Establishment and Validation of a Risk Prediction Model for Early Postoperative Distant Metastasis in Patients With...

This study aimed to investigate risk factors for early postoperative distant metastasis in patients with medullary thyroid carcinoma (MTC) and to establish a risk prediction model.

A total of 263 patients diagnosed with MTC after initial surgery at Zhejiang Cancer Hospital between March 2015 and August 2023 were included. The patients were divided into metastasis group (n = 75) and non-metastasis group (n = 188) based on the presence of distant tumor metastasis at 3 months postoperatively. Clinical data, including demographic information, laboratory results, and ultrasound findings, were collected for both groups. The collected data were then randomly assigned into a training set (n = 187) and a validation set (n = 76) at a ratio of 7:3. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for early postoperative distant metastasis. Also, the stepwise backward method was used to determine the predictors of early postoperative distant metastasis, which were utilized for developing a nomogram. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves were adopted to evaluate the performance and predictive value of the model developed.

Multivariate logistic regression analysis revealed that preoperative calcitonin levels and dissection approach were independent factors associated with postoperative distant metastasis. Tumor diameter and number of lesions also showed trends associated with distant metastasis and were therefore included in the predictive model. The final predictors we used to construct the model were age, preoperative carcinoembryonic antigen (CEA), preoperative calcitonin, tumor diameter, number of lesions, and lymph node dissection method. The model demonstrated superior predictive performance, with an area under the curve (AUC) of 0.823 for the training set and 0.763 for the validation set. Calibration curves confirmed good agreement between predicted and observed probabilities. Results from DCA further supported the model's ability to effectively identify individuals at high risk of postoperative distant metastasis on both training and validation sets.

Incorporating readily available clinical variables, the risk prediction model for early postoperative distant metastasis in MTC demonstrated robust discriminatory ability and calibration. Further large-scale prospective studies with external validation are warranted to evaluate the clinical applicability and utility of this model in surgical decision-making.