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Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer

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Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer

Journal of Gastroenterology

Fecha de publicación:

Autores: Yoshifumi Shimada, Shujiro Okuda, Yu Watanabe, Yosuke Tajima, Masayuki Nagahashi, Hiroshi Ichikawa, Masato Nakano, Jun Sakata, Yasumasa Takii, Takashi Kawasaki, Kei-ichi Homma, Tomohiro Kamori, Eiji Oki, Yiwei Ling, Shiho Takeuchi & Toshifumi Wakai 

DOI: https://doi.org/10.1007/s00535-021-01789-w

Background: Tumor mutational burden-high (TMB-H), which is detected with gene panel testing, is a promising biomarker for immune checkpoint inhibitors (ICIs) in colorectal cancer (CRC). However, in clinical practice, not every patient is tested for TMB-H using gene panel testing. We aimed to identify the histopathological characteristics of TMB-H CRC for efficient selection of patients who should undergo gene panel testing. Moreover, we attempted to develop a convolutional neural network (CNN)-based algorithm to predict TMB-H CRC directly from hematoxylin and eosin (H&E) slides.

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