Association between ultrasound radiomics features and central lymph node metastasis in patients with papillary thyroid carcinoma.
TL;DR
The combined model incorporating Rad-score, age stratification, maximum tumor diameter, and conventional ultrasound features effectively predicts CLNM risk in PTC patients, demonstrating good discrimination, calibration, and clinical net benefit.
Abstract
Objective
To develop and validate a predictive model combining ultrasound radiomics score (Rad-score) with clinical and conventional ultrasound features for preoperative assessment of central lymph node metastasis (CLNM) risk in papillary thyroid carcinoma (PTC).
Methods
This retrospective study included 500 PTC patients, randomly assigned (7:3) to training (n = 350) and validation (n = 150) sets. Radiomics features underwent repeatability validation, standardization, variance filtering, correlation-based redundancy removal, and LASSO logistic regression to construct the Rad-score. Four models were established: clinical/laboratory (Model A), conventional ultrasound (Model B), radiomics (Model C), and combined (Model D). Model performance was assessed using ROC curves, calibration curves, and decision curve analysis (DCA).
Results
Baseline analysis revealed that the CLNM group had a higher proportion of males (29.14% vs. 18.59%, P = 0.0286) and a significantly younger mean age (44.15±12.03 years vs. 49.25±11.80 years, P<0.001). Conventional ultrasonographic features demonstrated that the CLNM group presented higher rates of maximum tumor diameter >10 mm (57.62% vs. 31.66%, P<0.001), calcification (74.83% vs. 54.77%, P<0.001) and multifocality (51.66% vs. 35.68%, P = 0.0039). A total of 10 radiomic features were selected via the LASSO algorithm to establish the Rad-score. Univariate logistic regression analysis indicated that male gender, age ≥55 years, maximum tumor diameter >10 mm, calcification, multifocality and Rad-score were correlated with CLNM. Multivariate logistic regression analysis showed that age ≥55 years was associated with a reduced risk of CLNM (OR = 0.531, 95% CI: 0.311-0.907, P = 0.020). Tumor maximum diameter >10 mm (OR = 1.949, 95% CI: 1.192-3.187, P = 0.008) and Rad-score (OR = 35.074, 95% CI: 10.385-118.458, P<0.001) were correlated with an elevated risk of CLNM. Model performance evaluation showed that the areas under the curve (AUCs) of Model A, B, C and D were 0.632, 0.687, 0.757 and 0.764 in the training set, and 0.643, 0.702, 0.788 and 0.798 in the validation set, respectively.
Conclusion
The combined model incorporating Rad-score, age stratification, maximum tumor diameter, and conventional ultrasound features effectively predicts CLNM risk in PTC patients, demonstrating good discrimination, calibration, and clinical net benefit. It has potential value for preoperative risk stratification and individualized decision support.