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Development and internal validation of a nomogram for predicting lymph node metastasis posterior to the right recurrent laryngeal nerve in papillary thyroid carcinoma: a single-center retrospective study

In brief

A thyroid cancer nomogram showed 0.94 discrimination for hidden node spread

In 320 patients with papillary thyroid cancer, 51 had metastasis in lymph nodes behind the right recurrent laryngeal nerve. A model using tumor features and lymph node status had an area under the curve of 0.94, but its strongest predictors came from final pathology, not preoperative imaging. External, prospective testing is needed to learn how well it works before surgery.

Journal
Frontiers in endocrinology (Q1)
Published
17 September 2026
Study design
Cohort / observational study
Evidence level
Level 3, Low (CEBM 3b)
Authors
Qiran Lu, Luxin Wei, Lifeng Zhang
PMID
42824089
DOI
10.3389/fendo.2026.1953516

Why clinicians should know about it

Abstract

BACKGROUND: The lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLN) lie deep in the right central compartment. Clearing them is technically demanding and adds to the risk of injury to the nerve and parathyroid glands, whereas occult metastases left behind may cause persistent or recurrent disease. Knowing in advance which patients are likely to have LN-prRLN metastasis would help tailor the extent of central neck dissection. METHODS: We retrospectively reviewed 320 patients with papillary thyroid carcinoma (PTC) treated at a single tertiary center between August 2021 and February 2026, all of whom underwent thyroidectomy with right central neck dissection that included separate LN-prRLN harvesting. Candidate clinicopathological variables were screened by univariable logistic regression; non-redundant binary variables with P<0.05 then entered a multivariable Firth penalized-likelihood model, used because a single event among the 166 central-node-negative patients biased the ordinary maximum-likelihood estimate. The independent predictors formed a nomogram, internally validated by 1,000 bootstrap resamples that repeated the entire model-building procedure, and was assessed for discrimination, calibration (including the optimism-corrected calibration slope and the Hosmer-Lemeshow test) and clinical utility (decision curve analysis, DCA). RESULTS: LN-prRLN metastasis was present in 51 of 320 patients (15.9%). Multifocality (odds ratio [OR] 4.46), tumor diameter >10 mm (OR 2.87), isthmic/near-isthmic location (OR 6.28), central lymph node metastasis (OR 49.15) and lateral lymph node metastasis (OR 13.90) were independent predictors (all P<0.05), whereas capsular invasion was not retained (P = 0.286). The resulting nomogram reached an area under the receiver operating characteristic curve of 0.936 (95% confidence interval 0.901-0.971; optimism-corrected 0.927), with a sensitivity of 0.843 and a specificity of 0.896 at the optimal cutoff of 0.226. Calibration curves lay close to the ideal line (optimism-corrected calibration slope 0.961, intercept -0.024; Hosmer-Lemeshow P = 0.285), and DCA showed a positive net benefit across the clinically plausible range of threshold probabilities. CONCLUSION: A nomogram combining multifocality, tumor size, isthmic location, and central and lateral compartment metastasis predicted LN-prRLN metastasis with high discrimination and acceptable internal calibration. Intended for use with preoperative, imaging-based assessment of nodal status, the nomogram requires external validation, ideally multicenter and prospective, before routine use. It should be emphasized, however, that central and lateral nodal status - the two strongest predictors - were coded from the final histopathological report, and because the sensitivity of preoperative ultrasound and contrast-enhanced computed tomography for central compartment metastasis is limited, the performance reported here is an upper bound on what can be expected when the nomogram is used with imaging-based inputs.

Abstract as published, via PubMed.

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For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.