Computed Tomography Examination
All thorax CT examinations were performed on a 128-row CT scanner (Somatom Definition Flash; Siemens Healthcare, Forchheim, Germany).
Computed tomography images were acquired during a single breath-hold from the thoracic inlet to the costophrenic angle level in the craniocaudal direction. Contrast medium was administered by a dual-head pump injector (Stellant; Medtron, Saarbruecken, Germany). A volume of 60 to 100 mL (1.2 mL/kg of body weight) iodixanol injection 320 (HengRui, JiangSu, China) was injected in forearm vein at a flow rate of 3 mL/s using an 20-G needle, followed by a saline flush of 30 mL at the same rate. After intravenous injection of contrast medium, the arterial phase scan was triggered automatically 5 seconds after the attenuation in distal thoracic aorta increased to a default threshold (100 HU), and the venous phase scan was started with a 35-second delay after the end of the arterial phase scan. The scan parameters were as follows: 2 different tubes voltages (100 kV and Sn 140 kV, reference tube currents 160/68 mAs, respectively), slice thickness of 5.0 mm, collimation of 128 × 0.6 mm, tube rotation time of 0.28 seconds, and pitch of 0.55.
The spectral information from dual-energy data was used to generate iodine maps in axial 1.5-mm slices. These maps are comparable with color-coded CT images, but the displayed voxel values base exclusively on materials identified by the algorithm as contrast agent.
With a weighting factor of 0.6, the 2 data sets from the 2 x-ray tubes were fused to virtual images corresponding to a 120 kV scan (in the following, these images will be referred to as “conventional grey scale CT”) and were reconstructed into axial 3-mm slices using a standard soft tissue reconstruction kernel (Q30f medium smooth). Virtual monochromatic 70-keV images, synthesized from dual-energy CT data, that are known to be similar to conventional 120-kV images.
All data were transferred to a dedicated workstation (syngo.via, VB10B; Siemens Healthcare). The CT data was analyzed by 1 experienced radiologists (W.Q.Y., with 8 years of experience in chest CT imaging), who was aware that the patients had thymic tumors, but he was blinded to the pathological types of the tumors.
On conventional CT images, the tumor maximum and mean diameter, shape, boundary, necrotic or cystic changes, calcification, mediastinal lymphadenopathy, and presence of pericardial or pleural effusion were analyzed. The longest dimension (a) and the greatest perpendicular diameters (b) of the tumor were measured at the level where the tumor appeared largest on the horizontal-sectional image, and the other longest dimension (c) were obtained on sagittal or coronal slice. The mean diameter was calculated by (a + b + c)/3.9 The tumor shape was evaluated based on the ratio of the long-axis diameter to the short one. It was classified as round if the long- to short-axis ratio was less than or equal to 1.5, oval if the ratio was greater than 1.5 but less than 2.0, or plaque if the ratio was greater than or equal to 2.0. Marginal characteristics were subclassified as smooth, lobulated, or irregular. Mediastinal lymphadenopathy was defined based on following criteria: short-axis diameter greater than 10 mm.
Dual-energy computed tomography data analysis was performed with commercial software (Syngo.via, dual-energy, liver virtual noncontrast, Siemens Healthcare). Reviewers selected the slice that showed the largest part of the tumor. Three-round ROIs were drawn manually using an electronic cursor, which were placed to include the solid tumor elements by defining ROI based on the Hounsfield unit (HU) on the virtual nonenhanced, arterial phase, and venous phase images, avoiding large vessels, calcification, obvious cystic, and necrotic areas. The mean ± SD ROI area was 94.2 ± 11.0 mm2 (range = 60.0–110.0 mm2), respectively. The iodine maps and energy spectral curves were generated automatically (Figs. 1, 2), and the quantitative parameter: iodine-related HU (IHU, HU), IC (milligram per milliliter), mixed HU (MHU, HU), iodine ratio (IR, %), slope of energy spectral HU curve (λ) values both in artery and venous phase, and virtual noncontrast (VNC, HU) values were obtained.
The MHU was the CT attenuation value in postcontrast-enhanced HU, and the mean IHU was calculated as IHU = MHU − VNC. The mean IC (milligram per milliliter) of tumor was measured on the iodine images. The λ value was calculated as the CT attenuation difference at 2 energy levels (40 and 100 keV) divided by the energy difference (60keV) from the spectral HU curve, according to the formula: λ = |CT40 keV − CT100 keV|/60.19
The final diagnosis was determined by surgical specimen and confirmed with histopathological examination. Pathologic analysis was performed by a pathological expert. Tissue samples obtained from the specimens were routinely processed and stained for hematoxylin and eosin. Based on the criteria of the 2004 World Health Organization histological classification and Jeong simplification classification,23,24 thymic tumors were divided into the following 4 groups: low-risk thymoma (type A, AB, and B1), high-risk thymomas (type B2 and B3), thymic carcinoma, and thymic lymphoma.
