Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images

Abstract Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However, as many as 70% of patients still experience tumor...

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Autores principales:	Rikiya Yamashita, Jin Long, Atif Saleem, Daniel L. Rubin, Jeanne Shen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/28c1b7e83175401e8131ebcd53e2459a
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spelling	oai:doaj.org-article:28c1b7e83175401e8131ebcd53e2459a2021-12-02T15:23:29ZDeep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images10.1038/s41598-021-81506-y2045-2322https://doaj.org/article/28c1b7e83175401e8131ebcd53e2459a2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81506-yhttps://doaj.org/toc/2045-2322Abstract Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However, as many as 70% of patients still experience tumor recurrence at 5 years post-surgery. We developed and validated a deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary resection, directly from hematoxylin and eosin-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections. Our model achieved concordance indices of 0.724 and 0.683 on the internal and external test cohorts, respectively, exceeding the performance of the standard Tumor-Node-Metastasis classification system. The model’s risk score stratified patients into low- and high-risk subgroups with statistically significant differences in their survival distributions, and was an independent risk factor for post-surgical recurrence in both test cohorts. Our results suggest that deep learning-based models can provide recurrence risk scores which may augment current patient stratification methods and help refine the clinical management of patients undergoing primary surgical resection for HCC.Rikiya YamashitaJin LongAtif SaleemDaniel L. RubinJeanne ShenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rikiya Yamashita Jin Long Atif Saleem Daniel L. Rubin Jeanne Shen Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images
description	Abstract Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However, as many as 70% of patients still experience tumor recurrence at 5 years post-surgery. We developed and validated a deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary resection, directly from hematoxylin and eosin-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections. Our model achieved concordance indices of 0.724 and 0.683 on the internal and external test cohorts, respectively, exceeding the performance of the standard Tumor-Node-Metastasis classification system. The model’s risk score stratified patients into low- and high-risk subgroups with statistically significant differences in their survival distributions, and was an independent risk factor for post-surgical recurrence in both test cohorts. Our results suggest that deep learning-based models can provide recurrence risk scores which may augment current patient stratification methods and help refine the clinical management of patients undergoing primary surgical resection for HCC.
format	article
author	Rikiya Yamashita Jin Long Atif Saleem Daniel L. Rubin Jeanne Shen
author_facet	Rikiya Yamashita Jin Long Atif Saleem Daniel L. Rubin Jeanne Shen
author_sort	Rikiya Yamashita
title	Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images
title_short	Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images
title_full	Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images
title_fullStr	Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images
title_full_unstemmed	Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images
title_sort	deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/28c1b7e83175401e8131ebcd53e2459a
work_keys_str_mv	AT rikiyayamashita deeplearningpredictspostsurgicalrecurrenceofhepatocellularcarcinomafromdigitalhistopathologicimages AT jinlong deeplearningpredictspostsurgicalrecurrenceofhepatocellularcarcinomafromdigitalhistopathologicimages AT atifsaleem deeplearningpredictspostsurgicalrecurrenceofhepatocellularcarcinomafromdigitalhistopathologicimages AT daniellrubin deeplearningpredictspostsurgicalrecurrenceofhepatocellularcarcinomafromdigitalhistopathologicimages AT jeanneshen deeplearningpredictspostsurgicalrecurrenceofhepatocellularcarcinomafromdigitalhistopathologicimages
_version_	1718387251936755712

Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images

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