Breast Cancer Segmentation and Scoring in H&E
About
Interfaces
This algorithm implements all of the following input-output combinations:
| Inputs | Outputs | |
|---|---|---|
| 1 |
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Challenge Performance
| Date | Challenge | Phase | Rank |
|---|---|---|---|
| May 2, 2022 | tiger | Segmentation and Detection (Experimental) | 287 |
Model Facts
Summary
Tumor infiltrating lymphocytes have been shown to have predictive value for survival and chemotherapy response prediction in breast cancer. We developed a fully automated deep-learning based algorithm to segment breast H&E tissue into the classes Tumor, Normal, Fat, Stroma, Necrosis and Lymphocytes and compute three lymphocyte based biomarkers including the (computational) tumor infiltrating lymphocytes score. Tumor infiltrating lymphocytes have been shown to have predictive value for survival and chemotherapy response prediction in breast cancer. We developed a fully automated deep-learning based algorithm to segment breast H&E tissue into the classes Tumor, Normal, Fat, Stroma, Necrosis and Lymphocytes and compute three lymphocyte based biomarkers including the (computational) tumor infiltrating lymphocytes score.
Citation: Aswolinskiy, Witali, et al. "Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies." medRxiv (2022)
Mechanism
The algorithm has three steps:
- Preprocessing: Tissue is separated from background using a convolutional neural network
- Segmentation: The tissue is segmented into the classes Tumor, Normal, Fat, Stroma, Necrosis and Lymphocytes using a U-Net.
- From the segmentation, three biomarkers are computed: the computational tumor infiltrating lymphocytes score (TILs), inflamed tumor ratio (ITR) measuring the proportion of tumor within 80 microns of lymphocytes and the lymphocyte to tumor ratio (LTR).
Validation and Performance
Uses and Directions
This algorithm was developed for research purposes only. The terms of usage for Grand Challenge apply to the uploaded slides.
Warnings
Common Error Messages
Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3