A deep learning quantified stroma-immune score to predict survival of patients with stage II-III colorectal cancer

作者：Xu, Zeyan; Li, Yong; Wang, Yingyi; Zhang, Shenyan; Huang, Yanqi; Yao, Su; Han, Chu; Pan, Xipeng; Shi, Zhenwei; Mao, Yun; Xu, Yao; Huang, Xiaomei; Lin, Huan; Chen, Xin; Liang, Changhong; Li, Zhenhui^*; Zhao, Ke^*; Zhang, Qingling^*; Liu, Zaiyi^*

来源：Cancer Cell International, 2021, 21(1): 585.

DOI：10.1186/s12935-021-02297-w

• Deep learning
• Whole-slide images
• Tumor-infiltrating lymphocytes
• Colorectal cancer

Background Profound heterogeneity in prognosis has been observed in colorectal cancer (CRC) patients with intermediate levels of disease (stage II-III), advocating the identification of valuable biomarkers that could improve the prognostic stratification. This study aims to develop a deep learning-based pipeline for fully automatic quantification of immune infiltration within the stroma region on immunohistochemical (IHC) whole-slide images (WSIs) and further analyze its prognostic value in CRC. Methods Patients from two independent cohorts were divided into three groups: the development group (N = 200), the internal (N = 134), and the external validation group (N = 90). We trained a convolutional neural network for tissue classification of CD3 and CD8 stained WSIs. A scoring system, named stroma-immune score, was established by quantifying the density of CD3(+) and CD8(+) T-cells infiltration in the stroma region. Results Patients with higher stroma-immune scores had much longer survival. In the development group, 5-year survival rates of the low and high scores were 55.7% and 80.8% (hazard ratio [HR] for high vs. low 0.39, 95% confidence interval [CI] 0.24-0.63, P < 0.001). These results were confirmed in the internal and external validation groups with 5-year survival rates of low and high scores were 57.1% and 78.8%, 63.9% and 88.9%, respectively (internal: HR for high vs. low 0.49, 95% CI 0.28-0.88, P = 0.017; external: HR for high vs. low 0.35, 95% CI 0.15-0.83, P = 0.018). The combination of stroma-immune score and tumor-node-metastasis (TNM) stage showed better discrimination ability for survival prediction than using the TNM stage alone. Conclusions We proposed a stroma-immune score via a deep learning-based pipeline to quantify CD3(+) and CD8(+) T-cells densities within the stroma region on WSIs of CRC and further predict survival.

• 单位
  广东省人民医院; 中山大学; 重庆大学; 6; 1; 南方医科大学