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Improved discrimination between benign and malignant LDCT screening-detected lung nodules with dynamic over static 18F-FDG PET as a function of injected dose

机译:动态的静态18F-FDG PET作为注射剂量的函数改善了LDCT筛查良性和恶性肺结节的鉴别度

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摘要

Lung cancer mortality rate can be significantly reduced by up to 20% through routine low-dose computed tomography (LDCT) screening, which, however, has high sensitivity but low specificity, resulting in a high rate of false-positive nodules. Combining PET with CT may provide more accurate diagnosis for indeterminate screening-detected nodules. In this work, we investigated lowdose dynamic 18F-FDG PET in discrimination between benign and malignant nodules using a virtual clinical trial based on patient study with ground truth. Six patients with initial LDCT screening-detected lung nodules received 90-min single-bed PET scans following a 10 mCi FDG injection. Low-dose static and dynamic images were generated from under-sampled list-mode data at various count levels (100%, 50%, 10%, 5%, and 1%). A virtual clinical trial was performed by adding nodule population variability, measurement noise, and static PET acquisition start time variability to the time activity curves (TACs) of the patient data. We used receiver operating characteristic (ROC) analysis to estimate the classification capability of standardized uptake value (SUV) and net uptake constant Ki from their simulated benign and malignant distributions. Various scan durations and start times (t*) were investigated in dynamic Patlak analysis to optimize simplified acquisition protocols with a population-based input function (PBIF). The area under curve (AUC) of ROC analysis was higher with increased scan duration and earlier t*. Highly similar results were obtained using PBIF to those using image-derived input function (IDIF). The AUC value for Ki using optimized t* and scan duration with 10% dose was higher than that for SUV with 100% dose. Our results suggest that dynamic PET with as little as 1 mCi FDG could provide discrimination between benign and malignant lung nodules with higher than 90% sensitivity and specificity for patients similar to the pilot and simulated population in this study, with LDCT screening-detected indeterminate lung nodules.
机译:通过常规的低剂量计算机断层扫描(LDCT)筛查,肺癌的死亡率可以显着降低多达20%,但是,其敏感性高但特异性低,导致假阳性结节的发生率很高。 PET与CT的结合可以为不确定的筛查结节提供更准确的诊断。在这项工作中,我们使用基于地面真相的患者研究的虚拟临床试验,研究了低剂量动态 18 F-FDG PET在良性和恶性结节之间的区别。六名最初经LDCT筛查发现肺结节的患者在10 mCi FDG注射后接受了90分钟的单床PET扫描。低剂量的静态和动态图像是由在各种计数级别(100%,50%,10%,5%和1%)的欠采样列表模式数据生成的。通过将结节群体变异性,测量噪声和静态PET采集开始时间变异性添加到患者数据的时间活动曲线(TAC)中,进行了虚拟临床试验。我们使用接收器工作特征(ROC)分析,从模拟的良性和恶性分布中估算了标准化摄取值(SUV)和净摄取常数Ki的分类能力。在动态Patlak分析中研究了各种扫描持续时间和开始时间(t *),以优化基于人口的输入函数(PBIF)的简化采集协议。 ROC分析的曲线下面积(AUC)随着扫描持续时间的增加和t *的增加而增加。使用PBIF与使用源自图像的输入函数(IDIF)的结果非常相似。使用优化的t *和10%剂量的扫描持续时间的Ki的AUC值高于100%剂量的SUV的AUC值。我们的结果表明,与本研究中的试点和模拟人群相似的患者,动态PET的FDG仅为1 mCi FDG可以区分良性和恶性肺结节,敏感性和特异性均高于90%,并通过LDCT筛查检测出不确定的肺结节。

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