Interpretable survival prediction for colorectal cancer using deep learning

Abstract 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)...

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Autores principales:	Ellery Wulczyn, David F. Steiner, Melissa Moran, Markus Plass, Robert Reihs, Fraser Tan, Isabelle Flament-Auvigne, Trissia Brown, Peter Regitnig, Po-Hsuan Cameron Chen, Narayan Hegde, Apaar Sadhwani, Robert MacDonald, Benny Ayalew, Greg S. Corrado, Lily H. Peng, Daniel Tse, Heimo Müller, Zhaoyang Xu, Yun Liu, Martin C. Stumpe, Kurt Zatloukal, Craig H. Mermel
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/da537cd907ad48cb854376055689af37
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spelling	oai:doaj.org-article:da537cd907ad48cb854376055689af372021-12-02T16:45:06ZInterpretable survival prediction for colorectal cancer using deep learning10.1038/s41746-021-00427-22398-6352https://doaj.org/article/da537cd907ad48cb854376055689af372021-04-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00427-2https://doaj.org/toc/2398-6352Abstract 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.Ellery WulczynDavid F. SteinerMelissa MoranMarkus PlassRobert ReihsFraser TanIsabelle Flament-AuvigneTrissia BrownPeter RegitnigPo-Hsuan Cameron ChenNarayan HegdeApaar SadhwaniRobert MacDonaldBenny AyalewGreg S. CorradoLily H. PengDaniel TseHeimo MüllerZhaoyang XuYun LiuMartin C. StumpeKurt ZatloukalCraig H. MermelNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Ellery Wulczyn David F. Steiner Melissa Moran Markus Plass Robert Reihs Fraser Tan Isabelle Flament-Auvigne Trissia Brown Peter Regitnig Po-Hsuan Cameron Chen Narayan Hegde Apaar Sadhwani Robert MacDonald Benny Ayalew Greg S. Corrado Lily H. Peng Daniel Tse Heimo Müller Zhaoyang Xu Yun Liu Martin C. Stumpe Kurt Zatloukal Craig H. Mermel Interpretable survival prediction for colorectal cancer using deep learning
description	Abstract 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.
format	article
author	Ellery Wulczyn David F. Steiner Melissa Moran Markus Plass Robert Reihs Fraser Tan Isabelle Flament-Auvigne Trissia Brown Peter Regitnig Po-Hsuan Cameron Chen Narayan Hegde Apaar Sadhwani Robert MacDonald Benny Ayalew Greg S. Corrado Lily H. Peng Daniel Tse Heimo Müller Zhaoyang Xu Yun Liu Martin C. Stumpe Kurt Zatloukal Craig H. Mermel
author_facet	Ellery Wulczyn David F. Steiner Melissa Moran Markus Plass Robert Reihs Fraser Tan Isabelle Flament-Auvigne Trissia Brown Peter Regitnig Po-Hsuan Cameron Chen Narayan Hegde Apaar Sadhwani Robert MacDonald Benny Ayalew Greg S. Corrado Lily H. Peng Daniel Tse Heimo Müller Zhaoyang Xu Yun Liu Martin C. Stumpe Kurt Zatloukal Craig H. Mermel
author_sort	Ellery Wulczyn
title	Interpretable survival prediction for colorectal cancer using deep learning
title_short	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_sort	interpretable survival prediction for colorectal cancer using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/da537cd907ad48cb854376055689af37
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Interpretable survival prediction for colorectal cancer using deep learning

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