Evidence map›Paper›PMID 40821066›Full record

ArticleAmerican journal of translational research2025

Development of a carbon nanoparticle-guided nomogram for predicting lateral cervical lymph node metastasis in clinically node-negative papillary thyroid carcinoma.

Hui Qu, Pisong Li, Hongbo Qu, Xiaoyu Zhu, Zhongbin Han, Hongshen Chen

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Article in American journal of translational research, 2025. 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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5 · Who and what money

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6 authors.

Hui QuDepartment of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University Dalian 116001, Liaoning, The People's Republic of China.
Pisong LiDepartment of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University Dalian 116001, Liaoning, The People's Republic of China.
Hongbo QuDepartment of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University Dalian 116001, Liaoning, The People's Republic of China.
Xiaoyu ZhuDepartment of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University Dalian 116001, Liaoning, The People's Republic of China.
Zhongbin HanDepartment of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University Dalian 116001, Liaoning, The People's Republic of China.
Hongshen ChenDepartment of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University Dalian 116001, Liaoning, The People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop and validate a carbon nanoparticle-enhanced nomogram for predicting lateral lymph node (LLN) metastasis in patients with clinically node-negative (cN0) papillary thyroid carcinoma (PTC).

methodsA retrospective analysis was conducted on 421 cN0 PTC patients treated between 2014 and 2020. Patients were randomly divided into training (n=316) and internal validation (n=105) cohorts. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression analyses were performed to identify predictive factors from clinical, ultrasonographic, and carbon nanoparticle tracing data. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

resultsIndependent predictors identified included age (HR: 0.944, 95% CI: 0.908-0.982), tumor diameter ≥1 cm (HR: 0.221, 95% CI: 0.053-1.920), regular tumor morphology (HR: 0.090, 95% CI: 0.020-0.470), and the number of carbon nanoparticle-stained positive lateral lymph nodes (HR: 0.000, 95% CI: 0.000-0.231). The nomogram showed excellent discrimination, with an AUC of 0.911 in the training set and 0.916 in the validation set, and good calibration (Brier scores of 5.70 and 4.50, respectively). DCA confirmed the clinical utility of the model across a range of risk thresholds.

conclusionThis carbon nanoparticle-guided nomogram is a practical and highly accurate tool for intraoperative risk stratification of LLN metastasis in cN0 PTC patients. Integrating tracer-based lymph node assessment with conventional clinicopathological factors enhances predictive capability compared to existing methods, potentially reducing unnecessary neck dissections while ensuring appropriate management of high-risk cases. Multicenter validation and incorporation of molecular markers are important next steps toward clinical implementation.

Indexed as

independent predictorslateral cervical lymph node metastasisPapillary thyroid carcinomaprediction model

Identifiers

PMID40821066
PMCPMC12351581

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