TY - JOUR
T1 - Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer
AU - Shimada, Yoshifumi
AU - Okuda, Shujiro
AU - Watanabe, Yu
AU - Tajima, Yosuke
AU - Nagahashi, Masayuki
AU - Ichikawa, Hiroshi
AU - Nakano, Masato
AU - Sakata, Jun
AU - Takii, Yasumasa
AU - Kawasaki, Takashi
AU - Homma, Kei ichi
AU - Kamori, Tomohiro
AU - Oki, Eiji
AU - Ling, Yiwei
AU - Takeuchi, Shiho
AU - Wakai, Toshifumi
N1 - Publisher Copyright:
© 2021, Japanese Society of Gastroenterology.
PY - 2021/6
Y1 - 2021/6
N2 - 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. Methods: We used two CRC cohorts tested for TMB-H, and whole-slide H&E digital images were obtained from the cohorts. The Japanese CRC (JP-CRC) cohort (N = 201) was evaluated to detect the histopathological characteristics of TMB-H using H&E slides. The JP-CRC cohort and The Cancer Genome Atlas (TCGA) CRC cohort (N = 77) were used to develop a CNN-based TMB-H prediction model from the H&E digital images. Results: Tumor-infiltrating lymphocytes (TILs) were significantly associated with TMB-H CRC (P < 0.001). The area under the curve (AUC) for predicting TMB-H CRC was 0.910. We developed a CNN-based TMB-H prediction model. Validation tests were conducted 10 times using randomly selected slides, and the average AUC for predicting TMB-H slides was 0.934. Conclusions: TILs, a histopathological characteristic detected with H&E slides, are associated with TMB-H CRC. Our CNN-based model has the potential to predict TMB-H CRC directly from H&E slides, thereby reducing the burden on pathologists. These approaches will provide clinicians with important information about the applications of ICIs at low cost.
AB - 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. Methods: We used two CRC cohorts tested for TMB-H, and whole-slide H&E digital images were obtained from the cohorts. The Japanese CRC (JP-CRC) cohort (N = 201) was evaluated to detect the histopathological characteristics of TMB-H using H&E slides. The JP-CRC cohort and The Cancer Genome Atlas (TCGA) CRC cohort (N = 77) were used to develop a CNN-based TMB-H prediction model from the H&E digital images. Results: Tumor-infiltrating lymphocytes (TILs) were significantly associated with TMB-H CRC (P < 0.001). The area under the curve (AUC) for predicting TMB-H CRC was 0.910. We developed a CNN-based TMB-H prediction model. Validation tests were conducted 10 times using randomly selected slides, and the average AUC for predicting TMB-H slides was 0.934. Conclusions: TILs, a histopathological characteristic detected with H&E slides, are associated with TMB-H CRC. Our CNN-based model has the potential to predict TMB-H CRC directly from H&E slides, thereby reducing the burden on pathologists. These approaches will provide clinicians with important information about the applications of ICIs at low cost.
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U2 - 10.1007/s00535-021-01789-w
DO - 10.1007/s00535-021-01789-w
M3 - Article
C2 - 33909150
AN - SCOPUS:85105103672
SN - 0944-1174
VL - 56
SP - 547
EP - 559
JO - Journal of Gastroenterology
JF - Journal of Gastroenterology
IS - 6
ER -