Three-dimensional iodine mapping quantified by dual-energy CT for predicting programmed death-ligand 1 expression in invasive pulmonary adenocarcinoma.

We examined the association between texture features using three-dimensional (3D) io-dine density histogram on delayed phase of dual-energy CT (DECT) and expression of programmed death-ligand 1 (PD-L1) using immunostaining methods in non-small cell lung cancer. Consecutive 37 patients were scanned by DECT. Unenhanced and enhanced (3 min delay) images were obtained. 3D texture analysis was performed for each nodule to obtain 7 features (max, min, median, mean, standard deviation, skewness, and kurtosis) from iodine density mapping and extracellular volume (ECV). A pathologist evaluated a tumor proportion score (TPS, %) using PD-L1 immunostaining: PD-L1 high (TPS ≥ 50%) and low or negative expression (TPS < 50%). Associations between PD-L1 expression and each 8 parameter were evaluated using logistic regression analysis. The multivariate logistic regression analysis revealed that skewness and ECV were independent indicators associated with high PD-L1 expression (skewness: odds ratio [OR] 7.1 [95% CI 1.1, 45.6], p = 0.039; ECV: OR 6.6 [95% CI 1.1, 38.4], p = 0.037). In the receiver-operating characteristic analysis, the area under the curve of the combination of skewness and ECV was 0.83 (95% CI 0.67, 0.93) with sensitivity of 64% and specificity of 96%. Skewness from 3D iodine density histogram and ECV on dual energy CT were significant factors for predicting PD-L1 expression.

Prediction of cardiac allograft vasculopathy using splenic switch-off on myocardial PET.

BACKGROUND: Heart transplantation (HTx) is a definitive therapy for refractory heart failure. Cardiac allograft vasculopathy (CAV), characterized by diffuse arteriopathy involving the epicardial coronary arteries and microvasculature, is the major cause of death for patients with HTx. 13N-ammonia positron emission tomography (NH3-PET) can offer diagnostic and prognostic utility for CAV. The splenic switch-off (SSO) detected in NH3-PET is a hemodynamic indicator of favorable response to adenosine. We hypothesized that both CAV and SSO reflected a pathology that progresses in parallel with systemic vascular endothelial dysfunction. Therefore, we quantitatively evaluated splenic adenosine reactivity measured using NH3-PET as an index of endothelial function, and examined its predictability for CAV. METHODS: Forty-eight patients who underwent NH3-PET after HTx were analyzed. The spleen ratio was calculated as the mean standardized uptake value, measured by placing an ROI on the spleen, at stress divided by that at rest. SSO was defined by a cutoff determined using receiver operating characteristic (ROC) analysis for the spleen ratio. The endpoint was appearance or progression of CAV. Predictability of SSO was analyzed using Kaplan-Meier analysis. RESULTS: The endpoint occurred in 9 patients during a mean follow-up of 45 ±â€¯17 months. ROC curve analysis demonstrated a cutoff of 0.94 for spleen ratio. Patients without SSO displayed a significantly higher CAV rate than those with SSO (p = 0.022). CONCLUSIONS: SSO reflects the endothelial function of systemic blood vessels and was a predictor of CAV in patients with HTx.

Association between interstitial lung abnormality and mortality in patients with esophageal cancer.

