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. 2025 Jun;132(11):1040-1049.
doi: 10.1038/s41416-025-02995-6. Epub 2025 Apr 6.

Development of radiomics-based models on mammograms with mass lesions to predict prognostically relevant characteristics of invasive breast cancer in a screening cohort

Jim Peters  1 , Merle M van Leeuwen  2 , Nikita Moriakov  3   4 , Jos A A M van Dijck  5 , Ritse M Mann  4   6 , Jonas Teuwen  3 , Esther H Lips  2 , Alexandra W van den Belt-Dusebout  2 , Jelle Wesseling  2   7   8 , Bas B L Penning de Vries  9 , Sarah Verboom  6 , Nico Karssemeijer  6 , Sjoerd G Elias  9 , Mireille J M Broeders  5   10
Affiliations

Development of radiomics-based models on mammograms with mass lesions to predict prognostically relevant characteristics of invasive breast cancer in a screening cohort

Jim Peters et al. Br J Cancer. 2025 Jun.

Abstract

Background: Optimizing 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.

Methods: In 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.

Results: 23.5% 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).

Conclusions: Mammography-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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Conflict of interest statement

Competing interests: The authors declare the following relationships: N.K. is board member and shareholder of ScreenPoint Medical and shareholder of Volpara Technology. RM. received research grants or equipment from Bayer, Siemens, ScreenPoint Medical, Koning, Becton Dickinson, PA Imaging and Lunit and provided consultancy for ScreenPoint Medical, Becton Dickinson, Bayer, Guerbet, Bracco and Siemens. All other authors declare no potential conflict of interest Ethics approval and consent to participate: This study was conducted in accordance with the declaration of Helsinki. No explicit written or verbal consent was obtained, but consent was obtained through an opt-out procedure that exists in the Dutch screening program. When participating in the program, women are informed that their data can be used for evaluation of the program or scientific purposes to improve the program. If women chose to opt-out, their data was not used. The Radboud university medical center ethics committee declared that this study falls outside the scope of the Dutch Medical Research involving Human Subjects Act and could be carried out without approval of an Institutional Review Board.

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