Huan Liang, Weilin Tan, Jie Wang, Mengdan Li, Hua Pang, Xiaohui Wang, Lu Yang, Xingguo Jing
{"title":"结合 PET/CT 代谢参数、炎症标记物和 TNM 分期的新型预测模型:鼻咽癌个性化预后的前景。","authors":"Huan Liang, Weilin Tan, Jie Wang, Mengdan Li, Hua Pang, Xiaohui Wang, Lu Yang, Xingguo Jing","doi":"10.1007/s12149-024-01949-x","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>This study aims to develop a novel prediction model and risk stratification system that could accurately predict progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC).</p><h3>Methods</h3><p>Herein, we included 106 individuals diagnosed with NPC, who underwent <sup>18</sup>F-FDG PET/CT scanning before treatment. They were divided into training (<i>n</i> = 76) and validation (<i>n</i> = 30) sets. The prediction model was constructed based on multivariate Cox regression analysis results and its predictive performance was evaluated. Risk factor stratification was performed based on the nomogram scores of each case, and Kaplan–Meier curves were used to evaluate the model’s discriminative ability for high- and low-risk groups.</p><h3>Results</h3><p>Multivariate Cox regression analysis showed that N stage, M stage, SUV<sub>max</sub>, MTV, HI, and SIRI were independent factors affecting the prognosis of patients with NPC. In the training set, the model considerably outperformed the TNM stage in predicting PFS (AUCs of 0.931 vs. 0.841, 0.892 vs. 0.785, and 0.892 vs. 0.804 at 1–3 years, respectively). The calibration plots showed good agreement between actual observations and model predictions. The DCA curves further justified the effectiveness of the model in clinical practice. Between high- and low-risk group, 3-year PFS rates were significantly different (high- vs. low-risk group: 62.8% vs. 9.8%, <i>p</i> < 0.001). Adjuvant chemotherapy was also effective for prolonging survival in high-risk patients (<i>p</i> = 0.009).</p><h3>Conclusion</h3><p>Herein, a novel prediction model was successfully developed and validated to improve the accuracy of prognostic prediction for patients with NPC, with the aim of facilitating personalized treatment.</p></div>","PeriodicalId":8007,"journal":{"name":"Annals of Nuclear Medicine","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Novel prediction model combining PET/CT metabolic parameters, inflammation markers, and TNM stage: prospects for personalizing prognosis in nasopharyngeal carcinoma\",\"authors\":\"Huan Liang, Weilin Tan, Jie Wang, Mengdan Li, Hua Pang, Xiaohui Wang, Lu Yang, Xingguo Jing\",\"doi\":\"10.1007/s12149-024-01949-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>This study aims to develop a novel prediction model and risk stratification system that could accurately predict progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC).</p><h3>Methods</h3><p>Herein, we included 106 individuals diagnosed with NPC, who underwent <sup>18</sup>F-FDG PET/CT scanning before treatment. 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引用次数: 0
摘要
目的:本研究旨在开发一种新型预测模型和风险分层系统,以准确预测鼻咽癌(NPC)患者的无进展生存期(PFS)。方法:本研究纳入了 106 名确诊为鼻咽癌的患者,他们在治疗前接受了 18F-FDG PET/CT 扫描。他们被分为训练集(76 人)和验证集(30 人)。根据多元 Cox 回归分析结果构建预测模型,并评估其预测性能。根据每个病例的提名图评分对风险因素进行分层,并利用 Kaplan-Meier 曲线评估模型对高风险组和低风险组的区分能力:多变量 Cox 回归分析显示,N 分期、M 分期、SUVmax、MTV、HI 和 SIRI 是影响鼻咽癌患者预后的独立因素。在训练集中,该模型在预测PFS方面大大优于TNM分期(1-3年的AUC分别为0.931 vs. 0.841、0.892 vs. 0.785和0.892 vs. 0.804)。校准图显示,实际观测结果与模型预测结果之间存在良好的一致性。DCA曲线进一步证明了该模型在临床实践中的有效性。高危组和低危组的 3 年生存率有显著差异(高危组 62.8% 对低危组 9.8%):62.8% vs. 9.8%,P 结论:这是一个新的预测模型:本文成功开发并验证了一种新型预测模型,以提高鼻咽癌患者预后预测的准确性,从而促进个性化治疗。
Novel prediction model combining PET/CT metabolic parameters, inflammation markers, and TNM stage: prospects for personalizing prognosis in nasopharyngeal carcinoma
Purpose
This study aims to develop a novel prediction model and risk stratification system that could accurately predict progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC).
Methods
Herein, we included 106 individuals diagnosed with NPC, who underwent 18F-FDG PET/CT scanning before treatment. They were divided into training (n = 76) and validation (n = 30) sets. The prediction model was constructed based on multivariate Cox regression analysis results and its predictive performance was evaluated. Risk factor stratification was performed based on the nomogram scores of each case, and Kaplan–Meier curves were used to evaluate the model’s discriminative ability for high- and low-risk groups.
Results
Multivariate Cox regression analysis showed that N stage, M stage, SUVmax, MTV, HI, and SIRI were independent factors affecting the prognosis of patients with NPC. In the training set, the model considerably outperformed the TNM stage in predicting PFS (AUCs of 0.931 vs. 0.841, 0.892 vs. 0.785, and 0.892 vs. 0.804 at 1–3 years, respectively). The calibration plots showed good agreement between actual observations and model predictions. The DCA curves further justified the effectiveness of the model in clinical practice. Between high- and low-risk group, 3-year PFS rates were significantly different (high- vs. low-risk group: 62.8% vs. 9.8%, p < 0.001). Adjuvant chemotherapy was also effective for prolonging survival in high-risk patients (p = 0.009).
Conclusion
Herein, a novel prediction model was successfully developed and validated to improve the accuracy of prognostic prediction for patients with NPC, with the aim of facilitating personalized treatment.
期刊介绍:
Annals of Nuclear Medicine is an official journal of the Japanese Society of Nuclear Medicine. It develops the appropriate application of radioactive substances and stable nuclides in the field of medicine.
The journal promotes the exchange of ideas and information and research in nuclear medicine and includes the medical application of radionuclides and related subjects. It presents original articles, short communications, reviews and letters to the editor.