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Interpretable survival prediction for colorectal cancer using deep learning

Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specific survival for stage II and III colorectal cancer using 3652 cases (27,300 slides). When ev...

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Autores principales: Wulczyn, Ellery, Steiner, David F., Moran, Melissa, Plass, Markus, Reihs, Robert, Tan, Fraser, Flament-Auvigne, Isabelle, Brown, Trissia, Regitnig, Peter, Chen, Po-Hsuan Cameron, Hegde, Narayan, Sadhwani, Apaar, MacDonald, Robert, Ayalew, Benny, Corrado, Greg S., Peng, Lily H., Tse, Daniel, Müller, Heimo, Xu, Zhaoyang, Liu, Yun, Stumpe, Martin C., Zatloukal, Kurt, Mermel, Craig H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055695/
https://www.ncbi.nlm.nih.gov/pubmed/33875798
http://dx.doi.org/10.1038/s41746-021-00427-2
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author Wulczyn, Ellery
Steiner, David F.
Moran, Melissa
Plass, Markus
Reihs, Robert
Tan, Fraser
Flament-Auvigne, Isabelle
Brown, Trissia
Regitnig, Peter
Chen, Po-Hsuan Cameron
Hegde, Narayan
Sadhwani, Apaar
MacDonald, Robert
Ayalew, Benny
Corrado, Greg S.
Peng, Lily H.
Tse, Daniel
Müller, Heimo
Xu, Zhaoyang
Liu, Yun
Stumpe, Martin C.
Zatloukal, Kurt
Mermel, Craig H.
author_facet Wulczyn, Ellery
Steiner, David F.
Moran, Melissa
Plass, Markus
Reihs, Robert
Tan, Fraser
Flament-Auvigne, Isabelle
Brown, Trissia
Regitnig, Peter
Chen, Po-Hsuan Cameron
Hegde, Narayan
Sadhwani, Apaar
MacDonald, Robert
Ayalew, Benny
Corrado, Greg S.
Peng, Lily H.
Tse, Daniel
Müller, Heimo
Xu, Zhaoyang
Liu, Yun
Stumpe, Martin C.
Zatloukal, Kurt
Mermel, Craig H.
author_sort Wulczyn, Ellery
collection PubMed
description Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specific survival for stage II and III colorectal cancer using 3652 cases (27,300 slides). When evaluated on two validation datasets containing 1239 cases (9340 slides) and 738 cases (7140 slides), respectively, the DLS achieved a 5-year disease-specific survival AUC of 0.70 (95% CI: 0.66–0.73) and 0.69 (95% CI: 0.64–0.72), and added significant predictive value to a set of nine clinicopathologic features. To interpret the DLS, we explored the ability of different human-interpretable features to explain the variance in DLS scores. We observed that clinicopathologic features such as T-category, N-category, and grade explained a small fraction of the variance in DLS scores (R(2) = 18% in both validation sets). Next, we generated human-interpretable histologic features by clustering embeddings from a deep-learning-based image-similarity model and showed that they explained the majority of the variance (R(2) of 73–80%). Furthermore, the clustering-derived feature most strongly associated with high DLS scores was also highly prognostic in isolation. With a distinct visual appearance (poorly differentiated tumor cell clusters adjacent to adipose tissue), this feature was identified by annotators with 87.0–95.5% accuracy. Our approach can be used to explain predictions from a prognostic deep learning model and uncover potentially-novel prognostic features that can be reliably identified by people for future validation studies.
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spelling pubmed-80556952021-05-05 Interpretable survival prediction for colorectal cancer using deep learning Wulczyn, Ellery Steiner, David F. Moran, Melissa Plass, Markus Reihs, Robert Tan, Fraser Flament-Auvigne, Isabelle Brown, Trissia Regitnig, Peter Chen, Po-Hsuan Cameron Hegde, Narayan Sadhwani, Apaar MacDonald, Robert Ayalew, Benny Corrado, Greg S. Peng, Lily H. Tse, Daniel Müller, Heimo Xu, Zhaoyang Liu, Yun Stumpe, Martin C. Zatloukal, Kurt Mermel, Craig H. NPJ Digit Med Article Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specific survival for stage II and III colorectal cancer using 3652 cases (27,300 slides). When evaluated on two validation datasets containing 1239 cases (9340 slides) and 738 cases (7140 slides), respectively, the DLS achieved a 5-year disease-specific survival AUC of 0.70 (95% CI: 0.66–0.73) and 0.69 (95% CI: 0.64–0.72), and added significant predictive value to a set of nine clinicopathologic features. To interpret the DLS, we explored the ability of different human-interpretable features to explain the variance in DLS scores. We observed that clinicopathologic features such as T-category, N-category, and grade explained a small fraction of the variance in DLS scores (R(2) = 18% in both validation sets). Next, we generated human-interpretable histologic features by clustering embeddings from a deep-learning-based image-similarity model and showed that they explained the majority of the variance (R(2) of 73–80%). Furthermore, the clustering-derived feature most strongly associated with high DLS scores was also highly prognostic in isolation. With a distinct visual appearance (poorly differentiated tumor cell clusters adjacent to adipose tissue), this feature was identified by annotators with 87.0–95.5% accuracy. Our approach can be used to explain predictions from a prognostic deep learning model and uncover potentially-novel prognostic features that can be reliably identified by people for future validation studies. Nature Publishing Group UK 2021-04-19 /pmc/articles/PMC8055695/ /pubmed/33875798 http://dx.doi.org/10.1038/s41746-021-00427-2 Text en © The Author(s) 2021, corrected publication 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wulczyn, Ellery
Steiner, David F.
Moran, Melissa
Plass, Markus
Reihs, Robert
Tan, Fraser
Flament-Auvigne, Isabelle
Brown, Trissia
Regitnig, Peter
Chen, Po-Hsuan Cameron
Hegde, Narayan
Sadhwani, Apaar
MacDonald, Robert
Ayalew, Benny
Corrado, Greg S.
Peng, Lily H.
Tse, Daniel
Müller, Heimo
Xu, Zhaoyang
Liu, Yun
Stumpe, Martin C.
Zatloukal, Kurt
Mermel, Craig H.
Interpretable survival prediction for colorectal cancer using deep learning
title Interpretable survival prediction for colorectal cancer using deep learning
title_full Interpretable survival prediction for colorectal cancer using deep learning
title_fullStr Interpretable survival prediction for colorectal cancer using deep learning
title_full_unstemmed Interpretable survival prediction for colorectal cancer using deep learning
title_short Interpretable survival prediction for colorectal cancer using deep learning
title_sort interpretable survival prediction for colorectal cancer using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055695/
https://www.ncbi.nlm.nih.gov/pubmed/33875798
http://dx.doi.org/10.1038/s41746-021-00427-2
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