PURPOSE: To investigate the relationship between interstitial lung abnormalities (ILAs) and mortality in patients with esophageal cancer and the cause of mortality. MATERIALS AND METHODS: This retrospective study investigated patients with esophageal cancer from January 2011 to December 2015. ILAs were visually scored on baseline CT using a 3-point scale (0 = non-ILA, 1 = indeterminate for ILA, and 2 = ILA). ILAs were classified into subcategories of non-subpleural, subpleural non-fibrotic, and subpleural fibrotic. Five-year overall survival (OS) was compared between patients with and without ILAs using the multivariable Cox proportional hazards model. Subgroup analyses were performed based on cancer stage and ILA subcategories. The prevalences of treatment complications and death due to esophageal cancer and pneumonia/respiratory failure were analyzed using Fisher's exact test. RESULTS: A total of 478 patients with esophageal cancer (age, 66.8 years ± 8.6 [standard deviation]; 64 women) were evaluated in this study. Among them, 267 patients showed no ILAs, 125 patients were indeterminate for ILAs, and 86 patients showed ILAs. ILAs were a significant factor for shorter OS (hazard ratio [HR] = 1.68, 95% confidence interval [CI] 1.10-2.55, P = 0.016) in the multivariable Cox proportional hazards model adjusting for age, sex, smoking history, clinical stage, and histology. On subgroup analysis using patients with clinical stage IVB, the presence of ILAs was a significant factor (HR = 3.78, 95% CI 1.67-8.54, P = 0.001). Subpleural fibrotic ILAs were significantly associated with shorter OS (HR = 2.22, 95% CI 1.25-3.93, P = 0.006). There was no significant difference in treatment complications. Patients with ILAs showed a higher prevalence of death due to pneumonia/respiratory failure than those without ILAs (non-ILA, 2/95 [2%]; ILA, 5/39 [13%]; P = 0.022). The prevalence of death due to esophageal cancer was similar in patients with and without ILA (non-ILA, 82/95 [86%]; ILA 32/39 [82%]; P = 0.596). CONCLUSION: ILAs were significantly associated with shorter survival in patients with esophageal cancer.

Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix.

PURPOSE: To predict solid and micropapillary components in lung invasive adenocarcinoma using radiomic analyses based on high-spatial-resolution CT (HSR-CT). MATERIALS AND METHODS: For this retrospective study, 64 patients with lung invasive adenocarcinoma were enrolled. All patients were scanned by HSR-CT with 1024 matrix. A pathologist evaluated subtypes (lepidic, acinar, solid, micropapillary, or others). Total 61 radiomic features in the CT images were calculated using our modified texture analysis software, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select optimal radiomic features for predicting solid and micropapillary components in lung invasive adenocarcinoma. Final data were obtained by repeating tenfold cross-validation 10 times. Two independent radiologists visually predicted solid or micropapillary components on each image of the 64 nodules with and without using the radiomics results. The quantitative values were analyzed with logistic regression models. The receiver operating characteristic curves were generated to predict of solid and micropapillary components. P values < 0.05 were considered significant. RESULTS: Two features (Coefficient Variation and Entropy) were independent indicators associated with solid and micropapillary components (odds ratio, 30.5 and 11.4; 95% confidence interval, 5.1-180.5 and 1.9-66.6; and P = 0.0002 and 0.0071, respectively). The area under the curve for predicting solid and micropapillary components was 0.902 (95% confidence interval, 0.802 to 0.962). The radiomics results significantly improved the accuracy and specificity of the prediction of the two radiologists. CONCLUSION: Two texture features (Coefficient Variation and Entropy) were significant indicators to predict solid and micropapillary components in lung invasive adenocarcinoma.

Increased Left Ventricular End-Diastolic Volume Index Is Associated With Increased Adverse Events After MitraClip Implantation.

This study investigated the association between the left ventricular end-diastolic volume index (LVEDVI) and the incidence of adverse clinical events in patients after MitraClip implantation. In this retrospective, observational study, 123 patients who underwent the MitraClip procedure were enrolled. Participants were divided into 2 groups according to the LVEDVI cut-off level, calculated using receiver operating characteristic curve analysis, to predict the primary end point and the occurrence of cardiovascular events was compared between the groups. The primary end point was all-cause mortality and hospitalization because of heart failure. The receiver operating characteristic curve analysis for the composite primary end point revealed an LVEDVI cut-off point of 118 ml/m2. Based on this threshold, 61 patients (49.6%) were categorized into the LVEDVI <118 ml/m2 group, whereas 62 (50.4%) fell into the LVEDVI ≥118 ml/m2 group. Over a median follow-up period of 336 days (interquartile range 80 to 667), the primary end points occurred in 15 and 26 patients in the LVEDVI <118 and LVEDVI ≥118 ml/m2 groups, corresponding to incidence rates of 24.6% and 41.9%, respectively. Patients in the LVEDVI ≥118 ml/m2 group demonstrated a significantly higher risk of adverse clinical events than those in the LVEDVI <118 ml/m2 group (hazard ratio 2.24, 95% confidence interval 1.17 to 4.28, p = 0.01). This trend persisted even after adjusting for several confounders (p = 0.02). In conclusion, increased LVEDVI values were associated with increased adverse clinical events after MitraClip implantation in patients with severe mitral valve regurgitation.

