Quantification of the tumor microenvironment and prognostic analysis in colorectal cancer based on CAMuTILS
Keywords:
Colorectal cancer, Tumor microenvironment, Prognosis, Deep learning, Attention mechanismAbstract
The complex spatial organization of the tumor microenvironment (TME) plays a critical role in colorectal cancer progression, yet its quantitative characterization from histopathological images remains challenging and often relies on subjective pathological assessment. In this study, we develop a CAMuTILS-based TME scoring framework for quantitative analysis and prognostic evaluation in colorectal cancer. First, we propose CAMuTILS, a panoptic segmentation network for histopathological images. The model adopts a dual-branch U-shaped architecture to simultaneously segment tissue regions and cell nuclei at 1.0 MPP and 0.5 MPP resolutions. A cross-channel Transformer module is introduced for multi-scale feature alignment, together with an iterative attention-based (iAFF) cross-branch interaction mechanism and histology-informed spatial constraints that allow tissue regions to guide nuclear classification. Based on the segmentation results generated by CAMuTILS, TME features were quantified across five biological themes including epithelial architecture, stromal characteristics, tumor-infiltrating lymphocytes, necrosis, and spatial interactions. A total of 45 prognostic features were selected to construct a computational risk score termed CRS. Experimental results demonstrate that CAMuTILS achieves superior segmentation performance on the PanopTILs dataset compared with existing methods. Survival analysis on the TCGA colorectal cancer cohort shows that CRS is an independent predictor of progression-free survival and provides prognostic information complementary to conventional clinicopathological variables. External validation on an independent CPTAC cohort further supports the robustness and generalizability of the proposed framework across institutions. These findings highlight the potential of computational pathology to enable quantitative TME characterization and support precision prognostic assessment in colorectal cancer.
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