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.

Radiologists with and without deep learning-based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses.

OBJECTIVES: To compare the performance of radiologists in characterizing and diagnosing pulmonary nodules/masses with and without deep learning (DL)-based computer-aided diagnosis (CAD). METHODS: We studied a total of 101 nodules/masses detected on CT performed between January and March 2018 at Osaka University Hospital (malignancy: 55 cases). SYNAPSE SAI Viewer V1.4 was used to analyze the nodules/masses. In total, 15 independent radiologists were grouped (n = 5 each) according to their experience: L (< 3 years), M (3-5 years), and H (> 5 years). The likelihoods of 15 characteristics, such as cavitation and calcification, and the diagnosis (malignancy) were evaluated by each radiologist with and without CAD, and the assessment time was recorded. The AUCs compared with the reference standard set by two board-certified chest radiologists were analyzed following the multi-reader multi-case method. Furthermore, interobserver agreement was compared using intraclass correlation coefficients (ICCs). RESULTS: The AUCs for ill-defined boundary, irregular margin, irregular shape, calcification, pleural contact, and malignancy in all 15 radiologists, irregular margin and irregular shape in L and ill-defined boundary and irregular margin in M improved significantly (p < 0.05); no significant improvements were found in H. L showed the greatest increase in the AUC for malignancy (not significant). The ICCs improved in all groups and for nearly all items. The median assessment time was not prolonged by CAD. CONCLUSIONS: DL-based CAD helps radiologists, particularly those with < 5 years of experience, to accurately characterize and diagnose pulmonary nodules/masses, and improves the reproducibility of findings among radiologists. KEY POINTS: • Deep learning-based computer-aided diagnosis improves the accuracy of characterizing nodules/masses and diagnosing malignancy, particularly by radiologists with < 5 years of experience. • Computer-aided diagnosis increases not only the accuracy but also the reproducibility of the findings across radiologists.

[A Case of Primary Liver Cancer and Gastric Glomus Tumor Diagnosed Preoperatively and Treated with Liver Segmentectomy and Local Gastrectomy].

A man in his 70s was concurrently suspected of having a submucosal tumor(SMT)of the stomach and a liver tumor during a medical examination. Abdominal contrast-enhanced CT scan revealed S8 hepatocellular carcinoma(HCC)and an SMT of the stomach, which was strongly enhanced from the early to the later phase. Upper gastrointestinal endoscopy revealed a 20 mm SMT in the antrum of the stomach. Endoscopic ultrasonography showed a hyperechoic tumor in the fourth layer of the gastric wall. T2-weighted MRI showed a 25 mm SMT in the antrum of the stomach with a faint high signal intensity compared with that of the gastric wall. The patient was diagnosed with HCC and gastric glomus tumor, and a liver segmentectomy and a local gastrectomy were performed. Immunohistochemistry of the SMT revealed the expression of α-SMA but no expression of desmin, c-kit, CD34, or S-100. Therefore, a diagnosis of a Glomus tumor of the stomach was made. Gastric Glomus tumors are very rare; therefore, we have reviewed some citations and would like to discuss our case.

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.

Detectability of pulmonary ossifications in fibrotic lung on ultra-high-resolution CT using 2048 matrix size and 0.25-mm slice thickness.

To investigate the prevalence of nodular pulmonary ossifications (POs) in patients with honeycombing on ultra-high-resolution CT (UHRCT) and to compare the detectability of nodular POs between images reconstructed using the ultra-high-resolution setting (UHR-setting) and those using the conventional setting (C-setting) on UHRCT. Twenty patients with honeycombing in the lung were evaluated retrospectively. All patients underwent non-contrast-enhanced UHRCT. Images were reconstructed with UHR-setting (matrix, 2048 × 2048; slice thickness, 0.25 mm) and with C-setting (matrix size, 512 × 512; slice thickness, 0.5 mm). Two chest radiologists independently recorded the number of nodular POs (< 4 mm diameter) in each lung lobes. Each lobe was classified as one of the following five categories according to the number of POs: C0, none; C1, 1-4 POs; C2, 5-9 POs; C3, 10-49 POs; and C4, ≥ 50 POs. The maximum CT values of the POs were measured and compared between the two settings. PO categories were significantly higher with UHR-setting than with C-setting (p < 0.001). Maximum CT values were significantly higher with UHR-setting than with C-setting (p < 0.001). Nodular POs were seen in 80% or more of patients with honeycombing and more easily detected in images reconstructed with UHR-setting than in those with C-setting.

Quantitative volumetry of ground-glass nodules on high-spatial-resolution CT with 0.25-mm section thickness and 1024 matrix: Phantom and clinical studies.