Numerical variables were denoted as mean and SD. The Kolmogorov-Smirnov test was used for assessing the normality of data distribution. Conventional CT features (including tumor shape, boundary, and presence of necrotic changes, calcification, lymphadenopathy, pericardiac, or pleural effusion) among low-, high-risk thymoma, thymic carcinoma, and thymic lymphoma groups were analyzed using the χ2 test. The tumor mean diameter, maximum diameter, and DECT parameters (IHU, IC, MHU, IR, and λ values) were compared for differences among 4 groups based on one-way analysis of variance, and further post hoc multiple comparisons were performed with Bonferroni test (equal variances assumed) and Dunett T3 test (equal variances not assumed). Receiver operating characteristic (ROC) curve analyses were performed to determine optimum thresholds for differentiating the defined groups based on various parameters and to calculate sensitivity, specificity, and area under the curve (AUC) values. A P value of less than 0.05 indicated a statistically significant difference. IBM SPSS 20.0 software (IBM Corp, Chicago, Ill) was used for statistical analysis.
The Clinical and Demographic Data
The patient demographic characteristics are shown in Table 1. The study group consisted of 30 males and 27 females with a mean ± SD age of 47.7 ± 2.1 years (range = 11–76 years). The major clinical presentations of the patients were myasthenia gravis (10.5%, 6/57 patients), chest pain (19.3%, 11/57), others (47.4%, 27/57), and the other 13 patients were without any discomfort (22.8%).
The pathologic classifications of the 57 thymic tumor patients demonstrated that 16 patients had low-risk thymomas (type A [n = 2], AB [n = 12], and B1 [n = 2]); 15 high-risk thymomas (type B2 [n = 12] and B3 [n = 3]); 14 thymic carcinomas (squamous cell carcinoma [n = 10] and neuroendocrine carcinomas [n = 4]), and 12 thymic lymphomas (T lymphoblastic lymphoma [n = 5], diffuse large B-cell lymphoma [n = 3], Hodgkin lymphoma [n = 3], and mucosa-associated lymphoid tissue lymphoma [n = 1]) (Table 1).
Comparison of Conventional CT Findings Among Low-, High-Risk Thymomas, Thymic Carcinomas, and Thymic Lymphomas
Comparisons of conventional CT features among 4 groups depending on World Health Organization pathological classifications of thymic tumor are shown in Table 2. Overall, tumor mean diameter, maximum diameter, boundary, necrotic or cystic changes, mediastinal lymphadenopathy, and presence of pericardial or pleural effusion differed among patients with 4 groups (all P < 0.05), whereas tumor shape and calcification did not differ depending on tumor pathological classifications (all P > 0.05).
Dual-Energy Computed Tomography Parameters Comparison Among Low-, High-Risk Thymomas, Thymic Carcinomas, and Thymic Lymphomas
Comparison of DECT parameters among patients with low-, high-risk thymoma, thymic carcinoma, and thymic lymphoma are shown in Table 3 and Figure 3. There were significant differences for artery-phase IHU (Fig. 3A), venous phase IHU (Fig. 3B), artery-phase IC (Fig. 3C), venous phase IC (Fig. 3D), λ value, dual-phase MHU, and IR among low-, high-risk thymoma, thymic carcinoma, and thymic lymphoma groups based on one-way analysis of variance (all P < 0.05). In addition, pairwise comparison was performed, and the results revealed that the IHU, IC, and MHU values in both artery and venous phase were higher in low-risk thymoma group than in the high-risk thymoma, thymic carcinoma, and thymic lymphoma groups (IHU: 27.31, 15.15, 14.49, and 15.08 HU in artery phase, and 37.16, 22.15, 18.55, and 16.73 HU in venous phase; IC: 1.30, 0.67, 0.64, and 0.58 mg/mL in artery phase, and 1.75, 0.99, 0.82, and 0.70 mg/mL in venous phase; MHU: 64.06, 52.11, 51.35, and 48.18 HU in artery phase and 78.64, 61.39, 59.26, and 56.17 HU in venous phase, respectively, all P < 0.05/4).
In addition, venous phase IR value differed between low-risk thymoma and thymic carcinoma or thymic lymphoma groups (P < 0.05/4). However, the λ and artery-phase IR values did not differ between low- and high-risk thymoma, thymic carcinoma, or thymic lymphoma groups (P > 0.05/4).
Diagnostic Efficacy Analysis Results
Because there were statistically significant differences between low- and high-risk thymoma or thymic carcinoma groups in terms of dual-phase IHU, IC, and MHU values, diagnostic efficacy was assessed by ROC curves. The efficacy of parameters in differentiating the low- from high-risk thymoma and thymic carcinoma in ROC analysis is showed in Table 4 and Figure 4A. The venous phase IHU value yielded the highest performance with an AUC of 0.893, 75.0% sensitivity, and 89.7% specificity for differentiating the low- from high-risk thymoma and thymic carcinoma at the cutoff value of 34.3 HU. When comparing with venous phase IHU value, the other values also achieved the relatively high diagnostic efficacy, the AUC for artery-phase IHU, IC, MHU values, venous phase IC, and MHU value were 0.846, 0.866, 0.804, 0.888, and 0.888, respectively.
