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

IF 1.6 4区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
American journal of translational research Pub Date : 2025-07-25 eCollection Date: 2025-01-01 DOI:10.62347/JVGT3596
Hui Qu, Pisong Li, Hongbo Qu, Xiaoyu Zhu, Zhongbin Han, Hongshen Chen
{"title":"Development of a carbon nanoparticle-guided nomogram for predicting lateral cervical lymph node metastasis in clinically node-negative papillary thyroid carcinoma.","authors":"Hui Qu, Pisong Li, Hongbo Qu, Xiaoyu Zhu, Zhongbin Han, Hongshen Chen","doi":"10.62347/JVGT3596","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To 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).</p><p><strong>Methods: </strong>A 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).</p><p><strong>Results: </strong>Independent 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.</p><p><strong>Conclusion: </strong>This 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.</p>","PeriodicalId":7731,"journal":{"name":"American journal of translational research","volume":"17 7","pages":"5625-5640"},"PeriodicalIF":1.6000,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12351581/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American journal of translational research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.62347/JVGT3596","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

Objective: To 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).

Methods: A 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).

Results: Independent 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.

Conclusion: This 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.

碳纳米颗粒引导下预测临床淋巴结阴性甲状腺乳头状癌侧颈淋巴结转移的影像学研究进展。
目的:建立并验证碳纳米颗粒增强nomogram预测临床淋巴结阴性(cN0)甲状腺乳头状癌(PTC)患者侧淋巴结(LLN)转移的方法。方法:回顾性分析2014 ~ 2020年收治的421例cN0 PTC患者的临床资料。患者被随机分为训练组(n=316)和内部验证组(n=105)。最小绝对收缩和选择算子(LASSO)回归和Cox回归分析从临床、超声和碳纳米颗粒示踪数据中确定预测因素。采用受试者工作特征(ROC)曲线、校正图和决策曲线分析(DCA)评估模型的性能。结果:确定的独立预测因素包括年龄(HR: 0.944, 95% CI: 0.908-0.982)、肿瘤直径≥1 cm (HR: 0.221, 95% CI: 0.053-1.920)、肿瘤形态(HR: 0.090, 95% CI: 0.020-0.470)、碳纳米颗粒染色阳性侧淋巴结数量(HR: 0.000, 95% CI: 0.000-0.231)。模态图具有很好的判别性,训练集的AUC为0.911,验证集的AUC为0.916,校正效果良好(Brier评分分别为5.70和4.50)。DCA证实了该模型在一系列风险阈值范围内的临床实用性。结论:碳纳米颗粒引导的nomographic是cN0 PTC患者术中LLN转移风险分层的实用且高度准确的工具。与现有方法相比,将基于示踪剂的淋巴结评估与常规临床病理因素相结合可以提高预测能力,在确保对高危病例进行适当管理的同时,可能减少不必要的颈部清扫。多中心验证和分子标记的结合是下一步临床应用的重要步骤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
American journal of translational research
American journal of translational research ONCOLOGY-MEDICINE, RESEARCH & EXPERIMENTAL
自引率
0.00%
发文量
552
期刊介绍: Information not localized
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信