A Comprehensive AI-Based Approach in Classifying Breast Lesions: Focusing on Improving Pathologists' Accuracy and Efficiency.

BACKGROUND

Accurate classification of breast lesions is essential for effective clinical decision-making and patient management. In this study, we evaluated an artificial intelligence (AI) solution to classify whole slide images (WSIs) of breast lesions.

METHODS

We analyzed a cohort of 104 breast cases, including 20 invasive carcinomas, 4 microinvasive carcinomas, 15 ductal Carcinoma in situ (DCIS) cases, and 65 lobular neoplasia/benign cases. The AI's performance was compared with the ground truth established by breast pathologists.

RESULTS

For invasive carcinoma, it achieved an area under the curve (AUC) of 0.976, with sensitivity and specificity of 91.7% (84.4%, 95.4%) and 95% (88.0% 97.3%) respectively. For DCIS, the AUC was 0.976, with sensitivity and specificity of 93.3% and 96.6%. For lobular neoplasm, it achieved an AUC of 0.953, sensitivity of 94.1%, and specificity of 95.8%. The AI also performed well in detecting microcalcifications, with an AUC of 0.925 and sensitivity of 95%. Pathologists' diagnostic accuracy improved from 97.1% to 100% with AI support (303 vs. 312 accurate case reads per arm). Additionally, the AI use significantly enhanced the pathologists' efficiency, reducing their review time by an average of 16.5% across the 3 pathologists and led to a 33% reduction in immunohistochemistry usage.

CONCLUSION

This study highlights the potential of AI in breast lesion classification, demonstrating high sensitivity, specificity, and efficiency, and supports its integration into routine pathology practice.