OBJECTIVES: To compare high-resolution (HR) and conventional (C) settings of high-spatial-resolution computed tomography (CT) for software volumetry of ground-glass nodules (GGNs) in phantoms and patients. METHODS: We placed -800 and -630 HU spherical GGN-mimic nodules in 28 different positions in phantoms and scanned them individually. Additionally, 60 GGNs in 45 patients were assessed retrospectively. Images were reconstructed using the HR-setting (matrix size, 1024; slice thickness, 0.25 mm) and C-setting (matrix size, 512; slice thickness, 0.5 mm). We measured the GGN volume and mass using software. In the phantom study, the absolute percentage error (APE) was calculated as the absolute difference between Vernier caliper measurement-based and software-based volumes. In patients, we measured the density (mean, maximum, and minimum) and classified GGNs into low- and high-attenuation GGNs. RESULTS: In images of the -800 HU, but not -630 HU, phantom nodules, the volumes and masses differed significantly between the two settings (both p < 0.01). The APE was significantly lower in the HR-setting than in the C-setting (p < 0.01). In patients, volumes did not differ significantly between settings (p = 0.59). Although the mean attenuation was not significantly different, the maximum and minimum values were significantly increased and decreased, respectively, in the HR-setting (both p < 0.01). The volumes of both low-attenuation and high-attenuation GGNs were not significantly different between settings (p = 0.78 and 0.39, respectively). CONCLUSION: The HR-setting might yield a more accurate volume for phantom GGN of -800 HU and influence the detection of maximum and minimum CT attenuation.

Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT.

OBJECTIVES: To develop a deep learning-based algorithm to detect aortic dissection (AD) and evaluate the diagnostic ability of the algorithm compared with those of radiologists. METHODS: Included in the study were 170 patients (85 with AD and 85 without AD). An AD detection algorithm was developed using a convolutional neural network with Xception architecture. Of the patient data, 80% were used for training and validation and 20% were used for testing. Fivefold cross-validation was performed to evaluate the method. An average of 6688 non-contrast-enhanced CT images (slice thickness, 5 mm) were used for training. A radiologist reviewed both contrast-enhanced and non-contrast-enhanced images and identified the slices of AD. The identified slices were used as ground truth. Receiver operating characteristic curve and area under the curve (AUC) analysis was performed. Five radiologists independently evaluated the images. The accuracy, sensitivity, and specificity of the algorithm and those of the radiologists were compared. RESULTS: The AUC of the developed algorithm was 0.940, and a cutoff value of 0.400 provided accuracy of 90.0%, sensitivity of 91.8%, and specificity of 88.2%. For the radiologists, median (range) accuracy, sensitivity, and specificity were 88.8 (83.5-94.1)%, 90.6 (83.5-94.1)%, and 94.1 (72.9-97.6)%, respectively. There was no significant difference in performance in terms of accuracy, sensitivity, or specificity between the algorithm and the average performance of the radiologists (p > 0.05). CONCLUSIONS: The developed algorithm showed comparable diagnostic performance to radiologists for detecting AD, which suggests the potential of the proposed method to support clinical practice by reducing missed ADs. KEY POINTS: • A deep learning-based algorithm for detecting aortic dissection was developed using the non-contrast-enhanced CT images of 170 patients. • The algorithm had an AUC of 0.940 for detecting aortic dissection. • The accuracy, sensitivity, and specificity of the algorithm were comparable to those of radiologists.

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.

Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation.

OBJECTIVE. The objective of our study was to assess the effect of the combination of deep learning-based denoising (DLD) and iterative reconstruction (IR) on image quality and Lung Imaging Reporting and Data System (Lung-RADS) evaluation on chest ultra-low-dose CT (ULDCT). MATERIALS AND METHODS. Forty-one patients with 252 nodules were evaluated retrospectively. All patients underwent ULDCT (mean ± SD, 0.19 ± 0.01 mSv) and standard-dose CT (SDCT) (6.46 ± 2.28 mSv). ULDCT images were reconstructed using hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR), and they were postprocessed using DLD (i.e., HIR-DLD and MBIR-DLD). SDCT images were reconstructed using filtered back projection. Three independent radiologists subjectively evaluated HIR, HIR-DLD, MBIR, and MBIR-DLD images on a 5-point scale in terms of noise, streak artifact, nodule edge, clarity of small vessels, homogeneity of the normal lung parenchyma, and overall image quality. Two radiologists independently evaluated the nodules according to Lung-RADS using HIR, MBIR, HIR-DLD, and MBIR-DLD ULDCT images and SDCT images. The median scores for subjective analysis were analyzed using Wilcoxon signed rank test with Bonferroni correction. Intraobserver agreement for Lung-RADS category between ULDCT and SDCT was evaluated using the weighted kappa coefficient. RESULTS. In the subjective analysis, ULDCT with DLD showed significantly better scores than did ULDCT without DLD (p < 0.001), and MBIR-DLD showed the best scores among the ULDCT images (p < 0.001) for all items. In the Lung-RADS evaluation, HIR showed fair or moderate agreement (reader 1 and reader 2: κw = 0.46 and 0.32, respectively); MBIR, moderate or good agreement (κw = 0.68 and 0.57); HIR-DLD, moderate agreement (κw = 0.53 and 0.48); and MBIR-DLD, good agreement (κw = 0.70 and 0.72). CONCLUSION. DLD improved the image quality of both HIR and MBIR on ULDCT. MBIR-DLD was superior to HIR_DLD for image quality and for Lung-RADS evaluation.

