This study aimed to develop and apply a prediction model to estimate the probability of lateral lymph node metastasis (LLNM) in patients with cN0 unilateral papillary thyroid carcinoma (PTC) with central lymph node metastasis (CLNM).
All study data were collected from a single tertiary hospital.
Univariable and multivariable logistic regression analyses were used to explore independent predictors of LLNM in the derivation and internal validation cohorts, which were used to construct and validate a nomogram. Another 96 patients were included prospectively to evaluate the efficacy of this nomogram.
Maximum tumor diameter greater than 1.0 cm (OR, 2.712; 95% CI, 1.412–5.210), multifocality (OR, 2.758; 95% CI, 1.120–6.789), the number of CLNM ≥3 (OR, 2.579; 95% CI, 1.315–5.789), CLNM ratio ≥0.297 (OR, 2.905; 95% CI, 1.396–6.043), and tumors located in the upper portion (OR 2.846, 95% CI 1.151–7.039) were independent predictors associated with LLNM. The prediction model showed excellent discrimination with an AUC of 0.731 (95% CI, 0.635–0.827). Novel risk stratification for LLNM was constructed based on this nomogram. In the prospective cohort, we stratified these patients into three risk subgroups: low-, moderate-, and high-risk subgroups and we found that the probability of LLNM was positively correlated with the total points from the nomogram.
This nomogram was applied in prospective clinical practice and distinguished PTC patients with a genuinely high risk of LLNM. Surgeons can use our nomogram to tailor the surgical plan and to credibly determine further postoperative therapy.