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Article Open Access

A clinical feature‑based nomogram for the personalized prediction of radioactive iodine therapy outcomes in patients with papillary thyroid carcinoma

  • Authors:
    • Jingjia Cao
    • Huazhen Liang
    • Xiang Li
    • Xin Guan
    • Hao Xu
  • View Affiliations / Copyright

    Affiliations: Department of Nuclear Medicine, The Second Hospital of Shandong University, Jinan, Shandong 250033, P.R. China, Department of Second Clinical Medical School, Shandong University, Jinan, Shandong 250012, P.R. China, Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, Shandong 271000, P.R. China, Department of Breast and Thyroid Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, P.R. China
    Copyright: © Cao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 577
    |
    Published online on: October 7, 2025
       https://doi.org/10.3892/ol.2025.15323
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Abstract

The present study aimed to develop and validate a clinical nomogram for personalized prediction of radioactive iodine (RAI) therapy outcomes in patients with papillary thyroid carcinoma (PTC), addressing the limitations of single‑factor predictors such as thyroglobulin (Tg). A retrospective analysis of 1,073 patients with PTC treated with RAI was conducted, with cohorts divided into training (n=751) and validation (n=322) sets. Multivariate logistic regression identified age, sex, tumor subtype, size, metastasis (M) stage and Tg level as independent predictors of non‑excellent response at 6 months (all P<0.05), while subtype and Tg level remained significant for 3‑year outcomes. The nomogram demonstrated strong discrimination, with AUCs of 0.823 (95% CI, 0.801‑0.845) for 6 months and 0.929 (95% CI, 0.915‑0.943) for 3 years in the training cohort, and AUCs of 0.804 (95% CI, 0.793‑0.820) for 6 months and 0.930 (95% CI, 0.910‑0.946) for 3 years in the validation cohort. Calibration was excellent (mean absolute errors, 0.018‑0.021). Probability density and K‑means clustering confirmed accurate risk stratification. Notably, even without Tg, the model retained predictive power (6 months: AUC, 0.777; 95% CI, 0.725‑0.793; 3 years: AUC, 0.897; 95% CI, 0.843‑0.909) using factors such as subtype, M stage and lymph node ratio. The suggested nomogram integrates readily available clinical features to personalize risk assessment, aiding tailored follow‑up and treatment; it highlights synergistic interactions among secondary factors, offering robust prognostication when Tg is unreliable. This nomogram fills a gap in post‑RAI outcome prediction, enhancing precision in PTC management.
View Figures

Figure 1

Development, iteration and validation
of nomogram. ROC, receiver operating characteristic.

Figure 2

Nomogram excluding Tg. (A) 6-month
response and (B) 3-year response. FV, follicular variant; TCV, tall
cell variant; BIR, biochemical incomplete response; SIR, structural
incomplete response; M, metastasis; LNR, lymph node metastasis
ratio.

Figure 3

Evaluation of the excluding
thyroglobulin nomogram. (A) ROC comparison of training (95% CI,
0.725–0.793) and validation (95% CI, 0.721–0.790) groups for 6
months. (B) ROC comparison for training (95% CI, 0.843–0.909) and
validation (95% CI, 0.826–0.913) groups for 3 years. (C)
Probability density assessment of training and validation groups
for 6 months. (D) Probability density assessment of training and
validation groups for 3 years. ROC, receiver operating
characteristic; AUC, area under the curve.

Figure 4

Nomogram incorporating Tg. (A) 6-month
response and (B) 3-year response. FV, follicular variant; TCV, tall
cell variant; BIR, biochemical incomplete response; SIR, structural
incomplete response; M, metastasis; Tg, thyroglobulin.

Figure 5

Evaluation of the incorporating
thyroglobulin nomogram. (A) ROC comparison of the training (95% CI,
0.801–0.845) and validation (95% CI, 0.793–0.820) groups for 6
months. (B) ROC comparison of the training (95% CI, 0.915–0.943)
and validation (95% CI, 0.910–0.946) groups for 3 years. (C)
Probability density assessment of the training and validation
groups for 6 months. (D) Probability density assessment of the
training and validation groups for 3 years.
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Copy and paste a formatted citation
Spandidos Publications style
Cao J, Liang H, Li X, Guan X and Xu H: A clinical feature‑based nomogram for the personalized prediction of radioactive iodine therapy outcomes in patients with papillary thyroid carcinoma. Oncol Lett 30: 577, 2025.
APA
Cao, J., Liang, H., Li, X., Guan, X., & Xu, H. (2025). A clinical feature‑based nomogram for the personalized prediction of radioactive iodine therapy outcomes in patients with papillary thyroid carcinoma. Oncology Letters, 30, 577. https://doi.org/10.3892/ol.2025.15323
MLA
Cao, J., Liang, H., Li, X., Guan, X., Xu, H."A clinical feature‑based nomogram for the personalized prediction of radioactive iodine therapy outcomes in patients with papillary thyroid carcinoma". Oncology Letters 30.6 (2025): 577.
Chicago
Cao, J., Liang, H., Li, X., Guan, X., Xu, H."A clinical feature‑based nomogram for the personalized prediction of radioactive iodine therapy outcomes in patients with papillary thyroid carcinoma". Oncology Letters 30, no. 6 (2025): 577. https://doi.org/10.3892/ol.2025.15323
Copy and paste a formatted citation
x
Spandidos Publications style
Cao J, Liang H, Li X, Guan X and Xu H: A clinical feature‑based nomogram for the personalized prediction of radioactive iodine therapy outcomes in patients with papillary thyroid carcinoma. Oncol Lett 30: 577, 2025.
APA
Cao, J., Liang, H., Li, X., Guan, X., & Xu, H. (2025). A clinical feature‑based nomogram for the personalized prediction of radioactive iodine therapy outcomes in patients with papillary thyroid carcinoma. Oncology Letters, 30, 577. https://doi.org/10.3892/ol.2025.15323
MLA
Cao, J., Liang, H., Li, X., Guan, X., Xu, H."A clinical feature‑based nomogram for the personalized prediction of radioactive iodine therapy outcomes in patients with papillary thyroid carcinoma". Oncology Letters 30.6 (2025): 577.
Chicago
Cao, J., Liang, H., Li, X., Guan, X., Xu, H."A clinical feature‑based nomogram for the personalized prediction of radioactive iodine therapy outcomes in patients with papillary thyroid carcinoma". Oncology Letters 30, no. 6 (2025): 577. https://doi.org/10.3892/ol.2025.15323
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