Suppr超能文献

提高动态 F-FDG PET 与静态 F-FDG PET 比较在良恶性 LDCT 筛查检出肺结节中的诊断效能与注射剂量的关系。

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

机构信息

Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America. Department of Engineering Physics, Tsinghua University, Beijing, People's Republic of China. Key Laboratory of Particle & Radiation Imaging, Ministry of Education, Tsinghua University, Beijing, People's Republic of China.

出版信息

Phys Med Biol. 2018 Sep 6;63(17):175015. doi: 10.1088/1361-6560/aad97f.

Abstract

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 low-dose dynamic F-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 K 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 K 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.

摘要

通过常规低剂量计算机断层扫描(LDCT)筛查,肺癌死亡率可降低 20%,但该方法具有高灵敏度但特异性低的特点,导致假阳性结节率较高。将 PET 与 CT 相结合,可为不确定筛查检出的结节提供更准确的诊断。在这项工作中,我们使用基于患者研究的虚拟临床试验来研究低剂量动态 F-FDG PET 在鉴别良恶性结节中的作用,该临床试验具有真实数据。六名最初通过 LDCT 筛查发现肺结节的患者在注射 10 mCi FDG 后接受了 90 分钟的单床位 PET 扫描。从各种计数水平(100%、50%、10%、5%和 1%)的欠采样列表模式数据生成低剂量静态和动态图像。通过向患者数据的时间活性曲线(TAC)添加结节群体变异性、测量噪声和静态 PET 采集起始时间变异性,进行了虚拟临床试验。我们使用接收器操作特性(ROC)分析来估计 SUV 和净摄取常数 K 的分类能力,方法是从它们模拟的良性和恶性分布中获得。在动态 Patlak 分析中研究了各种扫描持续时间和起始时间(t ),以优化具有基于人群的输入函数(PBIF)的简化采集方案。随着扫描持续时间和 t 的增加,ROC 分析的曲线下面积(AUC)增加。使用 PBIF 获得的结果与使用图像衍生输入函数(IDIF)获得的结果非常相似。使用优化的 t 和扫描持续时间(10%剂量)的 K 的 AUC 值高于 100%剂量的 SUV。我们的研究结果表明,对于类似于本研究中的先导和模拟人群的患者,使用低至 1 mCi FDG 的动态 PET 可以提供良恶性肺结节之间的鉴别,其灵敏度和特异性均高于 90%,对于通过 LDCT 筛查发现的不确定肺结节。

相似文献

引用本文的文献

6
Generation of Whole-Body FDG Parametric Images from Static PET Images Using Deep Learning.利用深度学习从静态PET图像生成全身FDG参数图像
IEEE Trans Radiat Plasma Med Sci. 2023 May;7(5):465-472. doi: 10.1109/trpms.2023.3243576. Epub 2023 Feb 22.
7
Inter-pass motion correction for whole-body dynamic PET and parametric imaging.全身动态正电子发射断层扫描及参数成像的帧间运动校正
IEEE Trans Radiat Plasma Med Sci. 2023 Apr;7(4):344-353. doi: 10.1109/trpms.2022.3227576. Epub 2022 Dec 8.

本文引用的文献

10
Tracer Kinetic Modeling in PET.正电子发射断层扫描中的示踪动力学建模
PET Clin. 2007 Apr;2(2):267-77. doi: 10.1016/j.cpet.2007.08.003. Epub 2008 Feb 15.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验