Direct Link Between Cardiac Failure and Global Cerebral Atrophy in a Young Adult: A Case Report on Reduced Cerebral Artery Blood Flow.

BACKGROUND Heart failure is associated with structural brain abnormalities, including atrophy of multiple brain regions. Previous studies have reported brain atrophy in middle-aged patients with systolic heart failure. In this report, we present the case of a 21-year-old woman with idiopathic dilated cardiomyopathy, cardiac failure, and global cerebral atrophy due to reduced cerebral artery blood flow. We also discuss the impact of brain atrophy in this young adult patient with severe heart failure and no risk factors for atherosclerosis. CASE REPORT A 21-year-old woman with dyspnea and leg edema was admitted to our hospital. After several examinations, an endomyocardial biopsy led to a diagnosis of idiopathic dilated cardiomyopathy, and transthoracic ultrasound cardiography revealed that her left ventricular ejection fraction was 36%. One year after the first hospitalization, her heart failure was classified as New York Heart Association Class III. Magnetic resonance imaging showed severe global brain atrophy, and single-photon emission computed tomography combined with brain computed tomography showed reduced blood flow to the entire brain. She had no risk factors for atherosclerosis and no atherosclerotic changes to her brain or carotid arteries, but her neuropsychological and neurological findings indicated more pronounced brain and cognitive dysfunction. CONCLUSIONS This young adult patient with idiopathic dilated cardiomyopathy, cardiac failure, and global cerebral atrophy showed reduced cerebral artery blood flow and cognitive impairment. The findings of this report indicate that low cardiac output may directly cause brain atrophy in patients with systolic heart failure.

Usefulness of Three-Dimensional Iodine Mapping Quantified by Dual-Energy CT for Differentiating Thymic Epithelial Tumors.

Background: Dual-energy CT has been reported to be useful for differentiating thymic epithelial tumors. The purpose is to evaluate thymic epithelial tumors by using three-dimensional (3D) iodine density histogram texture analysis on dual-energy CT and to investigate the association of extracellular volume fraction (ECV) with the fibrosis of thymic carcinoma. Methods: 42 patients with low-risk thymoma (n = 20), high-risk thymoma (n = 16), and thymic carcinoma (n = 6) were scanned by dual-energy CT. 3D iodine density histogram texture analysis was performed for each nodule on iodine density mapping: Seven texture features (max, min, median, average, standard deviation [SD], skewness, and kurtosis) were obtained. The iodine effect (average on DECT180s-average on unenhanced DECT) and ECV on DECT180s were measured. Tissue fibrosis was subjectively rated by one pathologist on a three-point grade. These quantitative data obtained by examining associations with thymic carcinoma and high-risk thymoma were analyzed with univariate and multivariate logistic regression models (LRMs). The area under the curve (AUC) was calculated by the receiver operating characteristic curves. p values < 0.05 were significant. Results: The multivariate LRM showed that ECV > 21.47% in DECT180s could predict thymic carcinoma (odds ratio [OR], 11.4; 95% confidence interval [CI], 1.18-109; p = 0.035). Diagnostic performance was as follows: Sensitivity, 83.3%; specificity, 69.4%; AUC, 0.76. In high-risk thymoma vs. low-risk thymoma, the multivariate LRM showed that the iodine effect ≤1.31 mg/cc could predict high-risk thymoma (OR, 7; 95% CI, 1.02-39.1; p = 0.027). Diagnostic performance was as follows: Sensitivity, 87.5%; specificity, 50%; AUC, 0.69. Tissue fibrosis significantly correlated with thymic carcinoma (p = 0.026). Conclusions: ECV on DECT180s related to fibrosis may predict thymic carcinoma from thymic epithelial tumors, and the iodine effect on DECT180s may predict high-risk thymoma from thymoma.

The evaluation of the reduction of radiation dose via deep learning-based reconstruction for cadaveric human lung CT images.

