Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer
Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current internationa...
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Published in | Modern pathology Vol. 36; no. 9; p. 100233 |
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Main Authors | , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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Elsevier Inc
01.09.2023
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Abstract | Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H&E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H&E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n = 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H&E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials. |
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AbstractList | Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H&E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H&E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n = 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H&E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials. Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H & E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H & E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n 1/4 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H & E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials. & COPY; 2023 THE AUTHORS. Published by Elsevier Inc. on behalf of the United States & Canadian Academy of Pathology. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/). |
ArticleNumber | 100233 |
Author | Sheahan, Kieran Bokhorst, John-Melle Zlobec, Inti Vieth, Michael Ciompi, Francesco Oguz Erdogan, Ayse Selcen Simmer, Femke Öztürk, Sonay Kus Lugli, Alessandro Nagtegaal, Iris D. Dawson, Heather Kirsch, Richard van der Laak, Jeroen |
Author_xml | – sequence: 1 givenname: John-Melle orcidid: 0000-0001-7231-8833 surname: Bokhorst fullname: Bokhorst, John-Melle email: john-melle.bokhorst@radboudumc.nl organization: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands – sequence: 2 givenname: Francesco surname: Ciompi fullname: Ciompi, Francesco organization: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands – sequence: 3 givenname: Sonay Kus surname: Öztürk fullname: Öztürk, Sonay Kus organization: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands – sequence: 4 givenname: Ayse Selcen surname: Oguz Erdogan fullname: Oguz Erdogan, Ayse Selcen organization: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands – sequence: 5 givenname: Michael surname: Vieth fullname: Vieth, Michael organization: Klinikum of Pathology, Bayreuth University, Bayreuth, Germany – sequence: 6 givenname: Heather surname: Dawson fullname: Dawson, Heather organization: Institute of Tissue Medicine and Pathology, University of Bern, Bern, Switzerland – sequence: 7 givenname: Richard surname: Kirsch fullname: Kirsch, Richard organization: University of Toronto, Mount Sinai Hospital, Toronto, Canada – sequence: 8 givenname: Femke surname: Simmer fullname: Simmer, Femke organization: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands – sequence: 9 givenname: Kieran surname: Sheahan fullname: Sheahan, Kieran organization: Department of Pathology, St Vincent's Hospital, Dublin, Ireland – sequence: 10 givenname: Alessandro surname: Lugli fullname: Lugli, Alessandro organization: Klinikum of Pathology, Bayreuth University, Bayreuth, Germany – sequence: 11 givenname: Inti surname: Zlobec fullname: Zlobec, Inti organization: Klinikum of Pathology, Bayreuth University, Bayreuth, Germany – sequence: 12 givenname: Jeroen surname: van der Laak fullname: van der Laak, Jeroen organization: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands – sequence: 13 givenname: Iris D. surname: Nagtegaal fullname: Nagtegaal, Iris D. organization: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands |
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Keywords | automated assessment colorectal cancer computational pathology prognosis artificial intelligence tumor budding |
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SubjectTerms | artificial intelligence automated assessment colorectal cancer computational pathology prognosis tumor budding |
Title | Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer |
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