Similarly, in differentiating the low-risk thymoma from thymic lymphoma, the ROC analyses indicated that the venous phase IC value had the highest diagnostic efficacy with the AUC of 0.969, and sensitivity, specificity and the cutoff value were 87.5%, 100.0%, and 1.25 mg/mL, respectively. With regard to artery-phase IHU, IC, and MHU values as well as venous phase IHU and MHU values, the AUC were 0.836, 0.904, 0.862, 0.943, and 0.937, respectively, for differentiation of low-risk thymoma from thymic lymphoma (Table 4, Fig. 4B).
It is clinically important to accurately differentiate the thymic tumors before treatment. In the current study, we evaluated the differential diagnostic value of DECT parameters in thymic tumors. The results revealed that DECT parameters (IC, IHU, and MHU) in both artery and venous phase in patients with low-risk thymoma were significantly increased compared with those in patients with high-risk thymoma, thymic carcinoma, or thymic lymphoma. In addition, we also determined the most appropriate cutoff values for each suggested parameter, which could potentially be used in clinical practice regarding the differential diagnosis of thymic tumor before treatment.
Conventional CT with multiplanar reconstruction provides better morphological information regarding tumor internal structure and local spread for preoperative assessment, which are helpful in differentiating different pathological classifications and clinical stages of TETs.9,24–27 As in previous studies, current study detected that a smooth margin was more common in low-risk thymomas than in other thymic tumors, and necrotic or cystic changes, pericardial effusion, or pleural effusion was more often seen in thymic carcinoma or thymic lymphomas.9,24–27 The frequency of lymphadenopathy in thymic carcinomas on CT has been reported to range from 13% to 44%.9,24,27 In our study, mediastinal lymphadenopathy was seen in 21.4% of thymic carcinoma and 83.3% of thymic lymphoma patients, and no lymphadenopathy was seen in thymomas.
Iodine quantification parameters from DECT are significantly correlated with perfusion CT parameters,28 which can reflect the blood flow and assess the tumor vascularity.12,29,30 Angiogenesis is critical for tumor growth and metastasis.31 Previous research revealed a significant correlation between tumor invasiveness and angiogenesis in TET patients,22 and thymoma subtypes and thymic squamous cell carcinomas differed significantly in their vascular structure and expression of angiogenic growth factors.32 In this study, dual-phase DECT values (IC, IHU, and MHU) in patients with low-risk thymoma were significantly higher than those with high-risk thymoma, thymic carcinoma, or thymic lymphoma, which consisted with an initial result.20 Similarly, the maximal contrast-enhanced range from contrast-enhanced CT, blood volume value, and permeability from perfusion CT, and the fast diffusion coefficient (D*) value from intravoxel incoherent motion diffusion-weighted imaging in low-risk thymomas were significantly higher than that in high-risk thymomas or thymic carcinomas.5,9,33 This interesting blood flow characteristic of TETs can be explained by a pathologic research, which demonstrated that the short-spindled variant (57% histologic patterns of thymoma type A and AB) was composed of oval to short spindle cells commonly arranged in a hemangiopericytic or microcystic pattern.34 Therefore, taken together, these results suggest that DECT parameters might be valuable for differentiating thymic tumors.
In this study, we also evaluated the diagnostic efficacy of DECT parameters in differentiating the various thymic tumors. The results showed that venous phase IHU value achieved highest performance, with an AUC of 0.893, in differentiating the low- from high-risk thymomas or thymic carcinoma. The venous phase IC value obtained the highest diagnostic efficacy with the AUC of 0.969, in differentiating low-risk thymomas from thymic lymphoma. Therefore, as a supplement of the conventional CT, iodine quantification with DECT may potentially be used in clinical practice regarding the differential diagnosis of thymic tumors.
Our study had several limitations when interpreting the results. First, we drew the ROI manually, which might have introduced a sampling bias; the application of histogram analysis may improve diagnostic performance in future studies. Second, 14 patients did not undergo surgery because of the widespread invasion, and the final pathological results were proved by puncture biopsy, which may cause a study bias. Finally, because of the limited samples, we did not include the germ cell tumors, and further research is warranted to clarify this issue.
In summary, our study suggests that dual-phase DECT parameters including IC, IHU, and MHU in low-risk thymomas are significantly higher than in patients with high-risk thymoma, thymic carcinoma, and thymic lymphoma. Dual-energy computed tomography may be helpful in differential diagnosis of thymic tumors before subsequent treatment, with more quantitative information and higher efficacy compared with conventional CT examination.
The authors thank Dr. Yu-Yao Wang in our department for providing technical help regarding the analysis of DECT data.
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Keywords:Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.
thymoma; thymic carcinoma; thymic lymphoma; thymic epithelial tumors; dual-energy computed tomography; iodine quantification