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Purpose <p>Adherence 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.</p> Methods <p>Clinical data from 403 breast cancer patients were collected and analyzed. Patients were randomly divided into training (<i>n</i> = 281) and validation (<i>n</i> = 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).</p> Results <p>Multivariate analysis identified medical insurance type (OR = 3.435, 95% CI: 1.230–9.592, <i>P</i> = 0.019), psychological assessment (OR = 0.779, 95% CI: 0.712–0.853, <i>P</i> &lt; 0.001), and perceived social support (OR = 1.131, 95% CI: 1.088–1.177, <i>P</i> &lt; 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.</p> Conclusions <p>The 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.</p>

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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

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Dong Liu,
  • Haiyan Xu

摘要

Purpose

Adherence 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.

Methods

Clinical 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).

Results

Multivariate 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.

Conclusions

The 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.