基于18F-FDG PET/CT代谢参数的递归分割分析模型用于PD-1抑制剂联合化疗治疗的复发或转移性鼻咽癌
Recursive partitioning analysis model based on 18F-FDG PET/CT metabolic parameters for recurrent or metastatic nasopharyngeal carcinoma treated with PD-1 inhibitors and chemotherapy.
文献信息
| PMID | 42972443 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Huan Zhang |
| 作者单位 | Radiation Oncology Center, School of Medicine, Chongqing University Cancer Hospital, Chongqing University, Chongqing, 400030, China. |
| 期刊 | Cancer imaging : the official publication of the International Cancer Imaging Society |
| SCI 分区 | Q1 |
| IF | 4.8 |
| 研究类型 | AI/ML · 临床 |
| 所属专科 | 鼻咽癌 |
中文摘要
背景： 我们旨在评估基线18F-FDG PET/CT代谢参数的预后价值，并为接受一线PD-1抑制剂联合化疗的复发/转移性鼻咽癌（rmNPC）患者构建风险分层模型。
方法： 共纳入来自两个中心的148例rmNPC患者。评估全身总病灶糖酵解（wTLG）、全身代谢肿瘤体积（wMTV）和最大标准化摄取值（SUVmax）。通过受试者工作特征（ROC）曲线评估预测性能，同时使用Kaplan-Meier和Cox回归分析确定无进展生存期（PFS）和总生存期（OS）。随后开发并验证递归分割分析（RPA）模型用于风险分层。
结果： 较低的基线wTLG和wMTV与更好的缓解以及更长的PFS和OS相关，其中wTLG显示出最高的预测能力（AUC = 0.776）。RPA模型将患者分为低风险（低wTLG）、中风险（高wTLG无肝转移）和高风险（高wTLG伴肝转移）组，显示中位PFS（30.7、15.9、7.1个月）和OS（43.7、29.9、14.7个月；均p < 0.001）存在显著差异，预测3年疾病进展的AUC达到0.839。放疗改善了低风险组的生存结局，但未改善高风险组。对213个单个病灶的分析揭示了异质性缓解，其中淋巴结转移缓解最佳，肝转移缓解最差。
结论： 基线PET/CT代谢参数，尤其是wTLG，可作为接受化疗免疫治疗的NPC患者生存的有力预测因子。基于wTLG的RPA模型为风险分层提供了稳健工具，并有望指导个体化临床决策。
英文摘要
BACKGROUND: We aimed to evaluate the prognostic value of baseline 18F-FDG PET/CT metabolic parameters and to construct a risk stratification model for recurrent/metastatic nasopharyngeal carcinoma (rmNPC) patients receiving first-line PD-1 inhibitors plus chemotherapy.
METHODS: A total of 148 rmNPC patients from two centers were included. Whole-body total lesion glycolysis (wTLG), whole-body metabolic tumor volume (wMTV), and maximal standardized uptake values (SUVmax) were assessed. Predictive performance was evaluated via receiver operating characteristic (ROC) curve, while progression-free survival (PFS) and overall survival (OS) were identified using Kaplan-Meier and Cox regression analyses. A recursive partitioning analysis (RPA) model was subsequently developed and validated for risk stratification.
RESULTS: Lower baseline wTLG and wMTV were associated with better response and longer PFS and OS, with wTLG showing the highest predictive power (AUC = 0.776). The RPA model stratified patients into low-risk (low wTLG), medium-risk (high wTLG without liver metastasis), and high-risk (high wTLG with liver metastasis) groups, demonstrating significantly different median PFS (30.7, 15.9, 7.1 months) and OS (43.7, 29.9, 14.7 months; all p < 0.001), and achieving an AUC of 0.839 for predicting 3-year disease progression. Radiotherapy improved survival outcomes in the low-risk group but not in the high-risk group. Analysis of 213 individual lesions revealed heterogeneous responses, with lymph node metastases showing the best response and liver metastases the poorest.
CONCLUSION: Baseline PET/CT metabolic parameters, particularly wTLG, serve as powerful predictors of survival in NPC patients receiving chemoimmunotherapy. The wTLG-based RPA model provides a robust tool for risk stratification and holds promise for guiding individualized clinical decision-making.