Clinical Analysis of the Short-Term Outcome of Papillary Thyroid Micro Carcinoma After (131)I Treatment

PURPOSE: To explore the factors that influence the short-term clinical outcome after the first (131)I treatment of papillary thyroid micro carcinoma (PTMC). PATIENTS AND METHODS: From October 2015 to June 2018, patients who were diagnosed with PTMC with lymph node metastasis were analyzed retrospectively, excluding patients with incomplete clinical data, distant metastasis, positive TGAb, TSH<30 mIU/L. The baseline data of sex, age, time from last surgery to first (131)I treatment, tumor pathology information, and biochemical information were collected before admission. All patients included had radioactive iodine (RAI) with 3.70 GBq. The treatment response of patients was evaluated 6–8 months after discharge. By means of univariate and multivariate analysis, including excellent response (ER) and non-excellent response (NER) groups of clinical data, we assessed the impact of (131)I on patients’ outcome. A nomogram model was established based on the above independent risk factors. RESULTS: A total of 206 patients (59 males and 147 females, mean age 43.4 ± 10.6 years) were included in the study. The median follow-up time was 169.4 ± 10.5 days, including 139 patients in ER group (67.4%) and 67 patients in NER group (32.5%). Four factors including combining Hashimoto’s thyroiditis, pre-ablative Tg levels, UIE levels, and lateral lymph node numbers were statistically different between ER group and NER group with significance at P < 0.05. Further multivariate analysis showed that Hashimoto’s thyroiditis and Ps-Tg levels could be used as independent factors. The model verification showed that the C-index of the modeling set was 0.822, indicating that the nomogram model had a good predicted accuracy. CONCLUSION: Our data suggest that coexisting Hashimoto’s thyroiditis and elevated Ps-Tg levels are predictive factors for short-term outcome of thyroid micro papillary carcinoma after (131)I treatment. Also, the nomogram model had a good predicted accuracy.