Interpretable HER2 scoring by evaluating clinical Guidelines

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Interpretable HER2 scoring by evaluating clinical Guidelines

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[NOVEMBER 18TH, 2022]

Authors: 1. Manh Dan Pham, 2. Cyprien Tilmant,  3. Stéphanie Petit, 4. Isabelle Salmon, 1. Saima Ben Hadj, 1. Rutger H.J. Fick.

1. Tribun Health, 2 Rue du Capitaine Scott, 75015 Paris, France,
2. GHICL, Lille, France,
3. Xpath Nord, Leulinghem, France,
4. Hôpital Erasme, Route de Lennik 808 1070, Brussels, Belgium

Abstract. The evaluation of the Human Epidermal growth factor Receptor-2 (HER2) expression is an important prognostic biomarker for breast cancer treatment selection. However, HER2 scoring has notoriously high interobserver variability due to stain variations between centers and the need to estimate visually the staining intensity in specific percentages of tumor area. In this paper, focusing on the interpretability of HER2 scoring by a pathologist, we propose a semi-automatic, two-stage deep learning approach that directly evaluates the clinical HER2 guidelines defined by the American Society of Clinical Oncology/ College of American Pathologists (ASCO/CAP).

In the first stage, we segment the invasive tumor over the user-indicated Region of Interest (ROI). Then, in the second stage, we classify the tumor tissue into four HER2 classes. For the classification stage, we use weakly supervised, constrained optimization to find a model that classifies cancerous patches such that the tumor surface percentage meets the guidelines specification of each HER2 class. We end the second stage by freezing the model and refining its output logits in a supervised way to all slide labels in the training set.

To ensure the quality of our dataset’s labels, we conducted a multi-pathologist HER2 scoring consensus. For the assessment of doubtful cases where no consensus was found, our model can help by interpreting its HER2 class percentages output. We achieve a performance of 0.78 in F1-score on the test set while keeping our model interpretable for the pathologist, hopefully contributing to interpretable AI models in digital pathology.

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