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Objective <p>Early diagnosis of radioiodine-refractory differentiated thyroid cancer (RAIR-DTC) is crucial for timely treatment adjustment. Currently, diagnosis relies on clinical progression and assessment of iodine uptake in lesions, which is a time-consuming process. The objective of this study is to identify risk factors associated with RAIR-DTC and develop a visual predictive model to facilitate earlier identification.</p> Methods <p>Retrospectively collected data (including general information, biochemical indicators, pathological and imaging data) of DTC patients from August 2020 to September 2025. A total of 234 patients were included and divided into RAIR-DTC (<i>n</i> = 105) and non-RAIR-DTC (<i>n</i> = 129) according to guidelines. The dataset was divided into a model development cohort and an external validation cohort using January 2025 as the temporal cutoff. The model development cohort was randomly split into a training cohort and an internal validation cohort in a 7:3 ratio. Subsequently, univariate and multivariate logistic regression analysis were performed to determine the independent predictors of RAIR-DTC, which was then visualized using a nomogram. The performance of the nomogram was evaluated by the area under the receiver operating characteristic(AUC) in training, an internal validation and external validation cohorts. Calibration curve and decision curve analysis(DCA) were used to validate the nomogram’s performance. Additionally, progression-free survival (PFS) analysis was conducted using the Kaplan–Meier method.</p> Results <p>Through multivariate logistic regression, treatment response evaluation, recurrent /persistent lesions, sTg-second, and the ratio of sTg-second/sTg-first were obtained to develop a nomogram model for predicting RAIR-DTC. In the training cohort, internal validation cohort and external validation, the AUC were 0.893, 0.920 and 0.743, respectively. The nomogram fit well in calibration curves (<i>P</i> &gt; 0.05), and DCA further confirmed the clinical usefulness of the nomogram. Additionally, the RAIR-DTC group exhibited significantly shorter PFS compared to the non-RAIR group.</p> Conclusions <p>The nomogram model, constructed based on dynamic serological, imaging, and clinical evaluation, demonstrates good predictive performance and clinical utility. This model provides valuable guidance for individualized treatment decision-making in DTC patients.</p>

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Dyna-RR-DTC model: integrating clinicopathological features with dynamic indicators to predict radioiodine-refractory differentiated thyroid cancer risk

  • 期刊论文

  • 发表日期：

    2025年10月15日

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  • Shi-qi Chen,
  • Wei Jiang,
  • Peng-qing Wu,
  • Xue-zhong Chen,
  • Qing Zhang

摘要

Objective

Early diagnosis of radioiodine-refractory differentiated thyroid cancer (RAIR-DTC) is crucial for timely treatment adjustment. Currently, diagnosis relies on clinical progression and assessment of iodine uptake in lesions, which is a time-consuming process. The objective of this study is to identify risk factors associated with RAIR-DTC and develop a visual predictive model to facilitate earlier identification.

Methods

Retrospectively collected data (including general information, biochemical indicators, pathological and imaging data) of DTC patients from August 2020 to September 2025. A total of 234 patients were included and divided into RAIR-DTC (n = 105) and non-RAIR-DTC (n = 129) according to guidelines. The dataset was divided into a model development cohort and an external validation cohort using January 2025 as the temporal cutoff. The model development cohort was randomly split into a training cohort and an internal validation cohort in a 7:3 ratio. Subsequently, univariate and multivariate logistic regression analysis were performed to determine the independent predictors of RAIR-DTC, which was then visualized using a nomogram. The performance of the nomogram was evaluated by the area under the receiver operating characteristic(AUC) in training, an internal validation and external validation cohorts. Calibration curve and decision curve analysis(DCA) were used to validate the nomogram’s performance. Additionally, progression-free survival (PFS) analysis was conducted using the Kaplan–Meier method.

Results

Through multivariate logistic regression, treatment response evaluation, recurrent /persistent lesions, sTg-second, and the ratio of sTg-second/sTg-first were obtained to develop a nomogram model for predicting RAIR-DTC. In the training cohort, internal validation cohort and external validation, the AUC were 0.893, 0.920 and 0.743, respectively. The nomogram fit well in calibration curves (P > 0.05), and DCA further confirmed the clinical usefulness of the nomogram. Additionally, the RAIR-DTC group exhibited significantly shorter PFS compared to the non-RAIR group.

Conclusions

The nomogram model, constructed based on dynamic serological, imaging, and clinical evaluation, demonstrates good predictive performance and clinical utility. This model provides valuable guidance for individualized treatment decision-making in DTC patients.