Search

To evaluate the effects of computer-aided diagnosis (CAD) on inter-reader agreement in Lung Imaging Reporting and Data System (Lung-RADS) categorization. Two hundred baseline CT scans covering all Lung-RADS categories were randomly selected from the National Lung Cancer Screening Trial. Five radiologists independently reviewed the CT scans and assigned Lung-RADS categories without CAD and with CAD. The CAD system presented up to five of the most risk-dominant nodules with measurements and predicted Lung-RADS category. Inter-reader agreement was analyzed using multirater Fleiss κ statistics. The five readers reported 139–151 negative screening results without CAD and 126–142 with CAD. With CAD, readers tended to upstage (average, 12.3%) rather than downstage Lung-RADS category (average, 4.4%). Inter-reader agreement of five readers for Lung-RADS categorization was moderate (Fleiss kappa, 0.60 [95% confidence interval, 0.57, 0.63]) without CAD, and slightly improved to substantial (Fleiss kappa, 0.65 [95% CI, 0.63, 0.68]) with CAD. The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% [201/371] vs. 63.6% [232/365]). The proportion of disagreement in nodule size measurement was reduced from 5.1% (102/2000) to 3.1% (62/2000) with the use of CAD (p < 0.001). In 31 cancer-positive cases, substantial management discrepancies (category 1/2 vs. 4A/B) between reader pairs decreased with application of CAD (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004). Application of CAD demonstrated a minor improvement in inter-reader agreement of Lung-RADS category, while showing the potential to reduce measurement variability and substantial management change in cancer-positive cases. • Inter-reader agreement of five readers for Lung-RADS categorization was minimally improved by application of CAD, with a Fleiss kappa value of 0.60 to 0.65. • The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% vs. 63.6%). • In 31 cancer-positive cases, substantial management discrepancies between reader pairs, referring to a difference in follow-up interval of at least 9 months (category 1/2 vs. 4A/B), were reduced in half by application of CAD (32/310 to 16/310) (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004).

To apply radiomics analysis for overall survival prediction in chronic obstructive pulmonary disease (COPD), and evaluate the performance of the radiomics signature (RS). This study included 344 patients from the Korean Obstructive Lung Disease (KOLD) cohort. External validation was performed on a cohort of 112 patients. In total, 525 chest CT-based radiomics features were semi-automatically extracted. The five most useful features for survival prediction were selected by least absolute shrinkage and selection operation (LASSO) Cox regression analysis and used to generate a RS. The ability of the RS for classifying COPD patients into high or low mortality risk groups was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis. The five features remaining after the LASSO analysis were %LAA−950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA18mm. The RS demonstrated a C-index of 0.774 in the discovery group and 0.805 in the validation group. Patients with a RS greater than 1.053 were classified into the high-risk group and demonstrated worse overall survival than those in the low-risk group in both the discovery (log-rank test, < 0.001; hazard ratio [HR], 5.265) and validation groups (log-rank test, < 0.001; HR, 5.223). For both groups, RS was significantly associated with overall survival after adjustments for patient age and body mass index. A radiomics approach for survival prediction and risk stratification in COPD patients is feasible, and the constructed radiomics model demonstrated acceptable performance. The RS derived from chest CT data of COPD patients was able to effectively identify those at increased risk of mortality. • A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA −950 , AWT_Pi10_6 th , AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA 18mm were selected to generate a radiomics model. • A radiomics model for predicting survival of COPD patients demonstrated reliable performance with a C-index of 0.774 in the discovery group and 0.805 in the validation group. • Radiomics approach was able to effectively identify COPD patients with an increased risk of mortality, and patients assigned to the high-risk group demonstrated worse overall survival in both the discovery and validation groups.

