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F-FDG PET/CT对弥漫性大B细胞淋巴瘤(DLBCL)患者不同治疗节点的治疗效果及预后价值评估

Evaluation of therapeutic effect and prognostic value of F-FDG PET/CT in different treatment nodes of DLBCL patients.

作者信息

Zhao Wenyu, Wu Xiaodong, Huang Shuo, Wang Hui, Fu Hongliang

机构信息

Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.

Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China.

出版信息

EJNMMI Res. 2024 Feb 19;14(1):20. doi: 10.1186/s13550-024-01074-w.

DOI:10.1186/s13550-024-01074-w
PMID:38372908
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10876506/
Abstract

BACKGROUND

In the present study, we aimed to investigate the role of baseline (B), interim (I) and end-of-treatment (Eot) F-FDG PET/CT in assessing the prognosis of diffuse large B cell lymphoma (DLBCL), so as to identify patients who need intensive treatment at an early stage.

METHODS

A total of 127 DLBCL patients (62 men; 65 women; median age 62 years) were retrospectively analyzed in this study. Baseline (n = 127), interim (n = 127, after 3-4 cycles) and end-of-treatment (n = 53, after 6-8 cycles) PET/CT images were re-evaluated; semi-quantitative parameters such as maximum standardized uptake value of lesion-to-liver ratio (SUVmax) and lesion-to-mediastinum ratio (SUVmax), total metabolic tumor volume (TMTV) and total metabolic tumor volume (TLG) were recorded. ΔTLG was the change of interim relative to baseline TLG (I to B), ΔTLG (Eot to B). ΔSUVmax and ΔTMTV were the same algorithm. The visual Deauville 5-point scale (D-5PS) has been adopted as the major criterion for PET evaluation. Visual analysis (VA) and semi-quantitative parameters were assessed for the ability to predict progression-free survival (PFS) and overall survival (OS) by using Kaplan-Meier method, cox regression and logistic regression analysis. When visual and semi-quantitative analysis are combined, the result is only positive if both are positive.

RESULTS

At a median follow-up of 34 months, the median PFS and OS were 20 and 32 months. The survival curve analysis showed that advanced stage and IPI score with poor prognosis, ΔSUVmax < 89.2%, ΔTMTV < 91.8% and ΔTLG < 98.8%, ΔSUVmax < 86.4% were significantly related to the shortening of PFS in patient (p < 0.05). ΔSUVmax < 83.2% and ΔTLG < 97.6% were significantly correlated with the shortening of OS in patients (p < 0.05). Visual analysis showed that incomplete metabolic remission at I-PET and Eot-PET increased the risk of progress and death. In terms of predicting recurrence by I-PET, the combination of visual and semi-quantitative parameters showed higher positive predictive value (PPV) and specificity than a single index.

CONCLUSION

Three to four cycles of R-CHOP treatment may be a time point for early prediction of early recurrence/refractory (R/R) patients and active preemptive treatment. Combined visual analysis with semi-quantitative parameters of F-FDG PET/CT at interim can improve prognostic accuracy and may allow for more precise screening of patients requiring early intensive therapy.

摘要

背景

在本研究中,我们旨在探讨基线(B)、中期(I)和治疗结束时(Eot)的F-FDG PET/CT在评估弥漫性大B细胞淋巴瘤(DLBCL)预后中的作用,以便识别需要早期强化治疗的患者。

方法

本研究回顾性分析了127例DLBCL患者(62例男性;65例女性;中位年龄62岁)。重新评估了基线(n = 127)、中期(n = 127,3 - 4个周期后)和治疗结束时(n = 53,6 - 8个周期后)的PET/CT图像;记录了半定量参数,如病变与肝脏比值的最大标准化摄取值(SUVmax)、病变与纵隔比值(SUVmax)、总代谢肿瘤体积(TMTV)和总代谢肿瘤负荷(TLG)。ΔTLG是中期相对于基线TLG(I相对于B)、ΔTLG(Eot相对于B)的变化。ΔSUVmax和ΔTMTV采用相同算法。视觉Deauville 5分法(D-5PS)已被用作PET评估的主要标准。采用Kaplan-Meier法、cox回归和逻辑回归分析评估视觉分析(VA)和半定量参数预测无进展生存期(PFS)和总生存期(OS)的能力。当视觉分析和半定量分析相结合时,只有两者均为阳性结果才为阳性。

结果

中位随访34个月时,中位PFS和OS分别为20个月和32个月。生存曲线分析表明,晚期和预后不良的国际预后指数(IPI)评分、ΔSUVmax < 89.2%、ΔTMTV < 91.8%和ΔTLG < 98.8%、ΔSUVmax < 86.4%与患者PFS缩短显著相关(p < 0.05)。ΔSUVmax < 83.2%和ΔTLG < 97.6%与患者OS缩短显著相关(p < 0.05)。视觉分析显示,I-PET和Eot-PET时代谢未完全缓解增加了进展和死亡风险。在通过I-PET预测复发方面,视觉和半定量参数相结合显示出比单一指标更高的阳性预测值(PPV)和特异性。

结论

三到四个周期的R-CHOP治疗可能是早期预测早期复发/难治(R/R)患者并进行积极抢先治疗的时间点。中期将视觉分析与F-FDG PET/CT的半定量参数相结合可提高预后准确性,并可能允许更精确地筛选需要早期强化治疗的患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f82c/10876506/d4aba3357bc5/13550_2024_1074_Fig6_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f82c/10876506/266678614370/13550_2024_1074_Fig3_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f82c/10876506/d4aba3357bc5/13550_2024_1074_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f82c/10876506/3b394bddf384/13550_2024_1074_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f82c/10876506/fe4e6c098666/13550_2024_1074_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f82c/10876506/266678614370/13550_2024_1074_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f82c/10876506/351ed7a8df19/13550_2024_1074_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f82c/10876506/33be55efaa3e/13550_2024_1074_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f82c/10876506/d4aba3357bc5/13550_2024_1074_Fig6_HTML.jpg

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