Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics

作者：Yuming Jiang, Kangneng Zhou, Zepang Sun, Hongyu Wang, Jingjing Xie, Taojun Zhang, Shengtian Sang, Md Tauhidul Islam, Jen‐Yeu Wang, Chuanli Chen, Qingyu Yuan, Sujuan Xi, Tuanjie Li, Yikai Xu, Wenjun Xiong, Wei Wang, Guoxin Li, Ruijiang Li · 发表于：Cell Reports Medicine · 年份：2023 · DOI：10.1016/j.xcrm.2023.101146 · 被引用次数：112 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Gastric Cancer Management and Outcomes、Colorectal and Anal Carcinomas

The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accurately predicted the TME status and is an independent prognostic factor beyond clinicopathologic variables. The model further predicts the benefit from adjuvant chemotherapy for patients with localized disease. In patients treated with checkpoint blockade immunotherapy, the model predicts clinical response and further improves predictive accuracy when combined with existing biomarkers. Our approach enables noninvasive assessment of the TME, which opens the door for longitudinal monitoring and tracking response to cancer therapy. Given the routine use of radiologic imaging in oncology, our approach can be extended to many other solid tumor types.