Lung adenocarcinoma shows broad spectrum of prognosis and histologic heterogeneity. This study was to investigate the prognostic value of CT radiomics in resectable lung adenocarcinoma patients and assess its incremental value over clinical-pathologic risk factors. This retrospective analysis evaluated 1058 patients who underwent curative surgery for lung adenocarcinoma (training cohort: N = 754; temporal validation cohort: N = 304). Radiomics features were extracted from preoperative contrast-enhanced CT. Radiomics signature to predict disease-free survival (DFS) and overall survival (OS) was generated. Association between the radiomics signature and prognosis were evaluated using univariable and multivariable Cox proportional hazards regression analyses. Incremental value of the radiomics signature beyond clinical-pathologic risk factors was assessed using concordance index (C-index). The radiomics signatures were independently associated with DFS (hazard ratio [HR], 1.920; p

To investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. We retrospectively collected 2088 abnormal (nodule/mass) and 352 normal chest radiographs from two institutions. Three thoracic radiologists drew 2758 abnormalities regions. A total of 1736 abnormal chest radiographs were used for training and tuning convolutional neural networks (CNNs). The remaining 352 abnormal and 352 normal chest radiographs were used as a test set. Two CNNs (Mask R-CNN and RetinaNet) were selected to validate the effects of the squared different matrix size of chest radiograph (256, 448, 896, 1344, and 1792). For comparison, figure of merit (FOM) of jackknife free-response receiver operating curve and sensitivity were obtained. In Mask R-CNN, matrix size 896 and 1344 achieved significantly higher FOM (0.869 and 0.856, respectively) for detecting abnormalities than 256, 448, and 1792 (0.667–0.820) (p

To assess whether pulmonary vein injury is detectable on CT and associated with air embolism after percutaneous transthoracic needle biopsy (PTNB) in a tertiary referral hospital.Between January 2012 and November 2021, 11,691 consecutive CT-guided PTNBs in 10,685 patients were retrospectively evaluated. Air embolism was identified by reviewing radiologic reports. Pulmonary vein injury was defined as the presence of the pulmonary vein in the needle pathway or shooting range of the cutting needle with the presence of parenchymal hemorrhage. The association between pulmonary vein injury and air embolism was assessed using logistic regression analysis in matched patients with and without air embolism with a ratio of 1:4.A total of 27 cases of air embolism (median age, 67 years; range, 48-80 years; 24 men) were found with an incidence of 0.23% (27/11,691). Pulmonary vein injury during the procedures was identifiable on CT in 24 of 27 patients (88.9%), whereas it was 1.9% (2/108) for matched patients without air embolism The veins beyond the target lesion (70.8% [17/24]) were injured more frequently than the veins in the needle pathway before the target lesion (29.2% [7/24]). In univariable and multivariable analyses, pulmonary vein injury was associated with air embolism (odds ratio, 485.19; 95% confidence interval, 68.67-3428.19, p.001).Pulmonary vein injury was detected on CT and was associated with air embolism. Avoiding pulmonary vein injury with careful planning of the needle pathway on CT may reduce air embolism risk.• Pulmonary vein injury during CT-guided biopsy was identifiable on CT in most of the patients (88.9% [24/27]). • The veins beyond the target lesion (70.8% [17/24]) were injured more frequently than the veins in the needle pathway before the target lesion (29.2% [7/24]). • Avoiding the distinguishable pulmonary vein along the pathway or shooting range of the needle on CT may reduce the air embolism risk.

