Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning

Abstract Immune checkpoint inhibitor (ICI) therapy is widely used but effective only in a subset of gastric cancers. Epstein–Barr virus (EBV)-positive and microsatellite instability (MSI) / mismatch repair deficient (dMMR) tumors have been reported to be highly responsive to ICIs. However, detecting...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Munetoshi Hinata, Tetsuo Ushiku
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e099685c365a4f5fb19c30cab42e1569
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e099685c365a4f5fb19c30cab42e1569
record_format	dspace
spelling	oai:doaj.org-article:e099685c365a4f5fb19c30cab42e15692021-11-28T12:15:36ZDetecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning10.1038/s41598-021-02168-42045-2322https://doaj.org/article/e099685c365a4f5fb19c30cab42e15692021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02168-4https://doaj.org/toc/2045-2322Abstract Immune checkpoint inhibitor (ICI) therapy is widely used but effective only in a subset of gastric cancers. Epstein–Barr virus (EBV)-positive and microsatellite instability (MSI) / mismatch repair deficient (dMMR) tumors have been reported to be highly responsive to ICIs. However, detecting these subtypes requires costly techniques, such as immunohistochemistry and molecular testing. In the present study, we constructed a histology-based deep learning model that aimed to screen this immunotherapy-sensitive subgroup efficiently. We processed whole slide images of 408 cases of gastric adenocarcinoma, including 108 EBV, 58 MSI/dMMR, and 242 other subtypes. Many images generated by data augmentation of the learning set were used for training convolutional neural networks to establish an automatic detection platform for EBV and MSI/dMMR subtypes, and the test sets of images were used to verify the learning outcome. Our model detected the subgroup (EBV + MSI/dMMR tumors) with high accuracy in test cases with an area under the curve of 0.947 (0.901–0.992). This result was slightly better than when EBV and MSI/dMMR tumors were detected separately. In an external validation cohort including 244 gastric cancers from The Cancer Genome Atlas database, our model showed a favorable result for detecting the “EBV + MSI/dMMR” subgroup with an AUC of 0.870 (0.809–0.931). In addition, a visualization of the trained neural network highlighted intraepithelial lymphocytosis as the ground for prediction, suggesting that this feature is a discriminative characteristic shared by EBV and MSI/dMMR tumors. Histology-based deep learning models are expected to be used for detecting EBV and MSI/dMMR gastric cancers as economical and less time-consuming alternatives, which may help to effectively stratify patients who respond to ICIs.Munetoshi HinataTetsuo UshikuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Munetoshi Hinata Tetsuo Ushiku Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning
description	Abstract Immune checkpoint inhibitor (ICI) therapy is widely used but effective only in a subset of gastric cancers. Epstein–Barr virus (EBV)-positive and microsatellite instability (MSI) / mismatch repair deficient (dMMR) tumors have been reported to be highly responsive to ICIs. However, detecting these subtypes requires costly techniques, such as immunohistochemistry and molecular testing. In the present study, we constructed a histology-based deep learning model that aimed to screen this immunotherapy-sensitive subgroup efficiently. We processed whole slide images of 408 cases of gastric adenocarcinoma, including 108 EBV, 58 MSI/dMMR, and 242 other subtypes. Many images generated by data augmentation of the learning set were used for training convolutional neural networks to establish an automatic detection platform for EBV and MSI/dMMR subtypes, and the test sets of images were used to verify the learning outcome. Our model detected the subgroup (EBV + MSI/dMMR tumors) with high accuracy in test cases with an area under the curve of 0.947 (0.901–0.992). This result was slightly better than when EBV and MSI/dMMR tumors were detected separately. In an external validation cohort including 244 gastric cancers from The Cancer Genome Atlas database, our model showed a favorable result for detecting the “EBV + MSI/dMMR” subgroup with an AUC of 0.870 (0.809–0.931). In addition, a visualization of the trained neural network highlighted intraepithelial lymphocytosis as the ground for prediction, suggesting that this feature is a discriminative characteristic shared by EBV and MSI/dMMR tumors. Histology-based deep learning models are expected to be used for detecting EBV and MSI/dMMR gastric cancers as economical and less time-consuming alternatives, which may help to effectively stratify patients who respond to ICIs.
format	article
author	Munetoshi Hinata Tetsuo Ushiku
author_facet	Munetoshi Hinata Tetsuo Ushiku
author_sort	Munetoshi Hinata
title	Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning
title_short	Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning
title_full	Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning
title_fullStr	Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning
title_full_unstemmed	Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning
title_sort	detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e099685c365a4f5fb19c30cab42e1569
work_keys_str_mv	AT munetoshihinata detectingimmunotherapysensitivesubtypeingastriccancerusinghistologicimagebaseddeeplearning AT tetsuoushiku detectingimmunotherapysensitivesubtypeingastriccancerusinghistologicimagebaseddeeplearning
_version_	1718408108619857920

Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning

Ejemplares similares