ArticleBMC cancer2025
A nomogram based on conventional and contrast-enhanced ultrasound for predicting high-volume central lymph node metastasis in papillary thyroid carcinoma.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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8 authors.
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Abstract
backgroundIt is recommended that ≥ 5 pathological N1a central lymph node metastases required a second total thyroidectomy after unilateral thyroidectomy In papillary thyroid carcinoma (PTC) patients. We aimed to develop a preoperative nomogram integrating conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) to accurately predict high-volume central lymph node metastasis (HVCLNM) in PTC.
methodsPatients with pathologically confirmed PTC were enrolled in our study between May 2021 and May 2024. Overall, a total of 867 patients were enrolled, and they were split into training and validation datasets at random in a 7:3 ratio. A nomogram predicting HVCLNM probability was developed in the training dataset using conventional US and CEUS characteristics. Model performance was assessed using the area under the curve (AUC), calibration, and decision curve analysis (DCA).
resultsA total of 607 patients (mean age, 42 ± 11.8 years, M/F = 142/465) and 260 patients (mean age, 42 ± 11.4 years, M/F = 54/206) were enrolled in the training and validation datasets, respectively. LASSO logistic regression selected five imaging features with non-zero coefficients: size, multifocality, enhancement direction, peak intensity, and US-reported LN status. The nomogram incorporating these factors demonstrated strong predictive performance for HVCLNM, with mean AUCs of 0.9149 (95% CI: 0.8878-0.9419) in the training set and 0.8768 (95% CI: 0.8046-0.9527) in the validation set. Calibration and DCA for the HVCLNM nomogram also showed superior accuracy and therapeutic utility.
conclusionThis preoperative nomogram demonstrates high diagnostic accuracy and shows potential as a clinical decision-support tool for predicting HVCLNM.
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