학술논문
Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data
Document Type
article
Author
Melissa Zhao; Mai Chan Lau; Koichiro Haruki; Juha P. Väyrynen; Carino Gurjao; Sara A. Väyrynen; Andressa Dias Costa; Jennifer Borowsky; Kenji Fujiyoshi; Kota Arima; Tsuyoshi Hamada; Jochen K. Lennerz; Charles S. Fuchs; Reiko Nishihara; Andrew T. Chan; Kimmie Ng; Xuehong Zhang; Jeffrey A. Meyerhardt; Mingyang Song; Molin Wang; Marios Giannakis; Jonathan A. Nowak; Kun-Hsing Yu; Tomotaka Ugai; Shuji Ogino
Source
npj Precision Oncology, Vol 7, Iss 1, Pp 1-13 (2023)
Subject
Language
English
ISSN
2397-768X
Abstract
Abstract Routine tumor-node-metastasis (TNM) staging of colorectal cancer is imperfect in predicting survival due to tumor pathobiological heterogeneity and imprecise assessment of tumor spread. We leveraged Bayesian additive regression trees (BART), a statistical learning technique, to comprehensively analyze patient-specific tumor characteristics for the improvement of prognostic prediction. Of 75 clinicopathologic, immune, microbial, and genomic variables in 815 stage II–III patients within two U.S.-wide prospective cohort studies, the BART risk model identified seven stable survival predictors. Risk stratifications (low risk, intermediate risk, and high risk) based on model-predicted survival were statistically significant (hazard ratios 0.19–0.45, vs. higher risk; P