Rationale and Objectives
To investigate the value of dual-energy spectral computed tomography (DESCT) in the quantitative differentiation between pulmonary malignant masses and inflammatory masses.
Materials and Methods
This study was an institutional review board–approved study, and written informed consent was obtained from all patients. Sixty patients with 35 lung cancers and 25 inflammatory masses underwent DESCT scan during arterial phase (AP) and venous phase (VP). CT numbers of net enhancement in 70 keV monochromatic images in central and peripheral regions of masses and their differences (dCT) were measured. Iodine concentrations in the two regions were measured and normalized to the aorta as normalized iodine concentrations (NICs). The slopes of spectral attenuation curves (λHU) in the two regions were also calculated. The two-sample t test was used to compare quantitative parameters. Receiver operating characteristic (ROC) curves were generated to calculate sensitivity and specificity.
Results
CT numbers of net enhancement and NICs in central regions, and λHU values both in the central and peripheral region of lung cancers were significantly lower than those of inflammatory masses during AP and VP. On the other hand, the dCT values of lung cancers were higher than that of inflammatory masses. NIC value in the central regions in VP had the highest sensitivity (86%) and specificity (100%) in differentiating malignant masses from inflammatory masses.
Conclusions
DESCT imaging with quantitative parameters such as CT numbers of 70 keV monochromatic images, NIC, and λHU may be a new method for differentiating lung cancers from inflammatory masses.
Primary lung cancer is one of the most common cancers worldwide, comprising 17% of the total new cancer cases and 23% of the total cancer deaths. Early surgery and chemotherapy have been shown to be highly effective treatment techniques in most patients with lung cancer . However, some inflammatory masses with benign nature such as granulomatous inflammation, focal organizing pneumonia, and lung abscess are also very common in lung. The treatment options for inflammatory masses vary and consist of high-dose steroids, irradiation, and antibiotics, and the unnecessary pulmonary resection should be avoided. The differentiation between lung cancer and inflammatory masses is thus essential because of the different therapeutic approaches. The availability of multidetector-row computed tomography (MDCT) plays an important role in characterizing pulmonary masses noninvasively according to the morphology, interfaces, inner densities, and enhancement of masses . However, the nonspecific CT image appearance and the high degree of enhancement of inflammatory masses are similar to lung cancers, making it difficult to differentiate between the two common lesions in lungs by conventional scanning methods.
Recently a new dual-energy spectral computed tomography (DESCT) imaging mode based on the rapid switching between high- and low-energy data sets from view to view during a single rotation on the high-definition GE Discovery CT750 HD scanner was introduced, which could produce both the monochromatic spectral images at energy levels ranging from 40 to 140 keV and the material decomposition images for quantitative iodine concentration measurement . This spectral CT imaging mode was also named Gemstone Spectral Imaging (GSI) mode by the manufacturer. The DESCT has found its multiple clinical uses in diagnosing pulmonary embolism , staging gastric cancer , and differentiating small hepatocellular carcinoma from small hepatic hemangioma . The purpose of our study was to investigate the clinical utility of DESCT in quantitatively differentiating pulmonary malignant masses from inflammatory masses.
Materials and methods
Patients
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Reference Standards
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CT Examinations
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Quantitative Analysis
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Statistical Analysis
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Results
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Table 1
Quantitative Assessment of the CT Number of Net Enhancement in 70-keV Monochromatic Image, NIC, and λ HU in the Central Region of Lung Cancers and Inflammatory Masses in AP and VP
Group CT Number (HU) NIC λ HU AP Lung cancers 14.80 ± 8.08 0.04 ± 0.02 1.1 ± 0.65 Inflammatory masses 35.89 ± 18.60 0.14 ± 0.08 2.59 ± 1.01t 4.83 4.63 5.34P value .001 <.001 <.001 VP Lung cancers 32.45 ± 18.19 0.12 ± 0.10 1.69 ± 0.57 Inflammatory masses 56.27 ± 20.76 0.40 ± 0.19 3.40 ± 1.10t 6.79 4.40 5.25P value <.001 .001 <.001
AP, arterial phase; CT, computed tomography; HU, Hounsfield unit; λHU, slope of spectral CT curve; NIC, normalized iodine concentration; VP, venous phase.
