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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.    Supplementary Information  The online version contains supplementary material available at 10.1186/s12935-021-02297-w.

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