Invasion prediction with artificial intelligence in ductal carcinoma in situ (DCIS) patients: a proof-of-concept study
Adnan Gündoğdu, Ceyda Sönmez Wetherilt, Ayhan Alpar, Sangar Abdullah, Osman Cem Yilmaz, Levent Çelìk · BMC Surgery · 2025
BACKGROUND: Ductal carcinoma in situ (DCIS) is a heterogeneous precursor lesion with variable invasive potential. Current predictive parameters for invasion risk offer limited utility for personalized assessment. This study aims to evaluate artificial intelligence (AI)-assisted mammography analysis as a tool for predicting invasion risk in DCIS patients. METHODS: In this retrospective cohort study, 74 patients with pathologically proven DCIS by preoperative biopsy were analyzed using a deep learning-based AI system (Transpara version 1.7.4). The AI system classified patients into low-risk and high-risk groups, which were validated against postoperative histopathological findings. Statistical analysis included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy calculations. RESULTS: Invasion was detected in 19 (25.7%) patients, with 18 (94.7%) classified as high-risk by the AI system. The model demonstrated 94.7% sensitivity, 45.5% specificity, 37.5% PPV, and 96.2% NPV. In patients aged ≥ 50 years and those with lesions ≥ 3 cm, the NPV reached 100%. A significant relationship was found between necrosis and invasion (p = 0.004). CONCLUSIONS: The high NPV suggests AI-assisted mammography analysis could serve as an effective rule-out tool for invasion in DCIS patients, potentially identifying candidates for less aggressive surgical treatment. Further validation in larger, multi-center studies is necessary to confirm these findings.