Multimodal ultrasonographic and clinicopathological model for predicting high-volume lymph node metastasis in cN0...

Given the challenge in preoperative diagnosis of high-volume lymph node metastasis (HVLNM) in clinical practice, we constructed and externally validated a comprehensive predictive model that integrated conventional ultrasound characteristics, contrast-enhanced ultrasound (CEUS) parameters, BRAF^V600Emutation, and clinicopathological data for HVLNM in clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC).

Totally, 126 clinically lymph node-negative (cN0) PTC patients who underwent subtotal or total thyroidectomy and accompanied with prophylactic cervical lymph node dissection between December 2022 and December 2024 were enrolled in this retrospective study, and an additional 47 cN0 PTC patients included for the external validation cohort. Univariate and multivariate analysis were performed to identify the independent risk factors for HVLNM, and a binary logistic regression equation and relevant nomogram was constructed to predict the risk about HVLNM. The model underwent internal validation using ten-fold cross-validation and further externally validated in an independent external cohort. Clinical practicality of the nomogram model was assessed by the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Age, Dmax, ACR scores ≥11 points, and heterogeneous enhancement were four independent predictors of HVLNM after univariate and multivariate analysis in training set. These predictors were used to construct the corresponding nomograms with AUC of 0.860(95% CI: 0.792-0.928). Calibration curves and DCA plots revealed their robust calibration performances and fine net benefits. The accuracy and Kappa value obtained through ten-fold cross-validation were 0.864 and 0.468. The ROC value of the external validation was 0.885(95% CI:0.792-0.978).

Our visualization nomogram provides clinicians with useful information in a simple and cost-effective manner, aiding in the formulation of personalized treatment plans and the reduction of reoperation rates.