A preoperative nomogram for predicting ATA risk stratification in papillary thyroid carcinoma: development and internal validation

This study aimed to develop and internally validate a nomogram prediction model based on preoperative clinical indicators, designed to identify patients with papillary thyroid carcinoma (PTC) who fall into the intermediate- and high-risk categories of the 2015 American Thyroid Association (ATA) initial recurrence risk stratification system following surgery, thereby providing a reference for preoperative risk assessment. Relevant clinical data were collected from 404 patients with PTC who underwent surgical treatment at the institution between June 2021 and October 2024. Univariate and multivariate logistic regression analyses were conducted to identify factors associated with the postoperative ATA intermediate-to-high risk category, and a combined model was established along with a nomogram. The performance of the combined model was evaluated by the receiver operating characteristic (ROC) curve, the Hosmer-Lemeshow goodness-of-fit test, calibration curve, and decision curve analysis (DCA). Additionally, a baseline model was constructed to compare its effectiveness with that of the combined model using the DeLong test. This study comprised 404 PTC patients, and the cohort was randomly partitioned into a training set ( n = 284) and a validation set ( n = 120). Results from univariate and multivariate logistic regression analysis in the training set showed that age, ipsilateral nodular goiter (NG), multifocality, maximum diameter, and thyroglobulin (Tg) were identified as predictive factors for the postoperative ATA intermediate-to-high risk category in PTC. Good fitting and calibration were demonstrated by the combined model, with area under the curve (AUC) values of 0.795 and 0.777 observed in the training and validation sets, respectively. DCA revealed a net positive benefit of the combined model within a broad range of risk thresholds. Although the combined model had numerically higher AUC values than the baseline model in both the training and validation sets, the differences were not statistically significant. The nomogram based on preoperative clinical indicators shows good predictive performance for the postoperative ATA intermediate-to-high risk category, with the potential to preoperatively identify patients at this risk level and assist in clinical decision-making. Nevertheless, its clinical utility warrants further confirmation through multicenter external validation with large sample sizes.