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.

The effect of the reconstruction algorithm for the pulmonary nodule detection under the metal artifact caused by a pacemaker.

The aim was to compare the effects of metal artifacts from a pacemaker on pulmonary nodule detection among computed tomography (CT) images reconstructed using filtered back projection (FBP), single-energy metal artifact reduction (SEMAR), and forward-projected model-based iterative reconstruction solution (FIRST).Nine simulated nodules were placed inside a chest phantom with a pacemaker. CT images reconstructed using FBP, SEMAR, and FIRST were acquired at low and standard dose, and were evaluated by 2 independent radiologists.FIRST demonstrated the most significantly improved metal artifact and nodule detection on low dose CT (P < .0032), except at 10 mA and 5-mm thickness. At standard-dose CT, SEMAR showed the most significant metal artifact reduction (P < .00001). In terms of nodule detection, no significant differences were observed between FIRST and SEMAR (P = .161).With a pacemaker present, FIRST showed the best nodule detection ability at low-dose CT and SEMAR is comparable to FIRST at standard dose CT.

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.

Influence of field of view size on image quality: ultra-high-resolution CT vs. conventional high-resolution CT.

OBJECTIVES: This study was conducted in order to compare the effect of field of view (FOV) size on image quality between ultra-high-resolution CT (U-HRCT) and conventional high-resolution CT (HRCT). METHODS: Eleven cadaveric lungs were scanned with U-HRCT and conventional HRCT and reconstructed with five FOVs (40, 80, 160, 240, and 320 mm). Three radiologists evaluated and scored the images. Three image evaluations were performed, comparing the image quality with the five FOVs with respect to the 160-mm FOV. The first evaluation was performed on conventional HRCT images, and the second evaluation on U-HRCT images. Images were scored on normal structure, abnormal findings, and overall image quality. The third evaluation was a comparison of the images obtained with conventional HRCT and U-HRCT, with scoring performed on overall image quality. Quantitative evaluation of noise was performed by setting ROIs. RESULTS: In conventional HRCT, image quality was improved when the FOV was reduced to 160 mm. In U-HRCT, image quality, except for noise, improved when the FOV was reduced to 80 mm. In the third evaluation, overall image quality was improved in U-HRCT over conventional HRCT at all FOVs. Noise of U-HRCT increased with respect to conventional HRCT when the FOV was reduced from 160 to 40 mm. However, at 240- and 320-mm FOVs, the noise of U-HRCT and conventional HRCT showed no differences. CONCLUSIONS: In conventional HRCT, image quality did not improve when the FOV was reduced below 160 mm. However, in U-HRCT, image quality improved even when the FOV was reduced to 80 mm. KEY POINTS: • Reducing the size of the field of view to 160 mm improves diagnostic imaging quality in high-resolution CT. • In ultra-high-resolution CT, improvements in image quality can be obtained by reducing the size of the field of view to 80 mm. • Ultra-high-resolution CT produces images of higher quality compared with conventional HRCT irrespective of the size of the field of view.

Separation between the chest wall and subpleural lung lesions: A two-step method to preoperatively exclude invasion or focal pleural adhesion by multidetector computed tomography.