To compare the quality of CT images of the lung reconstructed using deep learning-based reconstruction (True Fidelity Image: TFI ™; GE Healthcare) to filtered back projection (FBP), and to determine the minimum tube current-time product in TFI without compromising image quality. Four cadaveric human lungs were scanned on CT at 120 kVp and different tube current-time products (10, 25, 50, 75, 100, and 175 mAs) and reconstructed with TFI and FBP. Two image evaluations were performed by three independent radiologists. In the first experiment, using the same tube current-time product, a side-by-side TFI and FBP comparison was performed. Images were evaluated with regard to noise, streak artifacts, and overall image quality. Overall image quality was evaluated in view of whole image quality. In the second experiment, CT images reconstructed using TFI and FBP with five different tube current-time products were displayed in random order, which were evaluated with reference to the 175 mAs-FBP image. Images were scored with regard to normal structure, abnormal findings, noise, streak artifacts, and overall image quality. Median scores from three radiologists were statistically analyzed. Quantitative evaluation of noise was performed by setting regions of interest (ROIs) in air. In first experiment, overall image quality was improved, and noise was decreased in images of TFI compared to that of FBP for all tube current-time products. In second experiment, scores of all evaluation items except for small vessels in images of 25 mAs-TFI were almost the same as that of 175 mAs-FBP (all p > 0.31). Using TFI instead of FBP, at least 85% radiation dose reduction could be possible without any degradation in the image quality.

CT-based radiomics analysis for differentiation between thymoma and thymic carcinoma.

Background: The purpose of our study was to differentiate between thymoma and thymic carcinoma using a radiomics analysis based on the computed tomography (CT) image features. Methods: The CT images of 61 patients with thymic epithelial tumors (TETs) who underwent contrast-enhanced CT with slice thickness <1 mm were analyzed. Pathological examination of the surgical specimens revealed thymoma in 45 and thymic carcinoma in 16. Tumor volume and the ratio of major axis to minor axis were calculated using a computer-aided diagnostic software. Sixty-one different radiomics features in the segmented CT images were extracted, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select the optimal radiomics features for predicting thymic carcinoma. The association between the quantitative values and a diagnosis of thymic carcinoma were analyzed with logistic regression models. Parameters identified as significant in univariate analysis were included in multiple analyses. Receiver-operating characteristic (ROC) curves were assessed to evaluate the diagnostic performance. Results: Thymic carcinoma was significantly predominant in men (P=0.001). Optimal radiomics features for predicting thymic carcinoma were as follows: gray-level co-occurrence matrix (GLCM)-homogeneity, GLCM-energy, compactness, large zone high gray-level emphasis (LZHGE), solidity, size of minor axis, and kurtosis. Multiple logistic regression analysis of these features revealed solidity and GLCM-energy as independent indicators associated with thymic carcinoma [odds ratio, 14.7 and 14.3; 95% confidence interval (CI): 1.6-139.0 and 3.0-68.7; and P=0.045 and 0.002, respectively]. Area under the curve (AUC) for diagnosing thymic carcinoma was 0.882 (sensitivity, 81.2%; specificity, 91.1%). Multivariate analysis adjusted for sex similarly revealed two features (solidity and GLCM-energy) as independent indicators associated with thymic carcinoma (odds ratio, 14.6 and 23.9; 95% CI: 2.4-89.2 and 1.9-302.8; P=0.004 and 0.014, respectively). Adjusted AUC for diagnosing thymic carcinoma was 0.921 (95% CI: 0.82-0.97): sensitivity, 62.5% and specificity, 100%. Conclusions: Two texture features (GLCM-energy and solidity) were significant predictors of thymic carcinoma.

The Effective Use of Digoxin in a Patient with Metastatic Breast Cancer and Anthracycline-induced Cardiomyopathy.

Anthracyclines have cardiotoxic side effects. Cardioprotective drugs such as angiotensin-converting enzyme inhibitors and beta-blockers are therefore recommended for patients with anthracycline-induced cardiomyopathy. We herein present a 54-year-old woman with recurrent metastatic breast cancer who developed heart failure (HF) with a left ventricular ejection fraction (LVEF) of 22% after undergoing epirubicin chemotherapy. However, her HF symptoms and low LVEF persisted despite 5 months of cardioprotective therapy and additional oral pimobendan. Pimobendan was discontinued because of ventricular arrhythmia and hypotension. After the start of low-dose (0.125 mg daily) digoxin, her LVEF increased to 42%, and her HF symptoms improved with no adverse events.

Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network.

