基线18 F-FDG PET/CT代谢参数与PINK模型提高了P-GEMOX化疗治疗结外NK/ t细胞淋巴瘤治疗结果的预测。

IF 2.3 3区 医学 Q2 HEMATOLOGY Annals of Hematology Pub Date : 2025-02-11 DOI:10.1007/s00277-025-06243-y
Cheng Huang, Yi Li, Haixia He, Yan Gao, Xu Zhang, Bing Bai, Liqin Ping, Yanxia He, Shoumin Bai, Xiaoxiao Wang, Huiqiang Huang
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Kaplan–Meier curves with log-rank tests were used for survival analysis. At a median follow-up of 49 months, the 3-year PFS and OS were 62.9% and 70.1%. SUVmean, SUVmax, and SUVpeak were related to both PFS and OS in univariate analysis(<i>P</i> &lt; 0.05 for all). Further multivariate analysis including PET/CT parameters and clinical parameters revealed that SUVmean was an independent prognostic factor and seemed to be slightly superior to SUVmax and SUVpeak. The low SUVmean was significantly associated with a better prognosis (3-year OS 85.1% vs.65.0%, <i>P</i> = 0.014; 3-year PFS 76.8% vs.62.1%, <i>P</i> = 0.032). SUVmean was able to further separate patients with a low-risk PINK/PINKE of &lt; 2(<i>n</i> = 85, 79, separately) into two subgroups with significantly different outcomes. Moreover, the metabolic-parameter-contained m-PINK/PINKE model was constructed and showed superior predictive performance in the whole cohort. Conclusions. 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引用次数: 0

摘要

本研究的目的是探讨基线PET/CT参数单独和结合P-GEMOX方案(pegaspargase,吉西他滨和奥沙利铂)治疗的结外自然杀伤/ t细胞淋巴瘤(ENKTL)患者的预后价值。对97例患者进行回顾性评估。采用Cox回归分析检验基线PETCT代谢参数与生存率的关系,并采用受试者工作特征(ROC)曲线分析评价这些参数的最佳截断值。生存分析采用Kaplan-Meier曲线和log-rank检验。在中位随访49个月时,3年PFS和OS分别为62.9%和70.1%。单因素分析显示,SUVmean、SUVmax和SUVpeak与PFS和OS均相关
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Metabolic parameter of baseline 18 F-FDG PET/CT with PINK models improve the prediction of treatment outcome in extranodal NK/T-cell lymphoma treated with P-GEMOX chemotherapy

The aim of this study was to investigate the prognostic value of baseline PET/CT parameters alone and combined with clinical features in Extranodal Natural killer/T-cell lymphoma (ENKTL) patients treated with P-GEMOX regimen (pegaspargase, gemcitabine and oxaliplatin). A total of 97 patients were retrospectively evaluated. The relationships between baseline PETCT metabolic parameters and survival were tested using Cox regression analysis and receiver operating characteristic(ROC) curve analysis was employed to evaluate the optimal cut-off value of these parameters. Kaplan–Meier curves with log-rank tests were used for survival analysis. At a median follow-up of 49 months, the 3-year PFS and OS were 62.9% and 70.1%. SUVmean, SUVmax, and SUVpeak were related to both PFS and OS in univariate analysis(P < 0.05 for all). Further multivariate analysis including PET/CT parameters and clinical parameters revealed that SUVmean was an independent prognostic factor and seemed to be slightly superior to SUVmax and SUVpeak. The low SUVmean was significantly associated with a better prognosis (3-year OS 85.1% vs.65.0%, P = 0.014; 3-year PFS 76.8% vs.62.1%, P = 0.032). SUVmean was able to further separate patients with a low-risk PINK/PINKE of < 2(n = 85, 79, separately) into two subgroups with significantly different outcomes. Moreover, the metabolic-parameter-contained m-PINK/PINKE model was constructed and showed superior predictive performance in the whole cohort. Conclusions. SUVmean was an independent prognostic factor in patients with ENKTL treated with P-GEMOX chemotherapy. Adding SUVmean to the PINK or PINKE model could improve the predictive value and further distinguish patients with poor outcomes.

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来源期刊
Annals of Hematology
Annals of Hematology 医学-血液学
CiteScore
5.60
自引率
2.90%
发文量
304
审稿时长
2 months
期刊介绍: Annals of Hematology covers the whole spectrum of clinical and experimental hematology, hemostaseology, blood transfusion, and related aspects of medical oncology, including diagnosis and treatment of leukemias, lymphatic neoplasias and solid tumors, and transplantation of hematopoietic stem cells. Coverage includes general aspects of oncology, molecular biology and immunology as pertinent to problems of human blood disease. The journal is associated with the German Society for Hematology and Medical Oncology, and the Austrian Society for Hematology and Oncology.
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