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README

Histomic Prognostic Signature (HiPS): A population-level computational histologic signature for invasive breast cancer prognosis

Authors

Mohamed Amgad1, James M. Hodge2, Maha A.T. Elsebaie3, Clara Bodelon2, Samantha Puvanesarajah2, David A. Gutman4, Kalliopi P. Siziopikou1, Jeffery A. Goldstein1, Mia M. Gaudet5, Lauren R. Teras2, Lee A.D. Cooper1

1 Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL. 2 Department of Population Science, American Cancer Society, Atlanta, GA. 3 Department of Medicine, John H. Stroger, Jr. Hospital of Cook County, Chicago, IL. 4 Departments of Neurology and Psychiatry, Emory University School of Medicine, Atlanta, GA. 5 Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD.

* Co-senior authors.

Paper

Access here. Citation:

Amgad, M., Hodge, J.M., Elsebaie, M.A.T. et al. A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer.
Nature Medicine 30, 85–97 (2024). https://doi.org/10.1038/s41591-023-02643-7

Abstract

Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists grade the microscopic appearance of breast tissue using the Nottingham criteria, which is qualitative and does not account for non-cancerous elements within the tumor microenvironment (TME). We present the Histomic Prognostic Signature (HiPS), a comprehensive, interpretable scoring of the survival risk incurred by breast TME morphology. HiPS uses deep learning to accurately map cellular and tissue structures in order to measure epithelial, stromal, immune, and spatial interaction features. It was developed using a population-level cohort from the Cancer Prevention Study (CPS)-II and validated using data from three independent cohorts, including the PLCO trial, CPS-3, and The Cancer Genome Atlas. HiPS consistently outperformed pathologists in predicting survival outcomes, independent of TNM stage and pertinent variables. This was largely driven by stromal and immune features. In conclusion, HiPS is a robustly validated biomarker to support pathologists and improve patient prognosis.

Methodology overview

image

Core symbols most depended-on inside this repo

_keep_and_rename_columns_from_df
called by 8
hips/SlideFeatureExtractor.py
_get_nuclei_props_subset_for_superclasses
called by 7
hips/SlideFeatureExtractor.py
_get_df_mean_and_std
called by 4
hips/SlideFeatureExtractor.py
calculate_unnormalized
called by 3
hips/RipleysK.py
maybe_normalize
called by 3
hips/RipleysK.py
_get_coords_from_tilename
called by 3
hips/HistomicFeatWSIVisualizer.py
_fix_and_move_identifier_columns
called by 2
hips/SlideFeatureExtractor.py
maybe_extract_global_nuclei_features
called by 2
hips/SlideFeatureExtractor.py

Shape

Method 112
Class 6

Languages

Python100%

Modules by API surface

hips/SlideFeatureExtractor.py95 symbols
hips/RipleysK.py16 symbols
hips/HistomicFeatWSIVisualizer.py7 symbols

For agents

$ claude mcp add HiPS \
  -- python -m otcore.mcp_server <graph>

⬇ download graph artifact