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Updated: May 5, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Nomogram models for predicting outcomes in thyroid cancer patients with distant metastasis receiving 131iodine
Shui Jin1, Xuemei Ye1,2, Ting Ye1
1Department of Nuclear Medicine, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China.
Abstract:
This study aimed to establish and validate prognostic nomogram models for patients who underwent 131I therapy for thyroid cancer with distant metastases. The cohort was divided into training (70%) and validation (30%) sets for nomogram development. Univariate and multivariate Cox regression analyses were used to identify independent predictors for overall survival (OS) and progression-free survival (PFS). Nomograms were developed based on these predictors, and Kaplan-Meier curves were constructed for validation. Among 451 patients who were screened, 412 met the inclusion criteria and were followed-up for a median duration of 65.2 months. The training and validation sets included 288 and 124 patients, respectively. Pathological type, first 131I administrated activity, and lesion 131I uptake in lesions were independent predictors for PFS. For OS, predictors included gender, age, metastasis site, first 131I administrated activity, 131I uptake, pulmonary lesion size, and stimulated thyroglobulin levels. These predictors were used to construct nomograms for predicting PFS and OS. Low-risk patients had significantly longer PFS and OS compared to high-risk patients, with 10-year PFS rates of 81.1% vs. 51.9% and 10-year OS rates of 86.2% vs. 37.4%. These may aid individualized prognostic assessment and clinical decision-making, especially in determining the prescribed activity for the first 131I treatment.

