Background/Objective:The objective was to evaluate the standalone performance of an AI system, Transpara 1.7.1 (ScreenPoint Medical), in screening mammography cases proceeding to stereotactic biopsy using histopathological results as ground truth.Methods:This retrospective study included 202 asymptomatic female patients (mean age: 57.8 years) who underwent stereotactic biopsy at a multicenter academic institution between October 2022 and September 2023 with a preceding screening mammogram within 14 months. Transpara AI risk scores were compared to pathology results (benign versus malignant). Performance metrics for AI including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC) were calculated.Results:Transpara AI classified 20 of 39 malignant findings (51%) as elevated risk compared with 50 of 211 total findings (24%). AI score was positively correlated with malignancy (r = 0.29,p< 0.001). Sensitivity for detecting malignancy (classifying as intermediate or elevated risk) was 94.9% (95% CI: 81.4–94.1), specificity was 24.4% (95% CI: 18.3–31.7), PPV was 22.2% (95% CI: 16.3–29.4), and NPV was 95.5% (95% CI: 83.3–99.2). Transpara had fair performance in detecting breast cancer with AUC 0.73 (95% CI: 0.63–0.82).Conclusions:Transpara AI is a useful screening mammography triage tool. Given its high sensitivity and high negative predictive value, AI may be used to guide radiologists in making biopsy or follow up recommendations. However, the high false-positive rate and presence of two false negatives underscore the need for radiologists to use caution and clinical expertise when interpreting AI results.
背景/目的：本研究旨在以组织病理学结果为金标准，评估人工智能系统Transpara 1.7.1（ScreenPoint Medical）在筛查性乳腺X线摄影病例中进行立体定向活检的独立性能。 方法：这项回顾性研究纳入了202名无症状女性患者（平均年龄：57.8岁），她们于2022年10月至2023年9月期间在一家多中心学术机构接受了立体定向活检，且在活检前14个月内进行过筛查性乳腺X线摄影。将Transpara AI的风险评分与病理结果（良性 vs. 恶性）进行比较。计算了AI的性能指标，包括敏感性、特异性、阳性预测值、阴性预测值以及受试者工作特征曲线下面积。 结果：在39例恶性发现中，Transpara AI将20例（51%）归类为高风险，而在总共211例发现中，有50例（24%）被归类为高风险。AI评分与恶性程度呈正相关（r = 0.29, p < 0.001）。检测恶性肿瘤（归类为中风险或高风险）的敏感性为94.9%（95% CI: 81.4–94.1），特异性为24.4%（95% CI: 18.3–31.7），阳性预测值为22.2%（95% CI: 16.3–29.4），阴性预测值为95.5%（95% CI: 83.3–99.2）。Transpara在检测乳腺癌方面表现尚可，AUC为0.73（95% CI: 0.63–0.82）。 结论：Transpara AI是一个有用的筛查性乳腺X线摄影分流工具。鉴于其高敏感性和高阴性预测值，AI可用于指导放射科医生做出活检或随访建议。然而，其高假阳性率以及存在两例假阴性的情况，强调了放射科医生在解读AI结果时需要谨慎并运用临床专业知识。
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