Subelack Jonas, Morant Rudolf, Blum Marcel, Gräwingholt Axel, Vogel Justus, Geissler Alexander, Ehlig David
Chair of Health Economics, Policy and Management, School of Medicine, University of St.Gallen, St.Gallen, Switzerland.
Cancer League of Eastern Switzerland, St.Gallen, Switzerland.
Eur Radiol. 2025 Aug 4. doi: 10.1007/s00330-025-11833-5.
To determine whether an AI system can identify breast cancer risk in interval breast cancer (IBC) screening mammograms.
IBC screening mammograms from a Swiss screening program were retrospectively analyzed by radiologists/an AI system. Radiologists determined whether the IBC mammogram showed human visible signs of breast cancer (potentially missed IBCs) or not (IBCs without retrospective abnormalities). The AI system provided a case score and a prognostic risk category per mammogram.
119 IBC cases (mean age 57.3 (5.4)) were available with complete retrospective evaluations by radiologists/the AI system. 82 (68.9%) were classified as IBCs without retrospective abnormalities and 37 (31.1%) as potentially missed IBCs. 46.2% of all IBCs received a case score ≥ 25, 25.2% ≥ 50, and 13.4% ≥ 75. Of the 25.2% of the IBCs ≥ 50 (vs. 13.4% of a no breast cancer population), 45.2% had not been discussed during a consensus conference, reflecting 11.4% of all IBC cases. The potentially missed IBCs received significantly higher case scores and risk classifications than IBCs without retrospective abnormalities (case score mean: 54.1 vs. 23.1; high risk: 48.7% vs. 14.7%; p < 0.05). 13.4% of the IBCs without retrospective abnormalities received a case score ≥ 50, of which 62.5% had not been discussed during a consensus conference.
An AI system can identify IBC screening mammograms with a higher risk for breast cancer, particularly in potentially missed IBCs but also in some IBCs without retrospective abnormalities where radiologists did not see anything, indicating its ability to improve mammography screening quality.
Question AI presents a promising opportunity to enhance breast cancer screening in general, but evidence is missing regarding its ability to reduce interval breast cancers. Findings The AI system detected a high risk of breast cancer in most interval breast cancer screening mammograms where radiologists retrospectively detected abnormalities. Clinical relevance Utilization of an AI system in mammography screening programs can identify breast cancer risk in many interval breast cancer screening mammograms and thus potentially reduce the number of interval breast cancers.
确定人工智能（AI）系统能否在间期乳腺癌（IBC）筛查乳腺钼靶片中识别乳腺癌风险。
对来自瑞士一项筛查项目的IBC筛查乳腺钼靶片进行回顾性分析，分析人员为放射科医生/AI系统。放射科医生判断IBC乳腺钼靶片是否显示出乳腺癌的肉眼可见迹象（潜在漏诊的IBC）或未显示（无回顾性异常的IBC）。AI系统为每张乳腺钼靶片提供一个病例评分和一个预后风险类别。
有119例IBC病例（平均年龄57.3（5.4）岁）可供放射科医生/AI系统进行完整的回顾性评估。82例（68.9%）被归类为无回顾性异常的IBC，37例（31.1%）为潜在漏诊的IBC。所有IBC中有46.2%的病例评分≥25，25.2%≥50，13.4%≥75。在病例评分≥50的IBC中，有25.2%（与之相比，非乳腺癌人群中有13.4%）在一次共识会议中未被讨论，占所有IBC病例的11.4%。与无回顾性异常的IBC相比，潜在漏诊的IBC获得的病例评分和风险分类显著更高（病例评分均值：54.1对23.1；高风险：48.7%对14.7%；p<0.05）。无回顾性异常的IBC中有13.4%的病例评分≥50，其中62.5%在共识会议中未被讨论。
AI系统能够识别出乳腺癌风险较高的IBC筛查乳腺钼靶片，特别是在潜在漏诊的IBC中，但在一些放射科医生未发现异常的无回顾性异常的IBC中也能识别，这表明其有能力提高乳腺钼靶筛查质量。
问题AI总体上为加强乳腺癌筛查提供了一个有前景的机会，但缺乏其降低间期乳腺癌能力的证据。发现AI系统在大多数放射科医生回顾性检测到异常的间期乳腺癌筛查乳腺钼靶片中检测到了较高的乳腺癌风险。临床意义在乳腺钼靶筛查项目中使用AI系统可以在许多间期乳腺癌筛查乳腺钼靶片中识别乳腺癌风险，从而有可能减少间期乳腺癌的数量。