To evaluate the effects of computer-aided diagnosis (CAD) on inter-reader agreement in Lung Imaging Reporting and Data System (Lung-RADS) categorization.Two hundred baseline CT scans covering all Lung-RADS categories were randomly selected from the National Lung Cancer Screening Trial. Five radiologists independently reviewed the CT scans and assigned Lung-RADS categories without CAD and with CAD. The CAD system presented up to five of the most risk-dominant nodules with measurements and predicted Lung-RADS category. Inter-reader agreement was analyzed using multirater Fleiss κ statistics.The five readers reported 139-151 negative screening results without CAD and 126-142 with CAD. With CAD, readers tended to upstage (average, 12.3%) rather than downstage Lung-RADS category (average, 4.4%). Inter-reader agreement of five readers for Lung-RADS categorization was moderate (Fleiss kappa, 0.60 [95% confidence interval, 0.57, 0.63]) without CAD, and slightly improved to substantial (Fleiss kappa, 0.65 [95% CI, 0.63, 0.68]) with CAD. The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% [201/371] vs. 63.6% [232/365]). The proportion of disagreement in nodule size measurement was reduced from 5.1% (102/2000) to 3.1% (62/2000) with the use of CAD (p0.001). In 31 cancer-positive cases, substantial management discrepancies (category 1/2 vs. 4A/B) between reader pairs decreased with application of CAD (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004).Application of CAD demonstrated a minor improvement in inter-reader agreement of Lung-RADS category, while showing the potential to reduce measurement variability and substantial management change in cancer-positive cases.• Inter-reader agreement of five readers for Lung-RADS categorization was minimally improved by application of CAD, with a Fleiss kappa value of 0.60 to 0.65. • The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% vs. 63.6%). • In 31 cancer-positive cases, substantial management discrepancies between reader pairs, referring to a difference in follow-up interval of at least 9 months (category 1/2 vs. 4A/B), were reduced in half by application of CAD (32/310 to 16/310) (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004).

To evaluate a deep learning–based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions or minimally invasive adenocarcinoma (MIA) on CT, making comparisons with radiologist-derived measurements of solid portion size. Four hundred eleven subsolid nodules (SSNs) (120 preinvasive lesions or MIAs and 291 IPAs) in 333 patients who underwent surgery between June 2010 and August 2016 were retrospectively included to develop the model (370 SSNs in 293 patients for training and 41 SSNs in 40 patients for tuning). Ninety SSNs of 2 cm or smaller (45 preinvasive lesions or MIAs and 45 IPAs) resected in 2018 formed a validation set. Six radiologists measured the solid portion of each nodule. Performances of the model and radiologists were assessed using receiver operating characteristics curve analysis. The deep learning model differentiated IPA from preinvasive lesions or MIA with areas under the curve (AUCs) of 0.914, 0.956, and 0.833 for the training, tuning, and validation sets, respectively. The mean AUC of the radiologists was 0.835 in the validation set, without significant differences between radiologists and the model (p = 0.97). The sensitivity, specificity, and accuracy of the model were 71% (32/45), 87% (39/45), and 79% (71/90), respectively, whereas the corresponding values of the radiologists were 75.2% (203/270), 76.7% (207/270), and 75.9% (410/540) with a 5-mm threshold for the solid portion size. The performance of the model for differentiating IPA from preinvasive lesions or MIA was comparable to that of the radiologists’ measurements of solid portion size. • A deep learning–based model differentiated IPA from preinvasive lesions or MIA with AUCs of 0.914 and 0.956 for the training and tuning sets, respectively. • In the validation set including subsolid nodules of 2 cm or smaller, the model showed an AUC of 0.833, being on par with the performance of the solid portion size measurements made by the radiologists (AUC, 0.835; p = 0.97). • SSNs with a solid portion measuring > 10 mm on CT showed a high probability of being IPA (positive predictive value, 93.5–100.0%).

