Cross-National Radiomics Validation Using Mammography to Predict Occult Invasion in Ductal Carcinoma in Situ

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test. Results The study included 1498 women (age range, 31-89 years; mean age, 59 years ± 9 [SD]), as follows: 696 women from the United States, 618 women from the United Kingdom, and 184 women from the Netherlands, with upstaging rates of 16.1%, 16.7%, and 14.1%, respectively. Internal cross-validation areas under the receiver operating characteristic curve (AUCs) were 0.675 (95% CI: 0.671, 0.679), 0.603 (95% CI: 0.567, 0.722), and 0.701 (95% CI: 0.697, 0.706) for the U.S., UK, and Netherlands datasets, respectively. The model that was trained on the U.S. dataset yielded cross-national validation AUCs of 0.604 (95% CI: 0.560, 0.648) and 0.682 (95% CI: 0.607, 0.757) for the UK and Netherlands datasets. Conclusion Radiomic machine learning models were shown to have the potential to predict occult invasive cancer in women with DCIS across diverse settings. © RSNA, 2025

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