Suppr超能文献

建立并验证一个列线图模型以预测甲状腺乳头状癌患者结构不完全缓解:一项回顾性研究。

Establishment and validation of a nomogram to predict structural incomplete response in papillary thyroid carcinoma patients: a retrospective study.

机构信息

Department of Ultrasound, Qilu Hospital of Shandong University (Qingdao), Qingdao, Shandong Province, China.

Department of Gynecology, Qingdao Women and Children's Hospital, Qingdao University, Qingdao, Shandong Province, China.

出版信息

J Int Med Res. 2023 Jan;51(1):3000605221149880. doi: 10.1177/03000605221149880.

Abstract

OBJECTIVE

To identify risk factors related to structural incomplete response (SIR) in papillary thyroid carcinoma (PTC) and develop a nomogram for PTC patients.

METHODS

In this respective study, clinical, ultrasonic, and pathological data of PTC patients treated at our institute between 2016 and 2020 were analyzed. Patients were randomly split into training and validation sets at a ratio of 7:3. Multivariate Cox regression analysis was conducted to determine independent prognostic factors. On the basis of these factors, a nomogram was built to predict SIR. value, concordance index, calibration plots and decision curve analysis were used to evaluate the model.

RESULTS

Multivariate Cox regression analysis showed that BRAF V600E status, lymph node metastasis, sex, tumor size, margin, and surgical procedure were independent prognostic factors. In the validation set, the concordance index of the nomogram was 0.774 (95% confidence interval: 0.703-0.845). Calibration plots at 3 and 5 years showed no apparent difference between predicted SIR probability and the actual SIR proportion. Additionally, the nomogram had good net clinical benefit according to the decision curve analysis compared with cases that were treat-all or treat-none.

CONCLUSION

We build a nomogram to predict individualized outcomes and help postoperative surveillance in PTC patients.

摘要

目的

确定与甲状腺乳头状癌(PTC)结构性不完全缓解(SIR)相关的风险因素,并为 PTC 患者开发一个列线图。

方法

在这项研究中,分析了 2016 年至 2020 年在我院治疗的 PTC 患者的临床、超声和病理数据。患者被随机分为训练集和验证集,比例为 7:3。多变量 Cox 回归分析用于确定独立的预后因素。基于这些因素,建立了一个预测 SIR 的列线图。 值、一致性指数、校准图和决策曲线分析用于评估模型。

结果

多变量 Cox 回归分析显示,BRAF V600E 状态、淋巴结转移、性别、肿瘤大小、切缘和手术方式是独立的预后因素。在验证集中,列线图的一致性指数为 0.774(95%置信区间:0.703-0.845)。3 年和 5 年的校准图显示,预测的 SIR 概率与实际 SIR 比例之间没有明显差异。此外,与治疗所有或不治疗所有病例相比,根据决策曲线分析,该列线图具有较好的净临床获益。

结论

我们构建了一个列线图来预测 PTC 患者的个体化结局,并有助于术后监测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ce1/9893078/bd90b8e9f94b/10.1177_03000605221149880-fig1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验