To investigate whether CT slice thickness influences the performance of radiomics prognostic models in non-small-cell lung cancer (NSCLC) patients. CT images including 1-, 3-, and 5-mm slice thicknesses acquired from 311 patients who underwent surgical resection for NSCLC between May 2014 and December 2015 were evaluated. Tumor segmentation was performed on CT of each slice thickness and total 94 radiomics features (shape, tumor intensity, and texture) were extracted. The study population was temporally split into development (n = 185) and validation sets (n = 126) for prediction of disease-free survival (DFS). Three radiomics models were built from three different slice thickness datasets (Rad-1, Rad-3, and Rad-5), respectively. Model performance was assessed and compared in three slice thickness datasets and mixed slice thickness dataset using C-indices. In corresponding slice thickness datasets, the C-indices of Rad-1, Rad-3, and Rad-5 for prediction of DFS were 0.68, 0.70, and 0.68 in the development set, and 0.73, 0.73, and 0.76 in the validation set (p = 0.40–0.89 and 0.27–0.90, respectively). Performance of the models was not significantly changed when they were applied to different slice thicknesses data in the validation set (C-index, 0.73–0.76, 0.72–0.73, 0.75–0.76; p = 0.07–0.92). In the mixed slice thickness dataset, performances of the models were similar to or slightly lower than their performances in the corresponding slice thickness datasets (C-index, 0.72–0.75 vs. 0.73–0.76) in the validation set. The performance of radiomics models for predicting DFS in NSCLC patients was not significantly affected by CT slice thickness. • Three radiomics models based on 1-, 3-, and 5-mm CT datasets showed C-indices for predicting disease-free survival of 0.68–0.70 in the development set and 0.73–0.76 in the validation set, without statistical differences (p = 0.27–0.90). • Application of the radiomics models to different slice thickness datasets showed no significant differences in performance between the values in the prediction of disease-free survival (p = 0.07–0.99). • Three radiomics models based on 1-, 3-, and 5-mm CT datasets performed well in mixed slice thickness datasets, showing similar or slightly lower performances.

To apply radiomics analysis for overall survival prediction in chronic obstructive pulmonary disease (COPD), and evaluate the performance of the radiomics signature (RS).This study included 344 patients from the Korean Obstructive Lung Disease (KOLD) cohort. External validation was performed on a cohort of 112 patients. In total, 525 chest CT-based radiomics features were semi-automatically extracted. The five most useful features for survival prediction were selected by least absolute shrinkage and selection operation (LASSO) Cox regression analysis and used to generate a RS. The ability of the RS for classifying COPD patients into high or low mortality risk groups was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis.The five features remaining after the LASSO analysis were %LAAA radiomics approach for survival prediction and risk stratification in COPD patients is feasible, and the constructed radiomics model demonstrated acceptable performance. The RS derived from chest CT data of COPD patients was able to effectively identify those at increased risk of mortality.• A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA

To investigate whether radiomics on iodine overlay maps from dual-energy computed tomography (DECT) can predict survival outcomes in patients with resectable lung cancer. Ninety-three lung cancer patients eligible for curative surgery were examined with DECT at the time of diagnosis. The median follow-up was 60.4 months. Radiomic features of the entire primary tumour were extracted from iodine overlay maps generated by DECT. A Cox proportional hazards regression model was used to determine independent predictors of overall survival (OS) and disease-free survival (DFS), respectively. Forty-two patients (45.2%) had disease recurrence and 39 patients (41.9%) died during the follow-up period. The mean DFS was 49.8 months and OS was 55.2 months. Univariate analysis revealed that significant predictors of both OS and DFS were stage and radiomic parameters, including histogram energy, histogram entropy, grey-level co-occurrence matrix (GLCM) angular second moment, GLCM entropy and homogeneity. The multivariate analysis identified stage and entropy as independent risk factors predicting both OS (stage, hazard ratio (HR) = 2.020 [95% CI 1.014–4.026], p = 0.046; entropy, HR = 1.543 [95% CI 1.069–2.228], p = 0.021) and DFS (stage, HR = 2.132 [95% CI 1.060–4.287], p = 0.034; entropy, HR = 1.497 [95% CI 1.031–2.173], p = 0.034). The C-index showed that adding entropy improved prediction of OS compared to stage only (0.720 and 0.667, respectively; p = 0.048). Radiomic features extracted from iodine overlay map reflecting heterogeneity of tumour perfusion can add prognostic information for patients with resectable lung cancer. • Radiomic feature (histogram entropy) from DECT iodine overlay maps was an independent risk factor predicting both overall survival and disease-free survival. • Adding histogram entropy to clinical stage improved prediction of overall survival compared to stage only (0.720 and 0.667, respectively; p = 0.048). • DECT can be a good option for comprehensive pre-operative evaluation in cases of resectable lung cancer.

