ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2026
Development and validation of a nomogram for predicting the probability of medication adherence to adjuvant endocrine therapy in breast cancer patients: a predictive modeling study.
Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 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
purposeAdherence to adjuvant endocrine therapy (AET) is critical for breast cancer prognosis, yet there is a current lack of convenient predictive tools that integrate multidimensional factors. This study aimed to develop a nomogram prediction model for forecasting AET adherence in breast cancer patients.
methodsClinical data from 403 breast cancer patients were collected and analyzed. Patients were randomly divided into training (n = 281) and validation (n = 122) cohorts at a 7:3 ratio. Risk factors influencing treatment adherence were screened using univariate and multivariate logistic regression. The nomogram was constructed and validated using R software, with its predictive performance and clinical utility evaluated through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
resultsMultivariate analysis identified medical insurance type (OR = 3.435, 95% CI: 1.230-9.592, P = 0.019), psychological assessment (OR = 0.779, 95% CI: 0.712-0.853, P < 0.001), and perceived social support (OR = 1.131, 95% CI: 1.088-1.177, P < 0.001) as independent predictors of AET adherence. The resulting nomogram achieved AUC values for the training cohort and validation cohort of 0.933 (95% CI: 0.905-0.961) and 0.891 (95% CI: 0.826-0.957), respectively. Calibration curves and DCA demonstrated excellent consistency and clinical applicability.
conclusionsThe study identified medical insurance type, psychological assessment, and perceived social support as key factors influencing adherence to AET. The developed nomogram on this basis provides a visual tool for identifying high-risk populations with poor adherence to AET, which helps to carry out personalized interventions for different patients in the future.
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