoyLyrjKtwrwRAVekDdrgduvA5eGzTGI0XZQj0X12KweKwtymHSy-TPxYOcPaB12tA77L-soyN5ao3bcnCvLpSlVzp8W5NVmGLdVypW4DhbXwz8YySG4LMjTwQA2KxGJ0VHWpiD1lmlsLXV7pJt_ztTd6Z4NAWNB3q6N6Oey_i6K5_9DFC_IPZ6tv2Jc7ZGdfnkN-wioev8y750fcQMcAg priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Technology Collection dbid: 8FG link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagSIgL4k1KQUbiBtYmjp04J1RKtwWpqIet1Jtlx3a7UklKNpXaA_-dGSfZanldY1ty_I09n2fGM4S8K7NgKlAdTBRwNxE1V8wUMrDK5NzUVXAuVms4-lYcnoivp_J0NLitxrDK6UyMB7Vra7SRz7hQqYxOr4-XPxhWjULv6lhC4y65l2EmPHwpPj9Y21iwfkmVq-mtjCpmqwz0a8owLgGVJVyfNvRRTNv_N675Z8jkb37TqI7mj8jDkUfS3QH4x-SOb56Q-0ejp_wp-Xnc-YtYsau7oTGHLG0D3f3CgCwjso66IchuuaIY-35G558P2PH-Yra3oEBkqbnGekQw-OIGAKdN6zz97nsDbBLHLBs6pmRdUbTlUovR7T2tUYq6Z-Rkvr_YO2RjqQVWF2nZM26FVcEXznCeVtxUwLJE5V0QQDBMmgF-hZK1rYP3wpisKFNphfOydKUPJs-fk62mbfxLQlNvQijQfQmar5aVMkEqk9laejhXPU_I-2nB9eWQUUPHm4gq9ACPBnh0hEeLhHxCTNY9MRt2_NB2Z3rcXNoJ7q2Quc8lvtvlFliNEtb4PHM5zDohOxOietyiK30rUAl5u26GzYUeE9P49mroA_c5oJgJeTEIwHomOeZ1FKVKSLkhGhtT3WxplucxgTda24A2JuTDJES30_r3Umz__y9ekQc8ynXGuNwhW3135V8DYertm7grfgFv9xQB priority: 102 providerName: ProQuest – databaseName: Scholars Portal Open Access Journals dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwdV1Lb9QwEB6VIlW9IN4ECjISNzBNHDuPA0KldClIi3rYlfZm2bFdVloSyG6l7oH_zthJFha2lxzy0sjzTeYbz2QG4FWeOFWi66A8w9iEV6ygKhOOliplqiqdMWFaw_hrdj7lX2ZitgfDuKN-AZc7Qzs_T2raLt5e_1y_R4N_Fwy-yI6XCTrNmPpiA-8BMSa6BbcZT7lH_Lin-32vb1GG4dZopwnFw2z4j2bnaw7hIPXd-7gfwvaX2wrd_XdR0v8rK_9JrwavNboLd3q6SU46fNyDPVvfh4Nxn1B_AL8uWrsIg73aNQmtZknjyMlnipzaA8AQ09XizZfEl8hfktHHT_TibHJ8OiHId4m69mOL8OHFGnFB6sZY8t2uFJJO_8y8Jn3n1iXxW75E-yL4Fak82NqHMB2dTU7PaT-RgVZZnK8o01wXzmZGMRaXTJVIxnhpjePIQ1ScoJqzQlS6ctZypZIsj4Xmxorc5NapNH0E-3VT2ydAYqucy3yWEx1kJcpCOVGoRFfC4ufXsgheDwsuf3SNN2QIWIpMdpqSqCkZNCV5BB-8TjZ3-qbZ4UTTXsreBqXhzGouUpsK_3sv00h-Cq6VTROTotQRHA0alQMQJeNFLEKyNYKXm8togz6xomrbXHX3YNiHTDSCxx0ANpIMAIog34LGlqjbV-r5t9Dn22_KIbuM4M0Aoj9i3bwUT28U4RkcsoBuhL44gv1Ve2WfI6Va6RfBTn4DlFQZBg priority: 102 providerName: Scholars Portal – databaseName: SpringerOpen dbid: C24 