To investigate the predictive value of a nomogram for central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC) based on CT features combined with clinical factors.

A case-control study was conducted on 256 pathologically confirmed PTMC patients from 2 tertiary hospitals from January 2022 to November 2024. All participants underwent contrast-enhanced neck CT scanning within 2 weeks before surgery and received central lymph node dissection. The 201 patients from the First Affiliated Hospital of Army Medical University were randomized into a training set (n=140) and an internal validation set (n=61) in a 7∶3 ratio. The 55 patients from the Second Affiliated Hospital of Chongqing Medical University were all assigned into an external validation set. Their clinical data and CT features were collected. Univariate and multivariate logistic regression analyses were employed to identify independent predictive factors for CLNM, and then a nomogram model was constructed. Receiver operating characteristic (ROC) curve analysis (area under the curve, AUC), calibration curve, and decision curve analysis (DCA) were performed to evaluate the model performance, discrimination and clinical utility.

Multivariate logistic regression analysis identified 4 independent CLNM predictors (P<0.05), that is, male (OR=5.991, 95%CI: 2.209~18.350), tumor size ≥0.82 cm (OR=18.880, 95%CI: 1.803~229.500), capsular involvement (OR=9.805, 95%CI: 4.015~26.340), and CT-diagnosed lymph node positivity (OR=2.872; 95%CI: 1.176~7.232). The nomogram achieved an AUC value of 0.859 (95%CI: 0.796~0.922), 0.786 (95%CI: 0.671~0.901), and 0.783 (95%CI: 0.648~0.917) in the training and internal and external validation sets, respectively. Calibration curves demonstrated high consistency between predicted and observed probabilities (Hosmer-Lemeshow P>0.05). DCA confirmed net clinical benefits for CLNM before surgical treatment with a threshold probability range of 0.18~0.80.

Based on sex, tumor size, capsular involvement, and CT-diagnosed lymph node metastasis, our nomogram model effectively predicts CLNM risk in PTMC patients. It can be used as a quantitative tool for personalized surgical planning and shows high clinical applicability.