18F-FDG PET/CT在肝脏良恶性肿瘤鉴别诊断中的应用价值
Application Value of 18F-FDG PET/CT in the Differential Diagnosis of Benign and Malignant Liver Tumors
罗晓东 1董军强2
作者信息
- 1. 河南天佑中西医结合肿瘤医院核医学科,河南郑州 451162
- 2. 郑州大学第一附属医院核医学科,河南郑州 450052
- 折叠
摘要
目的 探讨 18F-FDG PET/CT在肝脏良恶性肿瘤鉴别诊断中的应用价值.方法 90 例肝脏占位患者根据病理检查结果分为良性组(53 例)和恶性组(37 例),比较良性组、恶性组的SUVmax值,比较恶性组中不同分化程度患者的SUVmax值,分析 18F-FDG PET/CT对肝脏良恶性肿瘤的鉴别诊断效能.结果 恶性组的SUVmax值显著高于良性组(P＜0.05).恶性组中不同分化程度患者的SUVmax值比较:低分化＞中分化＞高分化,差异均有统计学意义(P＜0.05).18F-FDG PET/CT诊断肝脏肿瘤的AUC为 0.813,95%置信区间为 0.738～0.896,敏感度为 83.02%,特异度为 86.49%.结论 18F-FDG PET/CT在肝脏良恶性肿瘤的鉴别诊断中具有较高的敏感度、特异度,有助于临床尽早进行定性诊断.
Abstract
Objective To explore the application value of 18F-FDG PET/CT in the differential diagnosis of benign and malignant liver tumors.Methods According to the results of pathological examination,90 patients with liver space-occupying lesions were divided into the benign group(n=53)and the malignant group(n=37).The SUVmax values of the benign group and the malignant group,and the SUVmax values of patients with different degrees of differentiation in the malignant group were compared.The differential diagnostic efficacy of 18F-FDG PET/CT for benign and malignant liver tumors was analyzed.Results The SUVmax value of the malignant group was significantly higher than that of the benign group(P<0.05).The SUVmax values of patients with different degrees of differentiation in the malignant group from high to low were low differentiation,moderate differentiation and high differentiation,with statistically significant differences(P<0.05).The AUC of 18F-FDG PET/CT in the diagnosis of liver tumors was 0.813,the 95%confidence interval was 0.738～0.896,the sensitivity was 83.02%,and the specificity was 86.49%.Conclusions 18F-FDG PET/CT has high sensitivity and specificity in the differential diagnosis of benign and malignant liver tumors,which is helpful for early clinical qualitative diagnosis.
关键词
18F-FDG/PET/CT/肝脏肿瘤/鉴别诊断/SUVmax值Key words
18F-FDG PET/CT/Liver tumor/Differential diagnosis/SUVmax value引用本文复制引用
基金项目
河南省医学科技攻关计划省部共建项目(SBGJ202002064)
出版年
2024
国家科技期刊平台
NSTL
万方数据