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治疗前血管免疫母细胞性T细胞淋巴瘤^(18)F-FDG PET/CT中脾脏SUV_(max)的预后价值 被引量:1

Prognostic value of spleen SUV MAX in pretreatment 18 F-FDG PET/CT imaging of angioimmunoblastic T-cell lymphoma
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摘要 目的探讨血管免疫母细胞性T细胞淋巴瘤(AITL)患者治疗前^(18)F-脱氧葡萄糖(^(18)F-FDG)正电子发射计算机断层显像(PET/CT)中脾脏最大标准摄取值(SUV_(max))的预后价值。方法回顾性分析2009年9月至2019年9月60例AITL患者治疗前临床病理及^(18)F-FDG PET/CT影像学资料。测量患者病灶SUV_(max)、脾脏SUV_(max)、脾脏SUV_(max)/肝SUV_(max)(脾肝比)和脾脏垂直径。采用受试者工作特征(ROC)曲线计算病灶SUV_(max)和脾脏SUV_(max)的最佳预后临界值。以脾脏垂直径>13 cm或脾肝比>1为标准判读为影像学上脾脏受累。影响预后的多因素分析采用Cox比例风险回归模型。结果60例AITL患者SUV_(max)、脾脏垂直径、脾肝比、脾脏SUV_(max)分别为16.4(10.1,21.2)、13.6(12.0,16.0)cm、1.6±0.08、5.6(4.0,7.0)。全组1、3年无进展生存率分别为47.8%、31.2%,1、3年生存率分别为68.3%、45.8%。利用ROC曲线获得脾脏SUV_(max)的AUC=0.665(95%CI:0.526~0.803,P=0.029),其最佳临界值为6.0时,灵敏度为58.1%,特异度为75.9%。病灶SUV_(max)的ROC曲线显示,AUC=0.506(95%CI:0.356~0.655,P=0.941)。高脾脏SUV_(max)患者伴有较高的Ann Arbor分期、ECOG评分、PIT、脾脏垂直径以及较低的PLT和白蛋白水平。进展组患者脾脏SUV_(max)为5.9(4.1,7.1),高于无进展组的4.8(3.7,6.3),但差异无统计学意义(P=0.162)。死亡组患者脾脏SUV_(max)为6.3(4.7,7.8),高于存活组的4.9(3.6,6.1),差异有统计学意义(P=0.029)。单因素分析显示,ECOG评分、PIT、PLT计数、脾脏垂直径、脾肝比、脾脏受累、脾脏SUV_(max)与AITL患者的无进展生存期(PFS)和总生存期(OS)有关(P<0.05)。Cox多因素分析显示,脾脏SUV_(max)可作为影响AITL患者PFS和OS的独立因素(P<0.05)。结论AITL常累及脾脏,^(18)F-FDG PET/CT显像中脾脏SUV_(max)可作为影响AITL患者PFS和OS的独立因素,有助于预测患者的预后。 Objective To investigate the prognostic value of the maximum standardized up-take value(SUV_(max))of spleen in pretreatment^(18)F-deoxyglucose(^(18)F-FDG)positrone mission tomography/computed tomography(PET/CT)imaging in patients with angioimmunoblastic T-cell lymphoma(AITL).Methods A retrospective analysis of the pretreatment clinical data and^(18)F-FDG PET/CT imaging characteristics of 60 AITL patients from Sep 2009 to Sep 2019 was done.The SUV_(max),splenic SUV_(max)(sSUV_(max)),spleen to liver ratio(SLR)and spleen vertical diameter(SVD)were analyzed.The receiver operating characteristic(ROC)curve was applied to obtain the prognostic cut-off value of SUV_(max)and sSUV_(max).The vertical diameter of spleen>13 cm or the SLR>1 was used as the standard to judge the involvement of spleen in imaging.Cox proportional hazards regression model was used for multivariate analysis.Results In 60 AITL patients,the SUV_(max),SVD,SLR,sSUV_(max)were 16.4(10.1,21.2),13.6(12.0,16.0)cm,1.6±0.08,5.6(4.0,7.0),respectively.The 1-and 3-year progression free survival rates were 47.8%and 31.2%respectively,and the 1-and 3-year survival rates were 68.3%and 45.8%respectively.AUC=0.665(95%CI:0.526-0.803,P=0.029)of sSUV_(max)was obtained by ROC curve.When the optimal critical value was 6.0,the sensitivity was 58.1%and the specificity was 75.9%.The ROC curve of SUV_(max)showed that AUC=0.506(95%CI:0.356-0.655,P=0.941).Patients with high sSUV_(max)were associated with higher Ann Arbor stage,ECOG score,PIT,SVD and lower PLT and albumin levels.The sSUV_(max)in the progression group was 5.9(4.1,7.1),higher than 4.8(3.7,6.3)in the non-progression group,but the difference was not statistically significant(P=0.162).The sSUV_(max)in the death group was 6.3(4.7,7.8),higher than 4.9(3.6,6.1)in the survival group,and the difference was statistically significant(P=0.029).Univariate analysis showed that the SVD,SLR,spleen involvement,PIT,PLT level,ECOG score and sSUV_(max)were related to the progressive survival(PFS)and overall survival(OS)of AITL patients(P<0.05).Cox multivariate analysis showed that sSUV_(max)could be used as an independent factor affecting PFS and OS in AITL patients(P<0.05).Conclusion AITL often involves the spleen,sSUV_(max)in^(18)F-FDG PET/CT imaging may be applied as an independent adverse prognostic factor for PFS and OS in AITL patients,which may help to evaluate the prognosis of AITL patients.
作者 巩环宇 丁重阳 胡瑶 唐立钧 GONG Huanyu;DING Chongyang;HU Yao;TANG Lijun(Department of Nuclear Medicine,the First Affiliated Hospital of Nanjing Medical University,Nanjing 210029,China)
出处 《临床肿瘤学杂志》 CAS 2021年第11期1004-1010,共7页 Chinese Clinical Oncology
基金 江苏省医学重点人才基金资助项目(ZDRCB2016003)。
关键词 血管免疫母细胞性T细胞淋巴瘤 脾脏 预后 正电子发射计算机断层扫描 脱氧葡萄糖 Angioimmunoblastic T-cell lymphoma Spleen Prognosis Positron emission computed tomography Deoxyglucose
作者简介 通讯作者:唐立钧,E-mail:tanglijun@njmu.edu.cn。
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