RSS-Feed abonnieren
PDF herunterladen
DOI: 10.1055/s-0045-1814169
Deep Learning Model to Predict the Risk of Developing Breast Cancer in Mammography Based – A Pilot Study in Southern Brazil
Autor*innen
Funding The authors declare that they did not receive funding from agencies in the public, private or nonprofit sectors to conduct the present study.
Abstract
Introduction
Breast cancer is the leading cause of cancer-related deaths among women in Brazil, highlighting the importance of early detection to improve outcomes. Artificial intelligence (AI) has garnered considerable attention for its potential to enhance breast cancer screening by reducing unnecessary exams, minimizing diagnostic errors, and increasing efficiency and accuracy—exemplified by advanced tools like Mirai.
Materials and Methods
The present retrospective study analyzed 1,000 patients who underwent bilateral mammography from December 2019 to April 2024 at Hospital Santa Casa de Porto Alegre. All mammograms extracted in digital imaging and communications in medicine (DICOM) format were anonymized and processed by the Mirai algorithm to generate risk scores. Predictive performance was evaluated using discrimination metrics, such as C-index and area under the curve (AUC), as well as threshold analyses (F1-score, Youden's J) to estimate cancer risk.
Results
Mirai obtained a C-index of 0.76 (95% CI: 0.72–0.80) and an AUC of 0.81. The analysis further evaluated the F1-score and Youden's J statistic in an attempt to establish risk thresholds for cancer development.
Conclusion
The results suggest that the Mirai model holds promise as a valuable tool for breast cancer detection, particularly for early identification of high-risk patients.
Authors' Contributions
MZ: project administration, supervision, writing – review & editing; CFS: writing – original draft, writing – review & editing, conceptualization, resources; KAT: writing – original draft, writing – review & editing, formal analysis; RAS: conceptualization, writing – review & editing; TK: conceptualization, writing – review & editing; MGGS: conceptualization, formal analysis, methodology, software, writing – original draft, writing – review & editing; LRRL: conceptualization, writing – review & editing; JPM: formal analysis, methodology, software, writing – review & editing; LB: conceptualization, software; writing – review & editing; DR: conceptualization, software; writing – review & editing; JMB: formal analysis, methodology, software, writing – review & editing; MCSCS: conceptualization, formal analysis, writing – review & editing; ANK: conceptualization, investigation, project administration, writing – review & editing.
Publikationsverlauf
Eingereicht: 09. Mai 2025
Angenommen: 04. Oktober 2025
Artikel online veröffentlicht:
25. Januar 2026
© 2026. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution 4.0 International License, permitting copying and reproduction so long as the original work is given appropriate credit (https://creativecommons.org/licenses/by/4.0/)
Thieme Revinter Publicações Ltda.
Rua Rego Freitas, 175, loja 1, República, São Paulo, SP, CEP 01220-010, Brazil
Manuela Zereu, Carmela Farias da Silva, Katsuki Arima Tiscoski, Roberta de Almeida da Silva, Thiago Krieger, Márcio Gustavo Gusmão Scherer, Liana Rigon Rojas Lima, Jeison Philomena Molina, Letícia Batistela, Débora Roesler, Juliano Maurício Bordignon, Maria Claudia Schardosim Cotta de Souza, Antônio Nocchi Kalil. Deep Learning Model to Predict the Risk of Developing Breast Cancer in Mammography Based – A Pilot Study in Southern Brazil. Brazilian Journal of Oncology 2026; 22: s00451814169.
DOI: 10.1055/s-0045-1814169
-
References
- 1 Ministério da Saúde; Instituto Nacional de Câncer (INCA). Controle do Câncer de
Mama –Dados e Números – Incidência: Apresenta dados de incidência do câncer de mama
no Brasil, regiões e estados. [cited December 26, 2024]. Ministério da Saúde. Available
from: https://www.gov.br/inca/pt-br/assuntos/gestor-e-profissional-de-saude/controle-do-cancer-de-mama/dados-e-numeros/incidencia
- 2 World Health Organization. Newsroom – Fact sheets – Detail – Breast cancer. [cited
December 26, 2024].Available from: https://www.who.int/news-room/fact-sheets/detail/breast-cancer
- 3 Urban LABD, Chala LF, Paula IB. et al. Recommendations for breast cancer screening in Brazil, from the Brazilian College of Radiology and Diagnostic Imaging, the Brazilian Society of Mastology, and the Brazilian Federation of Gynecology and Obstetrics Associations. Radiol Bras 2023; 56 (04) 207-214
- 4 Nelson HD, Pappas M, Cantor A, Griffin J, Daeges M, Humphrey L. Harms of Breast Cancer Screening: Systematic Review to Update the 2009 U.S. Preventive Services Task Force Recommendation. Ann Intern Med 2016; 164 (04) 256-267
- 5 Rana M, Bhushan M. Machine learning and deep learning approach for medical image analysis: diagnosis to detection. Multimedia Tools Appl 2022; 82: 26731-26769
- 6 Santeramo R, Damiani C, Wei J, Montana G, Brentnall AR. Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case-control study. Breast Cancer Res 2024; 26 (01) 25
- 7 Yala A, Mikhael PG, Strand F. et al. Toward robust mammography-based models for breast cancer risk. Sci Transl Med 2021; 13 (578) eaba4373
- 8 Docker. Docker: The Industry-Leading Container Runtime. [cited January 23, 2025].
Docker Inc. Available from: https://www.docker.com/products/container-runtime/
- 9 Swets JA. Measuring the accuracy of diagnostic systems. Science 1988; 240 (4857) 1285-1293
- 10 Lamb LR, Mercaldo SF, Ghaderi K, Carney A, Lehman CD. Comparison of the Diagnostic Accuracy of Mammogram-based Deep Learning and Traditional Breast Cancer Risk Models in Patients Who Underwent Supplemental Screening with MRI. Radiology 2023; 308 (03) e223077
- 11 Jin Z, Zhang S, Zhang L, Chen Q, Liu S, Zhang B. Artificial Intelligence Risk Model (Mirai) Delivers Robust Generalization and Outperforms Tyrer-Cuzick Guidelines in Breast Cancer Screening. J Clin Oncol 2022; 40 (20) 2280-2281
- 12 Lehman CD, Mercaldo S, Lamb LR. et al. Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening. J Natl Cancer Inst 2022; 114 (10) 1355-1363
- 13 Omoleye OJ, Woodard AE, Howard FM. et al. External Evaluation of a Mammography-based Deep Learning Model for Predicting Breast Cancer in an Ethnically Diverse Population. Radiol Artif Intell 2023; 5 (06) e220299
- 14 Arasu VA, Habel LA, Achacoso NS. et al. Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study. Radiology 2023; 307 (05) e222733
- 15 Park EK, Lee H, Kim M. et al. Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction. Diagnostics (Basel) 2024; 14 (12) 1212
- 16 Hill H, Roadevin C, Duffy S, Mandrik O, Brentnall A. Cost-Effectiveness of AI for Risk-Stratified Breast Cancer Screening. JAMA Netw Open 2024; 7 (09) e2431715
- 17 Avendano D, Marino MA, Bosques-Palomo BA. et al. Validation of the Mirai model for predicting breast cancer risk in Mexican women. Insights Imaging 2024; 15 (01) 244