![]() These datasets were combined to produce the PanopTILs dataset, available at /view/panoptils. The Breast Cancer Semantic Segmentation dataset is available at /PathologyDataScience/BCSS, and the NuCLS dataset is available at /view/nucls. TCGA clinical data and WSIs are publicly available at. We provide this to facilitate reproducibility and to act as a resource for the scientific community. Supplementary Table 27 contains our calculated histomic feature values, HiPS scores and subscores, and related data for the TCGA cohort. In conclusion, HiPS is a robustly validated biomarker to support pathologists and improve patient prognosis. ![]() This was largely driven by stromal and immune features. HiPS consistently outperformed pathologists in predicting survival outcomes, independent of tumor–node–metastasis stage and pertinent variables. ![]() It was developed using a population-level cohort from the Cancer Prevention Study-II and validated using data from three independent cohorts, including the Prostate, Lung, Colorectal, and Ovarian Cancer trial, Cancer Prevention Study-3, and The Cancer Genome Atlas. HiPS uses deep learning to accurately map cellular and tissue structures to measure epithelial, stromal, immune, and spatial interaction features. Here we present the Histomic Prognostic Signature (HiPS), a comprehensive, interpretable scoring of the survival risk incurred by breast tumor microenvironment morphology. ![]() Pathologists grade the microscopic appearance of breast tissue using the Nottingham criteria, which are qualitative and do not account for noncancerous elements within the tumor microenvironment. Breast cancer is a heterogeneous disease with variable survival outcomes. ![]()
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