Invasion prediction with artificial intelligence in ductal carcinoma in situ (DCIS) patients: a proof-of-concept study.
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.
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.
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).
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.
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.
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).
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.
Authors
Gundogdu Gundogdu, Wetherilt Wetherilt, Alpar Alpar, Abdullah Abdullah, Yilmaz Yilmaz, Celik Celik
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