Personalized risk prediction of lateral lymph node metastasis in male papillary thyroid cancer: a sex-specific model leveraging central lymph node ratio
Year: 2025
Type: article
Study design: Observational Study
Abstract: Aims Notable sex-based disparities exist in papillary thyroid carcinoma (PTC), necessitating male-specific models for predicting lateral lymph node metastasis (LLNM) to guide treatment.Methods We developed and validated two nomograms, based on central lymph node ratio (LNR) and preoperative ultrasound (US), respectively, using data from 433 male PTC patients who underwent total thyroidectomy (TT) with central and lateral neck lymph node dissection. Multivariable analysis identified key risk factors. Model performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA) across training and validation sets.Results Male patients had 1.33-fold higher odds of central lymph node metastasis (CLNM) and 1.51-fold higher odds of LLNM than female patients (p < 0.05). Multivariable analysis identified multifocality, tumor size >20 mm, preoperative US nodal status, and an elevated LNR as independent risk factors for LLNM in males (p < 0.05). The LNR-based nomogram demonstrated superior predictive accuracy, with AUCs of 0.828 (training), 0.812 (internal validation), and 0.832 (external validation), outperforming the US-based model. Risk stratification using nomogram scores effectively categorized patients into three distinct groups with significantly different LLNM rates.Conclusions The nomogram based on the central LNR provides a precise and clinically valuable tool for the individualized prediction of LLNM in male PTC patients.
Source: Future Oncology
Authors: Xue Zhang, Yuan Qin, Niuniu Hou, Yunjiao Zhang, Changjiao Yan, Xi Chen, Chutuo Liu, Chong Chen, Rui Ling
Institutions: Xijing Hospital, Air Force Medical University
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Award: G3016127917
OpenAlex ID: W4415738241