PURPOSE: To develop and assess a non-invasive two-step method for evaluating the relationship between the parietal pleura and peripheral pulmonary lesions to preoperatively exclude invasion or focal pleural adhesion by multidetector computed tomography (CT). METHODS: Twenty-six patients with pulmonary peripheral lesions who underwent surgical lung resection between May and December 2017 were enrolled in this study. Routine CT was performed in the inspiratory phase in the supine position. Additional CT examinations were performed both in inspiratory and expiratory phases in the affected-side-up lateral position. Axial, sagittal, and coronal images were reconstructed from the CT data. In the first-step analysis, we evaluated the separation between the chest wall and subpleural lung lesions (separation) by comparing inspiratory- and expiratory-phase images obtained in the affected-side-up lateral position. When the separation was absent, we performed a second-step analysis, where we compared images obtained in the supine position during routine CT with those obtained in the affected-side-up lateral position and subsequently assessed the presence and absence of the separation. RESULTS: In the first-step analysis, the separation was observed in 21 lesions, which were categorised as showing "no invasion" or "no focal adhesion" on the basis of histological findings. After the second-step analysis, the separation was absent in three lesions and present in two; the latter two lesions were categorised as showing "no invasion" or "no focal adhesion" on the basis of operative and histological findings. Of the three lesions that did not exhibit the separation in either step of the analysis, two were diagnosed as exhibiting parietal pleural invasion on the basis of histological findings, while the third was categorised as showing "no invasion" or "no focal adhesion" on the basis of operative and histological findings. The sensitivity, specificity, positive and negative predictive values, and accuracy of this two-step method were 96% (95% confidence interval [CI]: 79-100%), 100% (95% CI: 16-100%), 100%, 67% (95% CI: 23-93%), and 96% (95% CI: 80-100%), respectively. CONCLUSIONS: Our two-step method is especially useful for excluding the parietal pleural involvement of peripheral pulmonary lesions. Even when the separation between the chest wall and subpleural lung lesions was limited, the change in position was useful for observing the separation and excluding parietal pleural involvement. This novel two-step method also has the advantage of being simple, cost-effective, and universally available.

Isolated unilateral proximal interruption of the pulmonary artery: findings of high-resolution computed tomography and three-dimensional volume rendering imaging of the pleura.

A unilateral proximal interruption of the pulmonary artery is a rare entity that is commonly associated with other congenital cardiovascular anomalies. However, less frequently, this condition may occur as an isolated finding, and some patients are completely asymptomatic. We report 2 cases of asymptomatic patients who had an isolated unilateral proximal interruption of the pulmonary artery. Herein, the radiological imaging findings are described with an emphasis on interlobular septal thickening of the affected lung demonstrated with high-resolution computed tomography. Three-dimensional volume rendering imaging clearly demonstrated reticular opacities on the surface of the affected side of the pleura.

Anterior mediastinal tumors: diagnostic accuracy of CT and MRI.

PURPOSE: To compare the diagnostic accuracy for anterior mediastinal tumors among CT, MRI, and both CT and MRI, and to determine the optimal CT and MRI procedures for the diagnosis of anterior mediastinal tumors. MATERIALS AND METHODS: Both CT and MRI were performed in 127 patients with pathologically diagnosed anterior mediastinal tumors. The patients included 48 cases of thymoma, 12 cases of thymic carcinoma, 12 cases of thymic cyst, 20 cases of mature teratoma, 13 cases of malignant germ cell tumor, and 22 cases of malignant lymphoma. The CT and MRI scans were assessed by two chest radiologists without knowledge of their clinical and pathologic data. The observers recorded various CT and MRI findings and their first choice of diagnosis. RESULTS: The two observers made a correct first-choice diagnosis in an average of 78 (61%) of 127 cases on CT, 71 (56%) of 127 cases on MRI, and 85.5 (67%) of 127 cases on both CT and MRI. These included 83% cases of thymoma on CT, 84% on MRI, and 85% on both CT and MRI; 38% cases of thymic carcinoma on CT and 13% on MRI, and 33% on both CT and MRI; 46% cases of thymic cyst on CT and 71% on MRI, and 63% on both CT and MRI; 58% cases of mature teratoma and 38% on MRI, and 78% on both CT and MRI; 35% cases of malignant germ cell tumor on CT and 27% on MRI, and 31% on both CT and MRI; and 55% cases of malignant lymphoma on CT and 43% on MRI, and 61% on both CT and MRI. There were significant differences between the diagnostic accuracy by CT and MRI in the cases with both thymic cysts and thymic carcinoma (p<0.05). CONCLUSION: CT is equal or superior to MRI in the diagnosis of anterior mediastinal tumors except for thymic cyst. CT should be considered the modality of choice following chest radiography, however, in certain circumstances, such as thymic cyst with hemorrhage or inflammation which mimic solid tumor despite low enhancement, MRI may be better in distinguishing anterior mediastinal tumors. For more helpful information in the diagnosis of mature teratoma after CT, MRI may follow.

Pulmonary malignancy in silicosis: factors associated with radiographic detection.

