ArticleBritish journal of cancer2025
Development of radiomics-based models on mammograms with mass lesions to predict prognostically relevant characteristics of invasive breast cancer in a screening cohort.
Article in British journal of cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Development and internal validation of mammography feature-based prognostic models for distant recurrence-free survival of invasive breast cancer in a screening cohort.NPJ breast cancer · 2026Article
- More than density: validating a mammographic masking prediction model in Dutch breast cancer screening.European radiology · 2025Article
- Development and validation of an ultrasound-based AI-radiomics model for diagnosing and risk-stratifying gastrointestional stromal tumors: a retrospective diagnostic study.BMC medical imaging · 2025Article
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14 authors.
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Abstract
backgroundOptimizing breast-screening performance involves minimizing overdiagnosis of prognostically favorable invasive breast cancer (IBC) that does not need immediate recall and underdiagnosis of prognostically unfavorable IBC that is not recalled timely. We investigated whether mammographic features of masses predict prognostically relevant IBC characteristics.
methodsIn a screening cohort, we obtained pathological information of 1587 IBCs presenting as a mass through the nationwide cancer registry and pathology databank. We developed models based on mammographic tumor appearance to predict whether IBC was prognostically favorable (T1N0M0 luminal A-like) or unfavorable. Models were based on 1095 positive screening mammograms (possible overdiagnosis), or on 603 last negative mammograms with in retrospect visible masses (possible underdiagnosis). We calculated performance metrics using cross-validation.
results23.5% of masses were prognostically favorable IBC. Using 1095 positive mammograms, the model's predictions to have prognostically favorable IBC (10th-90th percentile range 8.7-47.0%) yielded AUC 0.75 (SD across repeats 0.01), slope 1.16 (SD 0.07). Performance in 603 last negative screening mammograms with masses was poor: AUC 0.60 (SD 0.02), slope 0.85 (SD 0.28).
conclusionsMammography-based models from masses representing IBC at time of recall (possible overdiagnosis) predict prognostically relevant characteristics of IBC. Models based on in retrospect visible masses (possible underdiagnosis) performed poorly.
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