Prediction of Breast Cancer Recurrence Risk Using a Multi-Model Approach
  Integrating Whole Slide Imaging and Clinicopathologic Features
Manu Goyal Jonathan D. Marotti Adrienne A. Workman Elaine P. Kuhn Graham M. Tooker Seth K. Ramin Mary D. Chamberlin Roberta M. diFlorio-Alexander Saeed Hassanpour
Breast cancer is the most common malignancy affecting women worldwide and is
notable for its morphologic and biologic diversity, with varying risks of
recurrence following treatment. The Oncotype DX Breast Recurrence Score test is
an important predictive and prognostic genomic assay for estrogen
receptor-positive breast cancer that guides therapeutic strategies; however,
such tests can be expensive, delay care, and are not widely available. The aim
of this study was to develop a multi-model approach integrating the analysis of
whole slide images and clinicopathologic data to predict their associated
breast cancer recurrence risks and categorize these patients into two risk
groups according to the predicted score: low and high risk. The proposed novel
methodology uses convolutional neural networks for feature extraction and
vision transformers for contextual aggregation, complemented by a logistic
regression model that analyzes clinicopathologic data for classification into
two risk categories. This method was trained and tested on 993 hematoxylin and
eosin-stained whole-slide images of breast cancers with corresponding
clinicopathological features that had prior Oncotype DX testing. The model's
performance was evaluated using an internal test set of 198 patients from
Dartmouth Health and an external test set of 418 patients from the University
of Chicago. The multi-model approach achieved an AUC of 0.92 (95 percent CI:
0.88-0.96) on the internal set and an AUC of 0.85 (95 percent CI: 0.79-0.90) on
the external cohort. These results suggest that with further validation, the
proposed methodology could provide an alternative to assist clinicians in
personalizing treatment for breast cancer patients and potentially improving
their outcomes.