Identification of prognostic spatial organization features in colorectal cancer microenvironment using deep learning on histopathology images

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  • Jia Ke
  • Zhaoliang Yu
  • Yuni Lai
  • Yufeng Chen


Original languageEnglish
Article number100008
Journal / PublicationMedicine in Omics
Online published11 Feb 2021
Publication statusPublished - Sept 2021



Background: The ecological diversity in the tumor microenvironment influences cancer progression and clinical outcomes of patients. However, the complexity of cellular and tissue components hamper quantitative dissection of the tumor microenvironment. In this study, we aimed to develop an efficient and robust artificial intelligence (AI)-empowered framework for the identification of prognostic spatial organization features based on histopathological images. Results: Using two public H&E image cohorts involving 107,180 hand-delineated image patches, we trained and validated a robust and efficient deep convolutional neural network for accurate tissue classification. With the classification result, we calculated whole-slide and infiltrating spatial organization features (SOFs) for different tissue types. Interestingly, the whole-slide SOFs recapitulated the characteristics of the four Consensus Molecular Subtypes (CMSs) of colorectal cancer (CRC). More specifically, we found that lymphocyte, tumor, mucus, and stroma tissues are significantly more abundant in CMS1, 2, 3, and 4, respectively. Using univariate and multivariate analyses, we identified infiltrating lymphocyte ratio (ILR) and infiltrating stroma ratio (ISR) are significantly associated with relapse-free survival. Based on two independent clinical cohorts, we further demonstrated the combinatorial prognostic value of ILR and ISR. Together, our results suggest that a high level of lymphocyte infiltration may surpass the effect of stromal infiltration. However, stromal infiltration will be essential for RFS in patients with low degrees of immune activity. Conclusions: We developed CRC-SPA for accurate profiling of spatial organization features using histology images, providing a cost-efficient tool for more quantitative analysis of tumor microenvironment and stratification of patients for more optimized clinical management.

Research Area(s)

  • Colorectal Cancer, Molecular Subtype, H&E images, Deep learning, Prognosis

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