Abstract: Objective To investigate the influencing factors of cancer⁃specific survival (CSS) in patients aged ≥55 years with N1b stage papillary thyroid carcinoma (PTC), develop a random survival forest (RSF) model and used for risk stratification, and assess the survival benefits of different treatment options. Methods Clinical data of 2, 864 eligible patients with PTC in Surveillance, Epidemiology, and End Results(SEER) database from 2004 to 2019 were collected and randomly divided into the training group (n=2, 008) and the validation group (n=856) at a ratio of 7∶3. The potential risk factors of CSS were determined using Cox proportional hazards regression model and Kaplan⁃Meier (K⁃M) survival analyses. Key variables were selected through Lasso regression , and both Lasso⁃Cox and RSF prediction models were developed. Model performance was evaluated using the concordance index (C⁃index), time⁃dependent receiver operating characteristic (tROC) curves, and Brier scores. Risk stratification was conducted, and survival benefits associated with different treatment strategies were evaluated. Results Cox proportional hazards regression model indicated that older age, distant metastasis, larger tumor size, extensive extrathyroidal extension, marital status, lack of radiotherapy, thyroid lobectomy and receipt of chemotherapy were independent risk factors for CSS (all P<0.05). Lasso regression screened out four key variables: age, tumor size, extrathyroidal extension, and distant metastasis. Both models showed high discrimination and calibration, with the RSF model superior in the training group and comparable in validation group. Log⁃rank analysis revealed that the 3-year, 5-year and 10-year CSS rates in the high⁃risk group were 68.15%, 58.63% and 37.52%, compared with 96.86%, 94.38% and 88.87% in the low⁃risk group, respectively. neither the surgical approach nor radiotherapy significantly affected survival in the low⁃risk group, whereas significant survival differences were observed in the high⁃risk group. Conclusions The RSF model developed in this study demonstrated strong predictive performance for CSS in patients aged ≥55 years with N1b stage PTC. This model facilitates risk stratification and provides a basis for clinical treatment decision⁃making.

Key words: Papillary thyroid cancer, Lymph node metastasis, Random survival forest, Survival analysis