To assess the volume doubling time (VDT) of lung cancers in IIP compared with COPD. A total of 61 patients (32 with IIP and 29 with COPD) were identified. A radiologist performed three-dimensional manual segmentation for lung cancers. VDTs were calculated and compared between two groups. Logistic regression was performed to identify factors associated with rapid tumour growth (VDT < 90 days). The median VDT of lung cancers in IIP (78.2 days) was significantly shorter than that in COPD (126.1 days; p=0.004). Squamous cell carcinoma (SqCC) was the most frequent subtype, followed by small cell lung cancer (SCLC) in IIP. In COPD, SqCC was the most frequent subtype, followed by adenocarcinoma. Rapid tumour growth was observed in 20 cancers from IIP, and in nine cancers from COPD (p=0.021). SCLC was significantly correlated with rapid tumour growth (p=0.038). Multivariate analysis revealed that the presence of IIP was the single independent predictor of rapid tumour growth (p = 0.016; odds ratio, 3.7). Lung cancers in IIP showed more rapid growth, with median VDT < 90 days. Therefore, a shorter follow-up interval (

To retrospectively investigate whether the baseline extent and 1-year change in regional disease patterns on CT can predict survival of patients with idiopathic pulmonary fibrosis (IPF). A total of 144 IPF patients with CT scans at the time of diagnosis and 1 year later were included. The extents of five regional disease patterns were quantified using an in-house texture-based automated system. The fibrosis score was defined as the sum of the extent of honeycombing and reticular opacity. The Cox proportional hazard model was used to determine the independent predictors of survival. A total of 106 patients (73.6%) died during the follow-up period. Univariate analysis revealed that age, baseline forced vital capacity, total lung capacity, diffusing capacity of the lung for carbon monoxide, six-minute walk distance, desaturation, honeycombing, reticular opacity, fibrosis score, and interval changes in honeycombing and fibrosis score were significantly associated with survival. Multivariate analysis revealed that age, desaturation, fibrosis score and interval change in fibrosis score were significant independent predictors of survival (p = 0.003

To develop a model for differentiating the predominant subtype-based prognostic groups of lung adenocarcinoma using CT radiomic features, and to validate its performance in comparison with radiologists’ assessments. A total of 993 patients presenting with invasive lung adenocarcinoma between March 2010 and June 2016 were identified. Predominant histologic subtypes were categorized into three groups according to their prognosis (group 0: lepidic; group 1: acinar/papillary; group 2: solid/micropapillary). Seven hundred eighteen radiomic features were extracted from segmented lung cancers on contrast-enhanced CT. A model-development set was formed from the images of 893 patients, while 100 image sets were reserved for testing. A least absolute shrinkage and selection operator method was used for feature selection. Performance of the radiomic model was evaluated using receiver operating characteristic curve analysis, and accuracy on the test set was compared with that of three radiologists with varying experiences (6, 7, and 19 years in chest CT). Our model differentiated the three groups with areas under the curve (AUCs) of 0.892 and 0.895 on the development and test sets, respectively. In pairwise discrimination, the AUC was highest for group 0 vs. 2 (0.984). The accuracy of the model on the test set was higher than the averaged accuracy of the three radiologists without statistical significance (73.0% vs. 61.7%, p = 0.059). For group 2, the model achieved higher PPV than the observers (85.7% vs. 35.0–48.4%). Predominant subtype-based prognostic groups of lung adenocarcinoma were classified by a CT-based radiomic model with comparable performance to radiologists. • A CT-based radiomic model differentiated three prognosis-based subtype groups of lung adenocarcinoma with areas under the curve (AUCs) of 0.892 and 0.895 on development and test sets, respectively. • The CT-based radiomic model showed near perfect discrimination between group 0 and group 2 (AUCs, 0.984–1.000). • The accuracy of the CT-based radiomic model was comparable to the averaged accuracy of the three radiologists with 6, 7, and 19 years of clinical experience in chest CT (73.0% vs. 61.7%, p = 0.059), achieving a higher positive predictive value for group 2 than the observers (85.7% vs. 35.0–48.4%).

