Evidence map›Paper›PMID 42903142›Full record

ArticleFrontiers in oncology2026

High-volume cervical lymph node metastasis in papillary thyroid carcinoma: development and external validation of an interpretable prediction model.

Dehao Wang, Jinchao Wang, Zhen Wu, Zhaoqing Cui

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Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Dehao WangSchool of Medicine, Shandong First Medical University, Jinan, China.
Jinchao WangSchool of Medicine, Shandong First Medical University, Jinan, China.
Zhen WuDepartment of Thyroid, Breast and Hernia Surgery, Liaocheng People's Hospital Affiliated to Shandong First Medical University, Liaocheng, China.
Zhaoqing CuiDepartment of Thyroid, Breast and Hernia Surgery, Liaocheng People's Hospital Affiliated to Shandong First Medical University, Liaocheng, China.

Funding

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6 · The paper itself

Abstract

Background: The transportability of models for postoperative high-volume cervical nodal disease in papillary thyroid carcinoma (PTC) and the incremental predictive value of molecular variables remain uncertain. Methods: We developed models in the public Deyang cohort (521 patients; 90 events) and performed geographic external validation in the independent Liaocheng cohort (193 patients; 73 events). The outcome was >5 pathologically confirmed metastatic cervical lymph nodes. Five algorithms were compared using repeated stratified nested cross-validation with a prespecified 10-variable postoperative feature set. Ridge logistic regression (C = 0.1) was selected and applied without refitting. Paired comparisons of 8-variable clinicopathological and 10-variable clinicopathological-molecular models assessed the incremental value of high-risk co-mutation status and mutation count in both cohorts. Results: Internal validation yielded an area under the receiver operating characteristic curve (AUROC) of 0.795 (95% CI, 0.745-0.842), average precision (AP) of 0.497 (0.406-0.598), and Brier score of 0.115 (0.098-0.134). External AUROC was 0.752 (0.678-0.821), AP 0.601 (0.498-0.728), and Brier score 0.217 (0.180-0.254). Observed and mean predicted risks were 37.8% and 23.9%, respectively; the observed-to-expected ratio was 1.582 (1.319-1.860) and the weak-calibration slope was 0.831 (0.542-1.191). At the prespecified threshold of 0.158, sensitivity was 0.795 and specificity 0.600. Net benefit was 0.254 for the model and 0.261 for classifying all patients as high risk (difference, -0.008; 95% CI, -0.051 to 0.033). External paired differences after adding molecular variables were +0.002 for AUROC (-0.005 to 0.010), -0.002 for AP (-0.016 to 0.010), -0.001 for Brier score (-0.002 to 0.000), and +0.009 for net benefit (-0.003 to 0.026). Conclusion: The model retained moderate discrimination across centers but underestimated absolute risk and did not demonstrate a net benefit advantage over classifying all patients as high risk at the prespecified threshold. High-risk co-mutation status and mutation count showed no clear incremental predictive value. Further validation with standardized lymph-node assessment and patient outcomes is required before clinical application.

Indexed as

calibrationcervical lymph-node metastasisexternal validationmachine learningmolecular biomarkerspapillary thyroid carcinomaSHAP

Identifiers

PMID42903142
PMCPMC13680251

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.