ArticleJournal of imaging informatics in medicine2026
Personalized Breast Cancer Prognosis Using a Model Based on MRI and Clinicopathological Variables.
Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study aimed to develop and internally validate a prognostic prediction model based on MRI, pathological, and clinical findings to predict breast cancer recurrence and death. A retrospective study prediction model was developed using data from 922 breast cancer patients recruited in Duke University Hospital from January 2000 to March 2014. Cox and binary logistic regressions were implemented for hazard score and 2-, 3-, 5-, and 8-year survivals and recurrences. After assessing the collinearity of predictors, both univariable and multivariable analyses were performed. Qualitative and quantitative MRI variables were selected based on clinical expert opinion and literature review. Bootstrap and leave-one-out methods were used for internal validation. Calibration, shrinkage, time-dependent receiver operating characteristic (ROC) curve, and decision-curve analyses were also performed. Finally, a user-friendly calculator was built. Of included participants, 62 (6.72%) died with a mean patient-year follow-up of 8.89 years (CI = 8.74 to 9.04), while 90 (9.76%) experienced recurrence with mean patient-year follow-up of 8.20 years (CI = 7.92 to 8.48). The Akaike information criterion (AIC) value of survival and recurrence models were 752.9 and 1020.7, indicating a good balance between model complexity and fit. Validation model adjusted area under curve (AUC) in 8-, 5-, 3-, and 2-year survivals were 0.740 (CI = 0.711 to 0.768), 0.741 (CI = 0.712 to 0.770), 0.788 (CI = 0.761 to 0.816), and 0.783 (CI = 0.755 to 0.809), while in 8-, 5-, and 3-year recurrences were 0.678 (CI = 0.647 to 0.708), 0.696 (CI = 0.664 to 0.727), and 0.769 (CI = 0.740 to 0.798), respectively. Good calibration and shrinkage parameters were achieved. The internal validation and decision curve analyses highlighted the usefulness of the model across all probability levels. The combined MRI-pathological-clinical model has excellent performance in predicting overall survival and recurrence of breast cancer and may have a role to play in daily personalized breast cancer therapy.
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