Differentiation of Hamartomas and Malignant Lung Tumors in Single-Phased Dual-Energy Computed Tomography

IF 2.2 4区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Tomography Pub Date : 2024-02-11 DOI:10.3390/tomography10020020

M. Winkelmann, S. Gassenmaier, Sven S. Walter, Christoph Artzner, Konstantin Nikolaou, M. Bongers

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Abstract

This study investigated the efficacy of single-phase dual-energy CT (DECT) in differentiating pulmonary hamartomas from malignant lung lesions using virtual non-contrast (VNC), iodine, and fat quantification. Forty-six patients with 47 pulmonary lesions (mean age: 65.2 ± 12.1 years; hamartomas-to-malignant lesions = 22:25; male: 67%) underwent portal venous DECT using histology, PET-CT and follow-up CTs as a reference. Quantitative parameters such as VNC, fat fraction, iodine density and CT mixed values were statistically analyzed. Significant differences were found in fat fractions (hamartomas: 48.9%; malignancies: 22.9%; p ≤ 0.0001) and VNC HU values (hamartomas: −20.5 HU; malignancies: 17.8 HU; p ≤ 0.0001), with hamartomas having higher fat content and lower VNC HU values than malignancies. CT mixed values also differed significantly (p ≤ 0.0001), but iodine density showed no significant differences. ROC analysis favored the fat fraction (AUC = 96.4%; sensitivity: 100%) over the VNC, CT mixed value and iodine density for differentiation. The study concludes that the DECT-based fat fraction is superior to the single-energy CT in differentiating between incidental pulmonary hamartomas and malignant lesions, while post-contrast iodine density is ineffective for differentiation.

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单相双能量计算机断层扫描中 Hamartomas 与恶性肺肿瘤的鉴别

本研究采用虚拟非对比（VNC）、碘和脂肪定量法研究了单相双能 CT（DECT）在区分肺部火腿肠瘤和肺部恶性病变方面的疗效。46 名患有 47 个肺部病灶的患者（平均年龄：65.2 ± 12.1 岁；仓瘤与恶性病灶的比例 = 22:25；男性：67%）接受了门静脉 DECT，并以组织学、PET-CT 和后续 CT 作为参考。对 VNC、脂肪分数、碘密度和 CT 混合值等定量参数进行了统计分析。脂肪分数（火腿肠瘤：48.9%；恶性肿瘤：22.9%；p ≤ 0.0001）和 VNC HU 值（火腿肠瘤：-20.5 HU；恶性肿瘤：17.8 HU；p ≤ 0.0001）存在显著差异，与恶性肿瘤相比，火腿肠瘤的脂肪含量更高，VNC HU 值更低。CT混合值也有显著差异（p≤ 0.0001），但碘密度无显著差异。ROC 分析显示，脂肪分数（AUC = 96.4%；灵敏度：100%）优于 VNC、CT 混合值和碘密度的分辨能力。该研究得出结论，基于 DECT 的脂肪分数在区分偶然性肺火腿肠瘤和恶性病变方面优于单能 CT，而对比后碘密度对区分无效。

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来源期刊

Tomography Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

2.70

自引率

10.50%

发文量

222

期刊介绍： TomographyTM publishes basic (technical and pre-clinical) and clinical scientific articles which involve the advancement of imaging technologies. Tomography encompasses studies that use single or multiple imaging modalities including for example CT, US, PET, SPECT, MR and hyperpolarization technologies, as well as optical modalities (i.e. bioluminescence, photoacoustic, endomicroscopy, fiber optic imaging and optical computed tomography) in basic sciences, engineering, preclinical and clinical medicine. Tomography also welcomes studies involving exploration and refinement of contrast mechanisms and image-derived metrics within and across modalities toward the development of novel imaging probes for image-based feedback and intervention. The use of imaging in biology and medicine provides unparalleled opportunities to noninvasively interrogate tissues to obtain real-time dynamic and quantitative information required for diagnosis and response to interventions and to follow evolving pathological conditions. As multi-modal studies and the complexities of imaging technologies themselves are ever increasing to provide advanced information to scientists and clinicians. Tomography provides a unique publication venue allowing investigators the opportunity to more precisely communicate integrated findings related to the diverse and heterogeneous features associated with underlying anatomical, physiological, functional, metabolic and molecular genetic activities of normal and diseased tissue. Thus Tomography publishes peer-reviewed articles which involve the broad use of imaging of any tissue and disease type including both preclinical and clinical investigations. In addition, hardware/software along with chemical and molecular probe advances are welcome as they are deemed to significantly contribute towards the long-term goal of improving the overall impact of imaging on scientific and clinical discovery.