ArticleScientific reports2025
Prognostic model for predicting recurrence in breast cancer patients in Saudi Arabia.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Perioperative machine learning models with SHAP interpretation for predicting adverse outcomes in breast cancer surgery.Frontiers in surgery · 2026Article
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7 authors.
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Abstract
Breast cancer recurrence presents a significant global health challenge, and accurate prediction is crucial for effective patient management and improved outcomes. Reliable predictive tools can help tailor therapeutic approaches, provide personalized care, and enhance patient outcomes. In light of the current lack of such tools in clinical practice, our study aimed to develop predictive models for breast cancer recurrence within three years of treatment. We analyzed data from 408 breast cancer patients at the King Fahd Specialist Hospital in Dammam, Saudi Arabia and divided them into training (n = 285) and test (n = 123) cohorts. Using multivariable penalized logistic regression combined with a nested cross-validation framework and multivariate Cox regression analysis to determine time-dependent risk factors for breast cancer recurrence, we developed prognostic models that incorporated age, stage, tumor size, and treatment type. We evaluated the performance of the models using both the area under the receiver operating characteristic curve for multivariate logistic regression and C-index for multivariate Cox regression. The multivariate logistic regression model achieved an area under the curve (AUC) of 76% (95% confidence interval [CI]: 72-81%) for the training set and 76% (95% CI: 66-87%) for the test set. The Cox regression analysis yielded a C-index of 0.81 for the training set (95% CI: 0.73-0.84) and 0.84 for the test set (95% CI: 0.76-0.89). Chemotherapy was found to decrease recurrence odds by 86% (adjusted odds ratio [AOR]: 0.143, 95% CI: 0.089-0.218, p < 0.0001), and surgery resulted in a 99% reduction in recurrence probability (AOR: 0.009, 95% CI: 0.005-0.014, p < 0.0001). Increased tumor size improved the recurrence odds by 48.5% (AOR: 1.485, 95% CI: 1.128-1.918, p = 0.0043), while age did not significantly predict recurrence (AOR: 0.841, 95% CI: 0.657-1.061, p = 0.1398). The newly developed, routinely collected baseline clinical features to predict breast cancer recurrence may be a valuable tool for clinical decision-making and is freely available online. The tool can be accessed through the following link: https://iv3p9h-nurudeen-adegoke.shinyapps.io/breast_cancer .
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