Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer

作者：Yunrui Ye, Xiaomei Wu, Huihui Wang, Huifen Ye, Ke Zhao, Su Yao, Zaiyi Liu, Yaxi Zhu, Qingling Zhang, Changhong Liang · 发表于：Annals of Medicine · 年份：2023 · DOI：10.1080/07853890.2023.2215541 · 被引用次数：12 · 研究领域：Inflammatory Biomarkers in Disease Prognosis、Cancer Immunotherapy and Biomarkers、Ferroptosis and cancer prognosis

Background In colorectal cancer (CRC), both tumor invasion and immunological analysis at the tumor invasive margin (IM) are significantly associated with patient prognosis, but have traditionally been reported independently. We propose a new scoring system, the TGP-I score, to assess the association and interactions between tumor growth pattern (TGP) and tumor infiltrating lymphocytes at the IM and to predict its prognostic validity for CRC patient stratification.Materials and Methods The types of TGP were assessed in hematoxylin and eosin-stained whole-slide images. The CD3+ T-cells density at the IM was automatically quantified on immunohistochemical-stained slides using a deep learning method. A discovery (N = 347) and a validation (N = 132) cohorts were used to evaluate the prognostic value of the TGP-I score for overall survival.Results The TGP-I score3 (trichotomy) was an independent prognostic factor, with higher TGP-I score3 associated with worse prognosis in the discovery (unadjusted hazard ratio [HR] for high vs. low 3.62, 95% confidence interval [CI] 2.22–5.90; p < 0.001) and validation cohort (unadjusted HR for high vs. low 5.79, 95% CI 1.84–18.20; p = 0.003). The relative contribution of each parameter to predicting survival was analyzed. The TGP-I score3 had similar importance compared to tumor-node-metastasis staging (31.2% vs. 32.9%) and was stronger than other clinical parameters.Conclusions This automated workflow and the proposed TGP-I score could further p...