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwELWgSIgL4ptAi4zEDawmju04x7LtUpCKethKvVl2PC4rlQRltxI98N8ZO8lWC-XANbalkd-M501mPCbkXVUEW6PrYEJhbCIarplVMrDaltw2dfA-vdZw8lUdn4kv5_L85h53KnafMpLpoE5WrdX-qkDPmLNYURDdHAY-d8m9SB6iWs_GKw5jQ29Zl3q6IHPr0i0nlHr130Yw_66T_CNZmnzQ_BF5OJJHejCg_ZjcgfYJuX8ypsefkl-nPVymZ7r6a5oax9Iu0IPPDBlyhNNTP1TWLVc0Frxf0PnhJ3Z6tNifLSiyV2p_xkeIcPHlNaJM284D_Q5rixQyrlm2dOzDuqLxBy51saR9TZuoOv0zcjY_WsyO2fi-AmtUXq0Zd8LpAMpbzvOa2xqplajBB4GswuYFgqa0bFwTAIS1hapy6YQHWfkKgi3L52Sn7Vp4SWgONgQVc5bo7hpZaxuktoVrJOBhCjwj76cNNz-GNhomhR9amQEeg_CYBI8RGfkYMdnMjC2w04euvzCjRRkvODghSyhlvKzLHVIZLZyFsvAlSp2R3QlRM9rlynChc5lSpxl5uxlGi4ppEttCdzXMwSAOeWVGXgwKsJGkjM0cRaUzUm2pxpao2yPt8lvq2h1_sSFXzMiHSYluxPr3Vrz6v-mvyQOe9LxgXO6SnXV_BXvImtbuTbKS3zvlEDo priority: 102 providerName: Springer Nature |
Title | Preliminary study of AI-assisted diagnosis using FDG-PET/CT for axillary lymph node metastasis in patients with breast cancer |
URI | https://link.springer.com/article/10.1186/s13550-021-00751-4 https://www.ncbi.nlm.nih.gov/pubmed/33492478 https://www.proquest.com/docview/2480551197 https://search.proquest.com/docview/2480753610 https://pubmed.ncbi.nlm.nih.gov/PMC7835273 https://doaj.org/article/d42eb453e3564212b20184bae31d35cb |
Volume | 11 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3da9swEBdNB6Uvo_v22gUN9ra5sfVl-THJknaDlFASCHsxsiW1gcQpSQrrw_73nWQ7a_bxspfDn_jQnXy_053uEPqQxFalYDpCJsA3YQWRoRLchqmiRBWp1dp3axhdicsp-zrjswPEm70wPmm_yOfn5WJ5Xs5vfW7l3bLoNHlinfGo71YrwOx2WqiVUPrIRa8LevOUymaDjBSdTQxGNQpdMoKzkOAzHaMj6sryMddd7ZE98mX7_4Y1_0yZ_C1u6s3R8AQ9rXEk7lb8PkMHpnyOjkZ1pPwF-jFem4Xv2LV-wL6GLF5Z3P0SAlh2ktVYV0l28w12ue83ePj5IhwPJp3-BAOQxeq760cELy8eQOC4XGmDl2arAE26d-YlrkuybrBby8W5y27f4sJp0folmg4Hk_5lWLdaCAsRJduQ5CyX1gitCIlSolJAWSw12jIAGCqKQX5C8iIvrDFMqVgkEc-ZNjzRibGK0lfosFyV5g3CkVHWChe-BMtX8FQqy6WK84Ib-K8aEqCPzYBnd1VFjcx7IlJklaQykFTmJZWxAPWcTHZPumrY_sJqfZPVOpFpRkzOODWUu327JAdUI1muDI01Ba4DdNZINKun6CYjTEbcR1ED9H53GyaXi5io0qzuq2fAnwOIGaDXlQLsOGkUKEDJnmrssbp_B_TZF_Cu9TdAnxol-sXWv4fi7X9_6BQdE6_9cUj4GTrcru_NO8BS27yNWiy6AJrMEqByCMdPeoOr8TWc9QlzVPTbfqUC6IhJoNe9b20_54BOSfcnQUcmpg |
link.rule.ids | 230,315,733,786,790,870,891,2115,2236,12083,12792,21416,24346,27955,27956,31752,31753,33406,33407,33777,33778,40934,41152,41153,41556,42003,42221,42222,42625,43343,43633,43838,51609,52128,52266,52267,53825,53827,74100,74390,74657 |
