A Closer Look at Weak Supervision’s Limitations in WSI Recurrence Score Prediction

Abstract

Histological examination and derived ancillary testing remain the gold standard for breast cancer diagnosis, prognosis assessment and treatment guidance. Currently, a commercial molecular signature test OncotypeDX®, based on RNA quantitation and providing a recurrence score (RS) ranging from 0 to 100, is routinely utilized for luminal breast cancers (the largest sub-type group of breast cancers) to predict the probabilities of response to chemotherapy and disease recurrence. We attempt to predict RS using digital pathology and Weakly Supervised (WS) attention-based models. In tissue samples, the malignant component is haphazardly admixed with the non-malignant component in variable proportions. This represents a challenge for WS attention-based models to identify high-valued diagnostic/prognostic areas within whole slide images (WSIs). To address this, we propose an interactive, supervised approach with a human in the middle by creating a user-friendly Graphical User Interface (GUI) that allows an expert pathologist to annotate heatmaps generated by any WS attention-based model. We aim to enhance the model’s learning capabilities and performance by incorporating the feedback from the GUI as expected scores in the successive training process. We train WS attention-based models like CLAM (Clustering-constrained Attention Multiple Instance Learning) and TransMIL (Transformer based Correlated Multiple Instance Learning) on our in-house dataset before and after the expert feedback. We observe an improvement in RS prediction after retraining both models with the pathologist’s annotation- a 5% rise in validation-test AUC and 4% in validation-test accuracy for CLAM and a 4.5% increase in validation-test AUC and 3% in validation-test accuracy for TransMIL. We analyze the generated heatmaps and observe how additional supervision from a domain expert enhances the learning capacity of the models. We notice an improvement in cosine similarity between the pathologist’s GUI-based attention scores and trained models’ attention maps after feedback - 5% and 10% increase for CLAM and TransMIL, respectively. Our adaptive, interactive system harmonizes attention scores with expert intuition and instills higher confidence in the system’s predictions. This study establishes a potent synergy between AI and expert collaboration, addressing the constraints of WS by enhancing the discrimination of diagnostic features and making an effort to generate predictions according to clinical diagnostic norms.