The aim was to assess radiographic features of pulmonary malignancies in silicosis and to reveal confounding factors in their detection. A total of 501 silicosis patients were followed up between 1982 and 2003. Sixty pulmonary malignancies were pathologically confirmed in 54 (10.8%) patients. Two radiologists reviewed serial radiographs of these patients to determine radiographic features of tumor (size, margin, nodule or consolidation, localization, overlying structures) and silicosis (profusion of pneumoconiotic opacities, progressive massive fibrosis (PMF), hilar lymphadenopathy). Eleven tumors were radiographically negative. Forty-nine tumors were retrospectively visible with radiograph. Of these, 15 tumors were clinically detected with radiograph, but 25 were missed. The remaining nine tumors became radiographically positive after positive sputum cytology. There were no differences between missed and detected nodules in terms of radiographic findings. The mean tumor size was 30mm (range: 15-90mm) and was significantly larger in patients with PMF or hilar lymphadenopathy than in those without (35mm vs. 24mm, p=0.006; 33mm vs. 24mm, p=0.038, respectively). This was correlated with background profusion of small pneumoconiotic opacities (r=0.433, p=0.024). Retrospective reading tests by three radiologists showed correct localization of tumor in 75%, however, the correct diagnosis with a high confidence was reached in only 54%. In conclusion, radiographic detection of malignancy in silicosis proved a difficult task and no single radiographic finding was found to be associated with missing the tumor. The presence of PMF, hilar lymphadenopathy and profusion of small pneumoconiotic nodules affected tumor size at detection.

Pulmonary cavitary nodules on computed tomography: differentiation of malignancy and benignancy.

OBJECTIVE: To establish computed tomographic findings that enable accurate differentiation between malignant and benign cavitary lung nodules. METHODS: Computed tomographic scans from 39 patients with malignant cavitary nodules and from 39 patients with benign cavitary nodules were independently assessed by 2 observers. They recorded the computed tomographic findings of both types of cavitary nodules and surrounding pulmonary parenchyma. The computed tomographic findings were then compared using chi test. RESULTS: The notch was found in 29% of benign cavitary nodule cases and in 54% of malignant cavitary nodule cases (P < 0.01). An irregular internal wall was found in 26% of benign nodules and in 49% of malignant nodule cases (P < 0.01). A linear margin (P < 0.01), satellite nodule presence (P < 0.01), bronchial wall thickening (P < 0.05), consolidation (P < 0.05), and ground-glass attenuation (P < 0.01) were significantly more frequent in benign cavitary nodules than in malignant ones. CONCLUSIONS: Although the computed tomographic findings of benign and malignant cavitary nodules overlap, some computed tomographic findings are useful for differentiating cavitary nodules.

Pulmonary nodules: 3D volumetric measurement with multidetector CT--effect of intravenous contrast medium.

PURPOSE: To retrospectively evaluate the effect of contrast medium on the three-dimensional volumetric measurement of pulmonary nodules. MATERIALS AND METHODS: The study was approved by the local institutional review committee, with waiver of informed consent. Sixty pulmonary nodules in 60 patients (17 women, 43 men; age range, 29-82 years) were imaged before and after administration of contrast medium with a 64-channel multidetector computed tomographic (CT) scanner; reconstructed images with a section thickness of 0.625 mm were obtained by using a bone algorithm and a standard algorithm. Volumetric measurements of pulmonary nodules were performed by using commercially available software, and the postcontrast volume ratio was calculated by dividing the postcontrast volume by the precontrast volume. Precontrast and postcontrast volumes were then analyzed by using a Wilcoxon signed rank test. RESULTS: The median measured volumes of pulmonary nodules were 817 mm(3) (precontrast imaging, bone algorithm), 887 mm(3) (postcontrast imaging, bone algorithm), 812 mm(3) (precontrast imaging, standard algorithm), and 855 mm(3) (postcontrast imaging, standard algorithm). The measured volumes obtained with the bone algorithm were significantly larger than those obtained with the standard algorithm, both before and after administration of contrast medium (P < .01); with both the standard algorithm and the bone algorithm, the measured postcontrast volumes were significantly larger than the precontrast volumes (P < .01). The postcontrast volume ratio was more than 1.0 in 45 cases (75%) when the bone algorithm was used and in 53 cases (88%) when the standard algorithm was used. The mean postcontrast volume ratio was 1.054 with the bone algorithm and 1.065 with the standard algorithm. CONCLUSION: The measured volume of pulmonary nodules obtained by using three-dimensional volumetric software increased after administration of contrast medium. Moreover, the measured volume of pulmonary nodules that was obtained with the bone algorithm was larger than that obtained with the standard algorithm, regardless of whether contrast medium was used.