linkProvider | National Library of Medicine |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3db9MwELegk4AXxDeBAUbiDawmjp04T2gbLR2sVYU6aW-WE9uj0pZsbSexB_537py0U_l6jW3J8e_s-_nufEfIuzzxpgDVwUQGdxNRccVMJj0rTMpNVXhrQ7WG8SQbHYsvJ_KkM7gtu7DK9ZkYDmrbVGgj73OhYhmcXh8vLhlWjULvaldC4zbZwZSbqkd29geT6beNlQUrmBSpWr-WUVl_mYCGjRlGJqC6hAvUlkYKifv_xjb_DJr8zXMaFNLwAbnfMUm610L_kNxy9SNyZ9z5yh-Tn9OFOws1uxbXNGSRpY2ne4cM6DJia6ltw-zmS4rR76d0-Okzmw5m_YMZBSpLzQ-sSASDz64Bclo31tFztzLAJ3HMvKZdUtYlRWsuLTG-fUUrlKPFE3I8HMwORqwrtsCqLM5XjJeiVN5l1nAeF9wUwLNE4awXQDFMnACCmZJVWXnnhDFJlseyFNbJ3ObOmzR9Snp1U7vnhMbOeJ-hAxN0XyULZbxUJikr6eBkdTwi79cLri_anBo63EVUplt4NMCjAzxaRGQfMdn0xHzY4UOzONXd9tJWcFcKmbpU4stdXgKvUaI0Lk1sCrOOyO4aUd1t0qW-EamIvN00w_ZCn4mpXXPV9oEbHZDMiDxrBWAzkxQzO4pcRSTfEo2tqW631PPvIYU32tuAOEbkw1qIbqb176V48f-_eEPujmbjI310OPn6ktzjQcYTxuUu6a0WV-4V0KdV-brbI78AQRMYWA |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3db9QwDI9gSBMviO8VBgSJN4iuTZM2fUJj27EBm-7hJt1blDbJOGm049pJ7IH_HTttbzq-XptEcmM7_jl2bELe5Ik3BZgOJjLwTUTFFTOZ9KwwKTdV4a0N3RpOTrOjM_FpIRdD_lM7pFWOZ2I4qG1T4R35hAsVyxD0mvghLWJ2MH1_-Z1hBymMtA7tNG6TO2AlY2zjkC_y9X0L9jIpUjW-m1HZpE3A1sYMcxTQcIIrtWGbQgn_v-HOP9Mnf4uhBtM0vU_uDZiS7vVC8IDccvVDsn0yRM0fkZ-zlbsI3btW1zTUk6WNp3vHDIAzctlS2yfcLVuKefDndHrwkc0O55P9OQVQS80P7E0Eiy-ugfm0bqyj31xnAFnimmVNh_KsLcV7XVpipntHK5So1WNyNj2c7x-xoe0Cq7I47xgvRam8y6zhPC64KQBxicJZLwBsmDgBXmZKVmXlnRPGJFkey1JYJ3ObO2_S9AnZqpva7RAaO-N9hqFMsIKVLJTxUpmkrKSDM9bxiLwdN1xf9tU1dPBKVKZ79mhgjw7s0SIiH5An65lYGTt8aFbnelA0bQV3pZCpSyW-4eUlIBwlSuPSxKZAdUR2R47qQV1bfSNcEXm9HgZFw-iJqV1z1c8B3w7gZkSe9gKwpiTFGo8iVxHJN0Rjg9TNkXr5NRTzxps3gJAReTcK0Q1Z_96KZ___i1dkG5RDfzk-_fyc3OVBxBPG5S7Z6lZX7gXgqK58GRTkF4eSGxU |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Preliminary+study+of+AI-assisted+diagnosis+using+FDG-PET%2FCT+for+axillary+lymph+node+metastasis+in+patients+with+breast+cancer&rft.jtitle=EJNMMI+research&rft.au=Li%2C+Zongyao&rft.au=Kitajima%2C+Kazuhiro&rft.au=Hirata%2C+Kenji&rft.au=Togo%2C+Ren&rft.date=2021-01-25&rft.issn=2191-219X&rft.eissn=2191-219X&rft.volume=11&rft.issue=1&rft.spage=10&rft_id=info:doi/10.1186%2Fs13550-021-00751-4&rft_id=info%3Apmid%2F33492478&rft.externalDocID=33492478 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2191-219X&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2191-219X&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2191-219X&client=summon |