OBJECTIVES: To compare diagnostic performance for pulmonary invasive adenocarcinoma among radiologists with and without three-dimensional convolutional neural network (3D-CNN). METHODS: Enrolled were 285 patients with adenocarcinoma in situ (AIS, n = 75), minimally invasive adenocarcinoma (MIA, n = 58), and invasive adenocarcinoma (IVA, n = 152). A 3D-CNN model was constructed with seven convolution-pooling and two max-pooling layers and fully connected layers, in which batch normalization, residual connection, and global average pooling were used. Only the flipping process was performed for augmentation. The output layer comprised two nodes for two conditions (AIS/MIA and IVA) according to prognosis. Diagnostic performance of the 3D-CNN model in 285 patients was calculated using nested 10-fold cross-validation. In 90 of 285 patients, results from each radiologist (R1, R2, and R3; with 9, 14, and 26 years of experience, respectively) with and without the 3D-CNN model were statistically compared. RESULTS: Without the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 70.0%, 52.1%, and 90.5%; R2, 72.2%, 75%, and 69%; and R3, 74.4%, 89.6%, and 57.1%, respectively. With the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 72.2%, 77.1%, and 66.7%; R2, 74.4%, 85.4%, and 61.9%; and R3, 74.4%, 93.8%, and 52.4%, respectively. Diagnostic performance of each radiologist with and without the 3D-CNN model had no significant difference (p > 0.88), but the accuracy of R1 and R2 was significantly higher with than without the 3D-CNN model (p < 0.01). CONCLUSIONS: The 3D-CNN model can support a less-experienced radiologist to improve diagnostic accuracy for pulmonary invasive adenocarcinoma without deteriorating any diagnostic performances. KEY POINTS: • The 3D-CNN model is a non-invasive method for predicting pulmonary invasive adenocarcinoma in CT images with high sensitivity. • Diagnostic accuracy by a less-experienced radiologist was better with the 3D-CNN model than without the model.

Lung Adenocarcinoma at CT with 0.25-mm Section Thickness and a 2048 Matrix: High-Spatial-Resolution Imaging for Predicting Invasiveness.

Background High-spatial-resolution (HSR) CT provides detailed information and clear delineation of lung anatomy and disease states. HSR CT may have high diagnostic performance for predicting invasiveness of lung adenocarcinoma. Purpose To examine the diagnostic performance of HSR CT in predicting the invasiveness of lung adenocarcinoma. Materials and Methods In this retrospective study, 89 consecutive patients with adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IVA) were included who underwent surgery for lung cancer between January 2018 and December 2019. All patients underwent HSR CT with 0.25-mm section thickness and a 2048 matrix. Two independent observers evaluated the images for the presence or absence of the following HSR CT findings: lobulation, spiculation, pleural indentation, vessel convergence, homogeneity of ground-glass opacity, reticulation, irregularity and centrality of solid portion, and air bronchiologram (irregularity, disruption, or dilatation). The total diameter (≤1.6 cm or >1.6 cm) and the longest diameter of the solid portion (≤0.8 cm or >0.8 cm) were evaluated. Logistic regression models were used to identify findings associated with MIA plus IVA. Receiver operating characteristic analysis was performed to determine diagnostic performance. Results Eighty-nine patients (mean, 69 years ± 11 [standard deviation]; 49 men) were evaluated. The size of the nodules with invasion was a mean of 2.5 cm ± 1.2. Univariable analysis revealed lobulation, spiculation, pleural indentation, irregular and central solid portion, air bronchiologram with disruption and/or irregular dilatation, and total and solid portion diameters as associated with MIA plus IVA (all, P < .05). After adjustment for age, sex, and pack-years of smoking, disruption of air bronchogram and solid portion diameter greater than 0.8 cm remained as predictors of invasiveness (P = .001 and P = .02, respectively). The diagnostic performance of these two findings combined were as follows: sensitivity of 97% (59 of 61 patients; 95% confidence interval: 94%, 100%) and specificity of 86% (19 of 22 patients; 95% confidence interval: 65%, 97%), with an area under the curve of 0.94. Conclusion Using high-spatial-resolution CT, disruption of air bronchiologram and a solid portion greater than 0.8 cm were independently associated with a greater likelihood of invasiveness in lung adenocarcinoma. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lynch and Oh in this issue.

Ultra high-resolution computed tomography with 1024-matrix: Comparison with 512-matrix for the evaluation of pulmonary nodules.

