An Explainable AI Protocol for Early Detection of Malignancy in BIRADS 0 Mammography Studies

Abstract

Background: The global shift towards value-based, preventive healthcare requires diagnostic strategies that are accurate, patient-centered, and resource-efficient. Breast cancer remains [1] the most prevalent cancer in women worldwide, making effective screening a public health priority. While mammography is a cornerstone of early detection, current limitations particularly the high rate of false positives in BI-RADS 0 cases (incomplete category, requiring additional imaging or comparison with prior studies), generate patient anxiety, unnecessary biopsies , and a significant economic burden [2, 3].   Approach: This study employed a quantitative, computational approach using mammography images and clinical data from the publicly available Cancer Imaging Archive (CBIS-DDSM) [4]. The research pipeline included data cleaning, AI model design, implementation of explainability algorithms, and discussion of results with a multidisciplinary team. Data cleaning focuses on selecting only patients with BI-RADS 0, a clinically challenging group characterized by indeterminate findings that frequently require further tests and cause uncertainty. The final dataset comprised 68 patients (54 benign and 14 malignant cases). Radiomic features were extracted and combined with clinical variables. For feature selection and classification, machine learning models including Random Forest, LightGBM, and XGBoost were applied. Model interpretability was prioritized through SHAP and LIME.     Results: The key findings indicate that out of 45 features, the most relevant were five clinical variables (breast density, mass shape, mass margins, subtlety, and calc type) alongside 40 radiomic features covering shape, first-order statistics, and texture matrices. Using LightGBM, Random Forest, and XGBoost, models achieved high performance with accuracy 88.9–91.6%, recall 85.3–96.7%, F1 90.1–91.0%, and AUC 96.9–98.0%. Among them, Random Forest delivered the best overall results (91.6% accuracy, 85.3% recall, 91.0% F1, 98.0% AUC). Interpretability with LIME and SHAP highlighted clinically relevant features mass margins, mass shape, and texture descriptors as key for distinguishing. High values of GrayLevelNonUniformity and irregular mass margins supported malignancy, while measures like SmallDependenceLowGrayLevelEmphasis and certain calcification patterns supported benignity.   Implications: This study highlights the potential of interpretable AI to enhance preventive breast cancer diagnostics. In a cohort of 68 BI-RADS 0 cases, the model accurately identified all 14 malignancies, highlighting its ability to reduce unnecessary procedures, save healthcare resources, and alleviate patient anxiety. By reliably stratifying indeterminate cases, it improves workflow efficiency, supports timely clinical decision-making, and ensures patient-centered care. Explainability enables clinicians to validate AI recommendations, fostering trust, collaboration, and informed multidisciplinary decision-making. Scaled deployment could advance equitable access, optimize resource allocation, and strengthen value-based, patient-focused healthcare systems.   References: 1.World Health Organization. Breast cancer. Geneva: WHO; 2023. Available from: https://www.who.int/news-room/fact-sheets/detail/breast-cancer 2.Xu Z et al. A bimodal nomogram as an adjunct tool to reduce unnecessary breast biopsy following discordant ultrasonic and mammographic BI-RADS assessment. Eur Radiol. 2024;34(4):2608-2618. 3.Li N et al. Value of inversion imaging to diagnosis in differentiating malignant from benign breast masses. BMC Med Imaging. 2023;23(1):206. Published 2023 Dec 8. 4.Sawyer-Lee R et al. Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) [dataset]. The Cancer Imaging Archive; 2016