To investigate the feasibility of a deep learning–based detection (DLD) system for multiclass lesions on chest radiograph, in comparison with observers. A total of 15,809 chest radiographs were collected from two tertiary hospitals (7204 normal and 8605 abnormal with nodule/mass, interstitial opacity, pleural effusion, or pneumothorax). Except for the test set (100 normal and 100 abnormal (nodule/mass, 70; interstitial opacity, 10; pleural effusion, 10; pneumothorax, 10)), radiographs were used to develop a DLD system for detecting multiclass lesions. The diagnostic performance of the developed model and that of nine observers with varying experiences were evaluated and compared using area under the receiver operating characteristic curve (AUROC), on a per-image basis, and jackknife alternative free-response receiver operating characteristic figure of merit (FOM) on a per-lesion basis. The false-positive fraction was also calculated. Compared with the group-averaged observations, the DLD system demonstrated significantly higher performances on image-wise normal/abnormal classification and lesion-wise detection with pattern classification (AUROC, 0.985 vs. 0.958; p = 0.001; FOM, 0.962 vs. 0.886; p < 0.001). In lesion-wise detection, the DLD system outperformed all nine observers. In the subgroup analysis, the DLD system exhibited consistently better performance for both nodule/mass (FOM, 0.913 vs. 0.847; p < 0.001) and the other three abnormal classes (FOM, 0.995 vs. 0.843; p < 0.001). The false-positive fraction of all abnormalities was 0.11 for the DLD system and 0.19 for the observers. The DLD system showed the potential for detection of lesions and pattern classification on chest radiographs, performing normal/abnormal classifications and achieving high diagnostic performance. • The DLD system was feasible for detection with pattern classification of multiclass lesions on chest radiograph. • The DLD system had high performance of image-wise classification as normal or abnormal chest radiographs (AUROC, 0.985) and showed especially high specificity (99.0%). • In lesion-wise detection of multiclass lesions, the DLD system outperformed all 9 observers (FOM, 0.962 vs. 0.886; p < 0.001).

To retrospectively evaluate the diagnostic performance of a convolutional neural network (CNN) model in detecting pneumothorax on chest radiographs obtained after percutaneous transthoracic needle biopsy (PTNB) for pulmonary lesions. A CNN system for computer-aided diagnosis on chest radiographs was developed using the full 26-layer You Only Look Once model. A total of 1596 chest radiographs with pneumothorax were used for training. To validate the clinical feasibility of this model, follow-up chest radiographs obtained after PTNB for 1333 pulmonary lesions in 1319 patients in 2016 were prepared as an independent test set. Two experienced radiologists determined the presence of pneumothorax by consensus. The diagnostic performance of the CNN model was assessed using the jackknife free-response receiver operating characteristic method. The incidence of pneumothorax was 17.9% (247/1379) on 3-h follow-up chest radiographs and 23.3% (309/1329) on 1-day follow-up chest radiographs. Twenty-three (1.7% of all PTNBs) cases required drainage catheter insertion. Our approach had a sensitivity, a specificity, and an area under the curve (AUC), respectively, of 61.1% (151/247), 93.0% (1053/1132), and 0.898 for 3-h follow-up chest radiographs and 63.4% (196/309), 93.5% (954/1020), and 0.905 for 1-day follow-up chest radiographs. The overall accuracy was 87.3% (1204/1379) for 3-h follow-up radiographs and 86.5% (1150/1329) for 1-day follow-up radiographs. The CNN model found all 23 cases of pneumothorax requiring drainage. Our CNN model had good performance for detection of pneumothorax on chest radiographs after PTNB, especially for those requiring further procedures. It can be used as a screening tool prior to radiologist interpretation. • The CNN model had good performance for detection of pneumothorax on chest radiographs after PTNB and showed high specificity and negative predictive value. • The CNN model found all cases of pneumothorax requiring drainage after PTNB. • The CNN model can be used as a screening tool prior to radiologist interpretation.