Values are mean ± standard deviation (95% confidence interval).
Table 2
Quantitative Assessment of the CT Number of Net Enhancement in 70-keV Monochromatic Image, NIC, and λ HU in the Peripheral Region of Lung Cancers and Inflammatory Masses in AP and VP
Group CT Number (HU) NIC λ HU AP Lung cancers 45.18 ± 20.83 0.11 ± 0.06 1.32 ± 0.40 Inflammatory masses 60.19 ± 26.23 0.16 ± 0.09 2.57 ± 0.10t 0.49 1.80 −5.4P value .63 .09 <.001 VP Lung cancers 76.95 ± 26.64 0.45 ± 0.40 1.24 ± 0.60 Inflammatory masses 85.54 ± 24.67 0.42 ± 0.10 3.04 ± 1.21t 0.81 0.28 −6.16P value .43 .78 <.001
AP, arterial phase; CT, computed tomography; HU, Hounsfield unit; λHU, slope of spectral CT curve; NIC, normalized iodine concentration; VP, venous phase.
Values are mean ± standard deviation (95% confidence interval).
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Table 3
Quantitative Assessment of the dCT Values in the 70-keV Monochromatic Images for Lung Cancers and Inflammatory Masses in AP and VP
AP VP Lung cancers 34.11 ± 10.83 HU 33.91 ± 12.05 HU Inflammatory masses 17.96 ± 5.62 HU 15.13 ± 7.40HU_t_ −3.90 −3.64P value <.001 <.001
AP, arterial phase; dCT, the computed tomography value difference between the central and peripheral regions in lung masses in 70-keV images; HU, Hounsfield unit; VP, venous phase.
Values are mean ± standard deviation (95% confidence interval).
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![Figure 3, The λ HU of organizing pneumonia in an 80-year-old man (a) and the λ HU of adenocarcinoma in a 62-year-old man (b) . λ HU is calculated from the spectral computed tomography curve of the lesion (Hounsfield units [HU] in the y-axis, and photon energies in the x-axis), as λ HU = (HU measured at 40 keV − HU measured at 100 keV)/60.](https://storage.googleapis.com/dl.dentistrykey.com/clinical/DifferentiationofLungCancersFromInflammatoryMasseswithDualEnergySpectralCTImaging/2_1s20S1076633214003870.jpg)
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Table 4
Quantitative Assessment of the AUC, Thresholds, Sensitivities, and Specificities of the CT Values of Net Enhancement in the 70-keV Monochromatic Images, dCT Values, NIC, and λHU for Distinguishing Lung Cancers From Inflammatory Masses During AP and VP
AUC Thresholds Sensitivities (%) ∗ Specificities (%) † CT value (AP) 0.82 30.60 68 (24) 95 (24) CT value (VP) 0.83 51.95 73 (25) 95 (24) NIC (AP) 0.94 0.15 77 (27) 100 (25) NIC (VP) 0.96 0.34 86 (30) 100 (25) dCT (AP) 0.82 26.05 74 (26) 92 (23) dCT (VP) 0.84 34.16 67 (23) 92 (23) λ HU (central AP) 0.89 1.09 100 (35) 59 (15) λ HU (central VP) 0.94 2.29 85 (30) 85 (21) λ HU (peripheral AP) 0.88 1.91 73 (25) 95 (24) λ HU (peripheral VP) 0.90 2.12 82 (29) 90 (22)
AP, arterial phase; AUC, area under the ROC; CT, computed tomography; dCT, the computed tomography value difference between the central and peripheral regions in lung masses in 70-keV images; HU, Hounsfield unit; λ HU , slope of spectral CT curve; λ HU (peripheral AP), the slope of spectral CT curve in the peripheral regions of lesion during AP; λ HU (peripheral VP), the slope of spectral CT curve in the peripheral regions of lesion during VP; λ HU (central AP), the slope of spectral CT curve in the central regions of lesion during AP; λ HU (central VP), the slope of spectral CT curve in the central regions of lesion during VP; λ HU (peripheral VP), the slope of spectral CT curve in the peripheral regions of lesion during VP; NIC, normalized iodine concentration; VP, venous phase.
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Discussion
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Acknowledgments
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