Computational Analysis of Pathological Image Enables Interpretable Prediction for Microsatellite Instability

Computational Analysis of Pathological Image Enables Interpretable Prediction for Microsatellite Instability

BackgroundMicrosatellite instability (MSI) is associated with several tumor types and has become increasingly vital in guiding patient treatment decisions; however, reasonably distinguishing MSI from its counterpart is challenging in clinical practice.MethodsIn this study, interpretable pathological...

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Bibliographic Details
Main Authors: Jin Zhu, Wangwei Wu, Yuting Zhang, Shiyun Lin, Yukang Jiang, Ruixian Liu, Heping Zhang, Xueqin Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-07-01
Series:Frontiers in Oncology
Subjects:
cancer
microsatellite instability
interpretability
deep learning
random forest
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2022.825353/full

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https://www.frontiersin.org/articles/10.3389/fonc.2022.825353/full

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