PURPOSE: To determine whether a 1024-matrix provides superior image quality for the evaluation of pulmonary nodules. MATERIALS AND METHODS: Prospective evaluation conducted between December 2017 and April 2018, during which CT images showing lung nodules of more than 6 mm and less than 30 mmm were reconstructed with 2 different protocols: 0.5-mm thickness, 512 × 512 matrix, 34.5-cm field of view (FOV) (0.5-512 protocol); and 2-mm thickness, 1024 × 1024 matrix, 34.5-cm FOV (2-1024 protocol). Lung nodule characteristics such as margin, lobulation, pleural indentation, spiculation as well as peripheral vessels and bronchioles visibility and overall image quality were evaluated by three chest radiologists, using a 5-point scale. Image noise was evaluated by measuring the standard deviation in the region of interest for each image. RESULTS: A total of 89 nodules were evaluated. The 2-1024 protocol performed significantly better for the subjective evaluation of pulmonary nodules (p = 0.006 ∼ p < 0.0001). However, image noise was significantly higher both subjectively and objectively (p = 0.036, p < 0.0001). CONCLUSION: The use of a 2-1024 protocol does not increase the amount of images and allows better assessment of pulmonary nodules, despite noise increase.

Application of deep learning (3-dimensional convolutional neural network) for the prediction of pathological invasiveness in lung adenocarcinoma: A preliminary study.

To compare results for radiological prediction of pathological invasiveness in lung adenocarcinoma between radiologists and a deep learning (DL) system.Ninety patients (50 men, 40 women; mean age, 66 years; range, 40-88 years) who underwent pre-operative chest computed tomography (CT) with 0.625-mm slice thickness were included in this retrospective study. Twenty-four cases of adenocarcinoma in situ (AIS), 20 cases of minimally invasive adenocarcinoma (MIA), and 46 cases of invasive adenocarcinoma (IVA) were pathologically diagnosed. Three radiologists of different levels of experience diagnosed each nodule by using previously documented CT findings to predict pathological invasiveness. DL was structured using a 3-dimensional (3D) convolutional neural network (3D-CNN) constructed with 2 successive pairs of convolution and max-pooling layers, and 2 fully connected layers. The output layer comprises 3 nodes to recognize the 3 conditions of adenocarcinoma (AIS, MIA, and IVA) or 2 nodes for 2 conditions (AIS and MIA/IVA). Results from DL and the 3 radiologists were statistically compared.No significant differences in pathological diagnostic accuracy rates were seen between DL and the 3 radiologists (P >.11). Receiver operating characteristic analysis demonstrated that area under the curve for DL (0.712) was almost the same as that for the radiologist with extensive experience (0.714; P = .98). Compared with the consensus results from radiologists, DL offered significantly inferior sensitivity (P = .0005), but significantly superior specificity (P = .02).Despite the small training data set, diagnostic performance of DL was almost the same as the radiologist with extensive experience. In particular, DL provided higher specificity than radiologists.

Volumetric analysis of the thymic epithelial tumors: correlation of tumor volume with the WHO classification and Masaoka staging.

BACKGROUND: The purpose of our study was to investigate the correlation between tumor volume (TV) and each subtype of thymic epithelial tumors (TETs) based on the World Health Organization (WHO) classification and Masaoka staging. METHODS: Sixty-one consecutive patients (45 thymomas and 16 thymic carcinomas) were studied. All were classified according to Masaoka staging: 31 non-invasive TETs (stage I) and 30 invasive TETs (8 stage II, 11 stage III, 3 stage IVa, and 8 stage IVb). TV on computed tomography (CT) were semi-automatically calculated using our software. The correlation of TV with each WHO subtype and Masaoka staging was analyzed using Mann-Whitney U and Scheffe's F test. RESULTS: Thymic carcinoma (mean ± SD, 117.5±143.6 cm3) was significantly larger than thymoma (53.4±78.4 cm3) (P=0.0016). Stage IVb tumor (190.8±156.8 cm3) was significantly larger than stage I (33.1±42.6 cm3) (P<0.05). Invasive TETs were significantly larger than non-invasive TETs (P=0.0016). TV >54.3 cm3 indicated invasive TETs. CONCLUSIONS: TV of invasive TETs may be larger at the time of initial presentation. TV >54.3 cm3 indicates invasive TETs.