Verboom Sarah D, Mainprize James G, Peters Jim, Broeders Mireille, Yaffe Martin J, Sechopoulos Ioannis
Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands.
Eur Radiol. 2025 May 29. doi: 10.1007/s00330-025-11687-x.
To validate a lesion masking prediction model, Mammatus, previously developed on a North American cohort, on a larger retrospective breast cancer screening cohort from a single center in the Netherlands.
Mammatus was applied to all digital mammography screening examinations with a unilateral invasive breast cancer that was either diagnosed at screening or within 24 months after a negative screening, called interval cancers. All mammograms were retrospectively evaluated for the visibility of malignant masses using all available imaging and clinical information. The area under the receiver operator characteristic (ROC) curve (AUC) when using Mammatus to distinguish examinations with screen-detected cancers (assumed low masking risk) from interval cancers (assumed high masking risk) was computed. The AUC was compared to that of the original cohort and to that obtained using volumetric breast density (VBD) as a predictor. A second tghree-category ROC analysis was performed, with interval cancers that were retrospectively visible classified as intermediate lesion masking.
Mammatus achieved an AUC of 0.69 (95% CI: 0.66-0.73) for distinguishing between screen-detected-cancer exams (n = 635) and interval-cancer exams (n = 304). This performance did not differ from the original study (AUC = 0.75 (95% CI: 0.68-0.82), p = 0.15), and outperformed VBD (AUC = 0.66 (95% CI: 0.63-0.70, p = 0.019). Mammatus was better at identifying mammograms at low risk of lesion masking (AUC = 0.73 (95% CI: 0.70-0.76)) compared to those with high risk (AUC = 0.69 (95% CI: 0.64-0.74)).
Mammatus performed well in predicting breast cancer-masking risk in a Dutch screening cohort. This suggests that adding information other than density facilitates the prediction of lesion masking.
Question Mammographic lesion masking prediction models, such as Mammatus, require external validation in other screening programs before clinical application is possible. Findings Mammatus maintained similar performance in predicting lesion masking in a Dutch screening cohort and showed added benefit compared to VBD. Clinical relevance An externally validated lesion masking prediction model for digital mammography could potentially be used to identify screened women who could benefit from supplemental or alternative screening, with better accuracy than VBD alone.
在来自荷兰单一中心的更大规模回顾性乳腺癌筛查队列中，验证先前基于北美队列开发的病变掩盖预测模型Mammatus。
将Mammatus应用于所有单侧浸润性乳腺癌的数字乳腺钼靶筛查检查，这些癌症要么在筛查时被诊断出，要么在筛查阴性后的24个月内被诊断出，即间期癌。使用所有可用的影像和临床信息，对所有乳腺钼靶片进行回顾性评估，以确定恶性肿块的可见性。计算使用Mammatus区分筛查发现的癌症（假定低掩盖风险）检查和间期癌（假定高掩盖风险）检查时的受试者操作特征（ROC）曲线下面积（AUC）。将该AUC与原始队列的AUC以及使用乳腺体积密度（VBD）作为预测指标获得的AUC进行比较。进行了第二项三类ROC分析，将回顾性可见的间期癌分类为中度病变掩盖。
在区分筛查发现癌症的检查（n = 635）和间期癌检查（n = 304）时，Mammatus的AUC为0.69（95%CI：0.66 - 0.73）。该性能与原始研究无差异（AUC = 0.75（95%CI：0.68 - 0.82），p = 0.15），且优于VBD（AUC = 0.66（95%CI：0.63 - 0.70，p = 0.019）。与高风险乳腺钼靶片（AUC = 0.69（95%CI：0.64 - 0.74））相比，Mammatus在识别低病变掩盖风险的乳腺钼靶片方面表现更好（AUC = 0.73（95%CI：0.70 - 0.76））。
Mammatus在预测荷兰筛查队列中的乳腺癌掩盖风险方面表现良好。这表明添加密度以外的信息有助于病变掩盖的预测。
问题 乳腺钼靶病变掩盖预测模型，如Mammatus，在临床应用前需要在其他筛查项目中进行外部验证。发现 Mammatus在预测荷兰筛查队列中的病变掩盖方面保持了相似的性能，并且与VBD相比显示出额外的优势。临床意义 经过外部验证的数字乳腺钼靶病变掩盖预测模型可能潜在地用于识别可能从补充或替代筛查中受益且准确性优于单独使用VBD的筛查女性。