To investigate whether morphometric complexity in the lung can predict survival and act as a new prognostic marker in patients with chronic obstructive pulmonary disease (COPD). COPD (n = 302) patients were retrospectively reviewed. All patients underwent volumetric computed tomography and pulmonary function tests at enrollment (2005–2015). For complexity analysis, we applied power law exponent of the emphysema size distribution (Dsize) as well as box-counting fractal dimension (Dbox3D) analysis. Patients’ survival at February 2017 was ascertained. Univariate and multivariate Cox proportional hazards analyses were performed, and prediction performances of various combinatorial models were compared. Patients were 66 ± 6 years old, had 41 ± 28 pack-years’ smoking history and variable GOLD stages (n = 20, 153, 108 and 21 in stages I−IV). The median follow-up time was 6.1 years (range: 0.2−11.6 years). Sixty-three patients (20.9%) died, of whom 35 died of lung-related causes. In univariate Cox analysis, lower Dsize and Dbox3D were significantly associated with both all-cause and lung-related mortality (both p < 0.001). In multivariate analysis, the backward elimination method demonstrated that Dbox3D, along with age and the BODE index, was an independent predictor of survival (p = 0.014; HR, 2.08; 95% CI, 1.16–3.71). The contributions of Dsize and Dbox3D to the combinatorial survival model were comparable with those of the emphysema index and lung-diffusing capacity. Low morphometric complexity in the lung is a predictor of survival in patients with COPD. • A newly suggested method for quantifying lung morphometric complexity is feasible. • Morphometric complexity measured on chest CT images predicts COPD patients’ survival. • Complexity, diffusing capacity and emphysema index contribute similarly to the survival model.

We retrospectively evaluated the doubling time (DT) of thymic epithelial tumours (TET) according to the histological subtype on CT.From January 2005 to June 2016, we enrolled 53 patients who had pathologically confirmed TET and at least two CT scans. Tumour size was measured using a two-dimensional method, and the DT was calculated. DTs were compared among histological subtypes, and factors associated with rapid tumour growth (DT180 days) were assessed.In 42 of the 53 patients (79.2%) the tumours showed interval growth (2 mm) during follow-up. The median DT for all tumours was 400 days (range 48-1,964 days). There were no significant differences in DT in relation to histological subtype (p = 0.177). When TETs were recategorized into three groups, i.e. low-risk thymomas (types A, AB, B1), high-risk thymomas (types B2, B3), and thymic carcinoma, DT was significantly different among the groups (median DT 436, 381 and 189 days, respectively; p = 0.031). Histological subtype (type B3 and thymic carcinoma) was the single independent predictor of rapid tumour growth.The majority of TETs grew during follow-up with variable and relatively slow growth rates. Histological features of aggressive behaviour significantly correlated with a decreased DT and rapid growth.• The majority of thymic epithelial tumours grew during follow-up (79.2%, 42/53). • Doubling times of thymic epithelial tumours were highly variable (median 400 days). • Histological features of aggressive behaviour significantly correlated with a decreased doubling time.

