Cancer Detection in Breast MRI Screening via Explainable AI Anomaly Detection

Background: Artificial intelligence (AI) models hold potential to increase the accuracy and efficiency of breast MRI screening; however, existing models have not been rigorously evaluated in populations with low cancer prevalence and lack interpretability, both of which are essential for clinical adoption.

Purpose: To develop an explainable AI model for cancer detection at breast MRI that is effective in both high- and low-cancer-prevalence settings.

Materials and Methods: This retrospective study included 9738 breast MRI examinations from a single institution (2005–2022), with external testing in a publicly available multicenter dataset (221 examinations). In total, 9567 consecutive examinations were used to develop an explainable fully convolutional data description (FCDD) anomaly detection model to detect malignancies on contrast-enhanced MRI scans. Performance was evaluated in three cohorts: grouped cross-validation (for both balanced [20.0% malignant] and imbalanced [1.85% malignant] detection tasks), an internal independent test set (171 examinations), and an external dataset. Explainability was assessed through pixel-wise comparisons with reference-standard malignancy annotations. Statistical significance was assessed using the Wilcoxon signed rank test.

Results: FCDD outperformed the benchmark binary cross-entropy (BCE) model in cross-validation for both balanced (mean area under the receiver operating characteristic curve [AUC] = 0.84 ± 0.01 [SD] vs 0.81 ± 0.01; P < .001) and imbalanced (mean AUC = 0.72 ± 0.03 vs 0.69± 0.03; P < .001) detection tasks. At a fixed 97% sensitivity in the imbalanced setting, mean specificity across folds was 13% for FCDD and 9% for BCE (P = .02). In the internal test set, FCDD outperformed BCE for balanced (mean AUC = 0.81 ± 0.02 vs 0.72 ± 0.02; P < .001) and imbalanced (mean AUC = 0.78 ± 0.05 vs 0.76 ± 0.01; P < .02) detection tasks. For model explainability, FCDD demonstrated better spatial agreement with reference-standard annotations than BCE (internal test set: mean pixelwise AUC = 0.92 ± 0.10 vs 0.81 ± 0.13; P < .001). External testing confirmed that FCDD performed well, and better than BCE, in the balanced detection task (AUC = 0.86 ± 0.01vs 0.79 ± 0.01; P < .001).

Conclusion: The developed explainable AI model for cancer detection at breast MRI accurately depicted tumor location and outperformed commonly used models in both high- and low-cancer-prevalence scenarios.