ArticleKorean journal of radiology2025
Performance of Digital Mammography-Based Artificial Intelligence Computer-Aided Diagnosis on Synthetic Mammography From Digital Breast Tomosynthesis.
Article in Korean journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- ESR Essentials: artificial intelligence in breast imaging-practice recommendations by the European Society of Breast Imaging.European radiology · 2026Guideline
- Artificial intelligence in the radiologic assessment of ductal carcinomaFrontiers in oncology · 2026Pooled it
- Tomosynthesis and Synthesized-2D Breast AI Outputs in a Biopsy-Referred Cohort: A Diagnostic Study of Agreement and Incremental Decision-Support Value.Journal of imaging informatics in medicine · 2026Article
- Radiology Research and Publishing Across 2025-2026: Perspectives FromKorean journal of radiology · 2026Article
- Clinical diagnostic accuracy and non-inferiority of digital breast tomosynthesis by synthetic 2D mammography compared with full-field digital mammography for the diagnosis of breast cancer: a single-center retrospective study.Quantitative imaging in medicine and surgery · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectiveTo test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for full-field digital mammography (FFDM) when applied to synthetic mammography (SM). MATERIALS AND
methodsWe analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics. The results of SM and FFDM were compared.
resultsAI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%,
conclusionAI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM. Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.
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