Analysis of prognostic factors and establishment of a recurrence risk prediction model for papillary thyroid carcinoma based on BRAF stratification.

Abstract

Background: Predicting the likelihood of papillary thyroid carcinoma (PTC) recurrence is crucial for improving patient outcomes. The association between the BRAF V600E (BRAF) mutation and PTC recurrence remains controversial. Our goal was to determine prognostic features of PTC patients and construct models for predicting recurrence risk according to BRAF mutation status.

Methods: A total of 811 PTC patients whose clinical information and survival data were available were included in this study. Independent prognostic variables of PTC identified by screening via LASSO-Cox regression analysis were then used to construct nomograms. The performance of the predictive models was assessed according to the C-index, ROC curve, validation curve, and decision curve analyses. Kaplan-Meier curves were used to analyze differences between patients grouped according to prognostic factors and relapse risk.

Results: Multivariate Cox regression analysis demonstrated that extrathyroidal extension (ETE), vascular tumor thrombus, and lymph node yield (LNY) were correlated with recurrence-free survival (RFS) in the BRAF mutation-negative group, while extranodal extension (ENE), number of metastatic lymph node (NMLN), pathological stage, and vascular tumor thrombus were correlated with RFS in the BRAF mutation-positive group. The mutation-stratified predictive models demonstrated better performance than the model without stratification, as indicated by the greater C-index values (0.880 vs. 0.859 vs. 0.753), AUC values (1-year AUC: 0.946 vs. 0.947 vs. 0.758; 3-year AUC: 0.889 vs. 0.871 vs. 0.760; 5-year AUC: 0.845 vs. 0.793 vs. 0.758), and net clinical benefit. The calibration curves at 1 year, 3 years, and 5 years showed good consistency. The bootstrap internal validation had good AUC values exceeding 0.8 and showed a well-fitting calibration curve. Significant differences in RFS were observed between the low-risk and high-risk groups (P < 0.001).

Conclusion: Stratifying patients based on their BRAF mutation status can facilitate the development of better and more targeted postoperative management strategies. Nomograms for BRAF mutation positive and negative patients were developed to precisely and consistently predict recurrence risk in PTC patients.