Differentiation of Lung Cancers From Inflammatory Masses with Dual-Energy Spectral CT Imaging

Hou, Wei; Wu, Hua; Yin, Yuehui; Cheng, Jie; Zhang, Qing; Xu, Jian

doi:10.1016/j.acra.2014.10.004

Cited by 72 publications

(69 citation statements)

References 27 publications

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“…[1] Early surgery and radio-chemotherapy have been proven to be highly effective in most lung cancer patients. On the contrary, the prognosis of advanced lung cancer patients is still poor, with less than 10% surviving for 5 years.…”

Section: Introductionmentioning

confidence: 99%

Correlation of iodine uptake and perfusion parameters between dual-energy CT imaging and first-pass dual-input perfusion CT in lung cancer

Chen

Duan

et al. 2017

Medicine

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To investigate the potential relationship between perfusion parameters from first-pass dual-input perfusion computed tomography (DI-PCT) and iodine uptake levels estimated from dual-energy CT (DE-CT).The pre-experimental part of this study included a dynamic DE-CT protocol in 15 patients to evaluate peak arterial enhancement of lung cancer based on time-attenuation curves, and the scan time of DE-CT was determined. In the prospective part of the study, 28 lung cancer patients underwent whole-volume perfusion CT and single-source DE-CT using 320-row CT. Pulmonary flow (PF, mL/min/100 mL), aortic flow (AF, mL/min/100 mL), and a perfusion index (PI = PF/[PF + AF]) were automatically generated by in-house commercial software using the dual-input maximum slope method for DI-PCT. For the dual-energy CT data, iodine uptake was estimated by the difference (λ) and the slope (λHU). λ was defined as the difference of CT values between 40 and 70 KeV monochromatic images in lung lesions. λHU was calculated by the following equation: λHU = |λ/(70 − 40)|. The DI-PCT and DE-CT parameters were analyzed by Pearson/Spearman correlation analysis, respectively.All subjects were pathologically proved as lung cancer patients (including 16 squamous cell carcinoma, 8 adenocarcinoma, and 4 small cell lung cancer) by surgery or CT-guided biopsy. Interobserver reproducibility in DI-PCT (PF, AF, PI) and DE-CT (λ, λHU) were relatively good to excellent (intraclass correlation coefficient [ICC]Inter = 0.8726–0.9255, ICCInter = 0.8179–0.8842; ICCInter = 0.8881–0.9177, ICCInter = 0.9820–0.9970, ICCInter = 0.9780–0.9971, respectively). Correlation coefficient between λ and AF, and PF were as follows: 0.589 (P < .01) and 0.383 (P < .05). Correlation coefficient between λHU and AF, and PF were as follows: 0.564 (P < .01) and 0.388 (P < .05).Both the single-source DE-CT and dual-input CT perfusion analysis method can be applied to assess blood supply of lung cancer patients. Preliminary results demonstrated that the iodine uptake relevant parameters derived from DE-CT significantly correlated with perfusion parameters derived from DI-PCT.

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Section: Introductionmentioning

confidence: 99%

Correlation of iodine uptake and perfusion parameters between dual-energy CT imaging and first-pass dual-input perfusion CT in lung cancer

Chen

Duan

et al. 2017

Medicine

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“…However, among all types of benign focal pulmonary lesions, inflammatory lesions can share not only similar morphologic characteristics, but also comparable contrast enhancement levels with lung cancers (3), making the differential diagnosis even more difficult (4).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Heterogeneity Difference Between Edge and Core by Using Texture Analysis

et al. 2016

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“…CT numbers on the iodine‐enhanced image and the degree of enhancement show good agreement (intra‐class correlation coefficients: 0.83 and 0.91, respectively) with CT numbers on virtual non‐enhanced and non‐enhanced weighted average images . In a spectral CT imaging study, the net CT number increases of malignant SPNs in the monochromatic energy level images after contrast enhancement were higher than those of benign SPNs . Some studies concluded that net CT number of 70keV monochromatic images in a (CT70keVa) and v (CT70keVv) phases indicated suitable enhancement in tumors, while others suggested that the net CT number of 40 keV (CT40keV) was a good choice, although the scan times were different in the a and v phases after injection of the contrast medium.…”

Section: Discussionmentioning

confidence: 85%

Can spectral computed tomography imaging improve the differentiation between malignant and benign pulmonary lesions manifesting as solitary pure ground glass, mixed ground glass, and solid nodules?

Chen

Wei

et al. 2018

Thoracic Cancer

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BackgroundThis study quantitatively assessed the efficacy of spectral computed tomography (CT) imaging parameters for differentiating the malignancy and benignity of solitary pulmonary nodules (SPNs) manifesting as ground glass nodules (GGNs) and solid nodules (SNs).MethodsThe study included 114 patients with SPNs (61 GGNs, and 53 SNs) who underwent CT plain and enhanced scans in the arterial (a) and venous (v) phases using the spectral imaging mode. The spectral CT imaging parameters included: iodine concentrations (IC) of lesions in the arterial (ICLa) and venous (ICLv) phases; normalized IC (NICa/NICv, normalized to the IC in the aorta); the slope of the spectral Hounsfield unit (HU) curve (λHUa/λHUv); and monochromatic CT number (CT40keVa/v, CT70keVa/v) enhancement on 40 and 70 keV images. The two‐sample Mann–Whitney U test was used to compare quantitative parameters between malignant and benign SPNs, SNs, and GGNs.ResultsPathology revealed 75 lung cancer cases, 3 metastatic nodules, 14 benign nodules, and 22 inflammatory nodules. Among the 53 SNs there were 37 malignant and 16 benign nodules. Among the 61 GGNs there were 41 malignant and 20 benign nodules. Overall, the CT40keVa, λHUa, CT40keVv, λHUv, and ICLv of benign SPNs were all greater than those of malignant SPNs (all P < 0.05). For GGNs, CT40keVa/v, CT70keVa/v, λHUa/λHUv, and ICLv of malignant GGNs were all lower than those of benign GGNs.ConclusionSpectral CT imaging is a more promising method for distinguishing malignant from benign nodules, especially in nodules manifesting as GGNs in contrast‐enhanced scanning.

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Differentiation of Lung Cancers From Inflammatory Masses with Dual-Energy Spectral CT Imaging

Cited by 72 publications

References 27 publications

Correlation of iodine uptake and perfusion parameters between dual-energy CT imaging and first-pass dual-input perfusion CT in lung cancer

Correlation of iodine uptake and perfusion parameters between dual-energy CT imaging and first-pass dual-input perfusion CT in lung cancer

Assessment of Heterogeneity Difference Between Edge and Core by Using Texture Analysis

Can spectral computed tomography imaging improve the differentiation between malignant and benign pulmonary lesions manifesting as solitary pure ground glass, mixed ground glass, and solid nodules?

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