Objectives 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. Materials and methods 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. Results 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)). Conclusion 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. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Mammographic lesion masking prediction models, such as Mammatus, require external validation in other screening programs before clinical application is possible. <Emphasis Type="BoldItalic">Findings</Emphasis> Mammatus maintained similar performance in predicting lesion masking in a Dutch screening cohort and showed added benefit compared to VBD. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> 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. Graphical Abstract

More than density: validating a mammographic masking prediction model in Dutch breast cancer screening

期刊论文
出版模式：

开放获取
发表日期：

2025年5月29日

查看全文

Sarah D. Verboom,
James G. Mainprize,
Jim Peters,
Mireille Broeders,
Martin J. Yaffe,
Ioannis Sechopoulos

摘要

Objectives

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.

Materials and methods

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.

Results

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)).

Conclusion

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.

Key Points

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.

Graphical Abstract