Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients

Abstract We hypothesized that a deep-learning algorithm using HE images might be capable of predicting the benefits of adjuvant chemotherapy in cancer patients. HE slides were retrospectively collected from 1343 de-identified breast cancer patients at the Samsung Medical Center and used to develop t...

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Autores principales:	Soo Youn Cho, Jeong Hoon Lee, Jai Min Ryu, Jeong Eon Lee, Eun Yoon Cho, Chang Ho Ahn, Kyunghyun Paeng, Inwan Yoo, Chan-Young Ock, Sang Yong Song
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2b0c21c7490746a88438ccd2e45960e7
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spelling	oai:doaj.org-article:2b0c21c7490746a88438ccd2e45960e72021-12-02T19:09:30ZDeep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients10.1038/s41598-021-96855-x2045-2322https://doaj.org/article/2b0c21c7490746a88438ccd2e45960e72021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96855-xhttps://doaj.org/toc/2045-2322Abstract We hypothesized that a deep-learning algorithm using HE images might be capable of predicting the benefits of adjuvant chemotherapy in cancer patients. HE slides were retrospectively collected from 1343 de-identified breast cancer patients at the Samsung Medical Center and used to develop the Lunit SCOPE algorithm. Lunit SCOPE was trained to predict the recurrence using the 21-gene assay (Oncotype DX) and histological parameters. The risk prediction model predicted the Oncotype DX score > 25 and the recurrence survival of the prognosis validation cohort and TCGA cohorts. The most important predictive variable was the mitotic cells in the cancer epithelium. Of the 363 patients who did not receive adjuvant therapy, 104 predicted high risk had a significantly lower survival rate. The top-300 genes highly correlated with the predicted risk were enriched for cell cycle, nuclear division, and cell division. From the Oncotype DX genes, the predicted risk was positively correlated with proliferation-associated genes and negatively correlated with prognostic genes from the estrogen category. An integrative analysis using Lunit SCOPE predicted the risk of cancer recurrence and the early-stage hormone receptor-positive breast cancer patients who would benefit from adjuvant chemotherapy.Soo Youn ChoJeong Hoon LeeJai Min RyuJeong Eon LeeEun Yoon ChoChang Ho AhnKyunghyun PaengInwan YooChan-Young OckSang Yong SongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Soo Youn Cho Jeong Hoon Lee Jai Min Ryu Jeong Eon Lee Eun Yoon Cho Chang Ho Ahn Kyunghyun Paeng Inwan Yoo Chan-Young Ock Sang Yong Song Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
description	Abstract We hypothesized that a deep-learning algorithm using HE images might be capable of predicting the benefits of adjuvant chemotherapy in cancer patients. HE slides were retrospectively collected from 1343 de-identified breast cancer patients at the Samsung Medical Center and used to develop the Lunit SCOPE algorithm. Lunit SCOPE was trained to predict the recurrence using the 21-gene assay (Oncotype DX) and histological parameters. The risk prediction model predicted the Oncotype DX score > 25 and the recurrence survival of the prognosis validation cohort and TCGA cohorts. The most important predictive variable was the mitotic cells in the cancer epithelium. Of the 363 patients who did not receive adjuvant therapy, 104 predicted high risk had a significantly lower survival rate. The top-300 genes highly correlated with the predicted risk were enriched for cell cycle, nuclear division, and cell division. From the Oncotype DX genes, the predicted risk was positively correlated with proliferation-associated genes and negatively correlated with prognostic genes from the estrogen category. An integrative analysis using Lunit SCOPE predicted the risk of cancer recurrence and the early-stage hormone receptor-positive breast cancer patients who would benefit from adjuvant chemotherapy.
format	article
author	Soo Youn Cho Jeong Hoon Lee Jai Min Ryu Jeong Eon Lee Eun Yoon Cho Chang Ho Ahn Kyunghyun Paeng Inwan Yoo Chan-Young Ock Sang Yong Song
author_facet	Soo Youn Cho Jeong Hoon Lee Jai Min Ryu Jeong Eon Lee Eun Yoon Cho Chang Ho Ahn Kyunghyun Paeng Inwan Yoo Chan-Young Ock Sang Yong Song
author_sort	Soo Youn Cho
title	Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
title_short	Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
title_full	Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
title_fullStr	Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
title_full_unstemmed	Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
title_sort	deep learning from he slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2b0c21c7490746a88438ccd2e45960e7
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Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients

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