PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification

TL;DR

PersonalizedUS is an interpretable machine learning system that provides personalized breast cancer risk assessments with guaranteed coverage, reducing unnecessary biopsies while maintaining high accuracy across diverse patient subgroups.

Contribution

The paper introduces PersonalizedUS, a novel conformal prediction-based system that offers local coverage guarantees and subgroup-specific risk estimates for breast lesion assessment.

Findings

Achieves over 0.9 sensitivity, specificity, and predictive values across thresholds.

Reduces biopsies by up to 65% in certain lesion groups.

Provides a curated dataset and benchmarks for future research.

Abstract

Correctly assessing the malignancy of breast lesions identified during ultrasound examinations is crucial for effective clinical decision-making. However, the current "golden standard" relies on manual BI-RADS scoring by clinicians, often leading to unnecessary biopsies and a significant mental health burden on patients and their families. In this paper, we introduce PersonalizedUS, an interpretable machine learning system that leverages recent advances in conformal prediction to provide precise and personalized risk estimates with local coverage guarantees and sensitivity, specificity, and predictive values above 0.9 across various threshold levels. In particular, we identify meaningful lesion subgroups where distribution-free, model-agnostic conditional coverage holds, with approximately 90% of our prediction sets containing only the ground truth in most lesion subgroups, thus explicitly characterizing for which patients the model is most suitably applied. Moreover, we make available a curated tabular dataset of 1936 biopsied breast lesions from a recent observational multicenter study and benchmark the performance of several state-of-the-art learning algorithms. We also report a successful case study of the deployed system in the same multicenter context. Concrete clinical benefits include up to a 65% reduction in requested biopsies among BI-RADS 4a and 4b lesions, with minimal to no missed cancer cases.