To compare the parenchymal attenuation change between inspiration/expiration CTs with dynamic ventilation change between xenon wash-in (WI) inspiration and wash-out (WO) expiration CTs.52 prospectively enrolled COPD patients underwent xenon ventilation dual-energy CT during WI and WO periods and pulmonary function tests (PFTs). The parenchymal attenuation parameters (emphysema index (EI), gas-trapping index (GTI) and air-trapping index (ATI)) and xenon ventilation parameters (xenon in WI (Xe-WI), xenon in WO (Xe-WO) and xenon dynamic (Xe-Dyna)) of whole lung and three divided areas (emphysema, hyperinflation and normal) were calculated on virtual non-contrast images and ventilation images. Pearson correlation, linear regression analysis and one-way ANOVA were performed.EI, GTI and ATI showed a significant correlation with Xe-WI, Xe-WO and Xe-Dyna (EI R = -.744, -.562, -.737; GTI R = -.621, -.442, -.629; ATI R = -.600, -.421, -.610, respectively, p 0.01). All CT parameters showed significant correlation with PFTs except forced vital capacity (FVC). There was a significant difference in GTI, ATI and Xe-Dyna in each lung area (p 0.01).The parenchymal attenuation change between inspiration/expiration CTs and xenon dynamic change between xenon WI- and WO-CTs correlate significantly. There are alterations in the dynamics of xenon ventilation between areas of emphysema.• The xenon ventilation change correlates with the parenchymal attenuation change. • The xenon ventilation change shows the difference between three lung areas. • The combination of attenuation and xenon can predict more accurate PFTs.

To validate a method for performing myocardial segmentation based on coronary anatomy using coronary CT angiography (CCTA). Coronary artery-based myocardial segmentation (CAMS) was developed for use with CCTA. To validate and compare this method with the conventional American Heart Association (AHA) classification, a single coronary occlusion model was prepared and validated using six pigs. The unstained occluded coronary territories of the specimens and corresponding arterial territories from CAMS and AHA segmentations were compared using slice-by-slice matching and 100 virtual myocardial columns. CAMS more precisely predicted ischaemic area than the AHA method, as indicated by 95% versus 76% (p

To investigate the optimal threshold of subtraction method for quantification of air trapping on co-registered CT in COPD patients in correlation with pulmonary function parameters. From June 2005 to October 2010, 174 patients were included in our study. Inspiration and expiration CT were performed followed by non-rigid registration using in-house software. The subtraction value per voxel between inspiration and registered expiration CT was obtained, and volume fraction of air trapping (air trapping index, ATI), using variable thresholds was calculated. ATI, expiration/inspiration ratio of mean lung density (E/I MLD) and the percentage of lung voxels below −856 HU on expiration CT (Exp−856) were correlated with FEF25–75% and RV/TLC. The highest correlation coefficient with FEF25–75% was −0.656, using the threshold of 80 HU. As for RV/TLC, the highest correlation coefficient was 0.664, using the threshold of 30 HU. When plotting the relationship between subtraction thresholds and FEF25–75% and RV/TLC, the threshold of 60 HU was most suitable (r = −0.649 and 0.651). Those correlation coefficients were comparable to the results with E/I MLD (r = −0.670 and 0.657) and Exp−856 (r = −0.604 and 0.565). The optimal threshold for quantification of air trapping was 60 HU and showed comparable correlations with pulmonary function parameters. • The optimal CT threshold of subtraction method for air trapping was 60 HU. • ATI with 60 HU threshold was comparable to E/I MLD and Exp −856 . • Emphysema may substantially contribute to air trapping with statistical significance (P

To evaluate automated texture-based segmentation of dual-energy CT (DECT) images in diffuse interstitial lung disease (DILD) patients and prognostic stratification by overlapping morphologic and perfusion information of total lung. Suspected DILD patients scheduled for surgical biopsy were prospectively included. Texture patterns included ground-glass opacity (GGO), reticulation and consolidation. Pattern- and perfusion-based CT measurements were assessed to extract quantitative parameters. Accuracy of texture-based segmentation was analysed. Correlations between CT measurements and pulmonary function test or 6-minute walk test (6MWT) were calculated. Parameters of idiopathic pulmonary fibrosis/usual interstitial pneumonia (IPF/UIP) and non-IPF/UIP were compared. Survival analysis was performed. Overall accuracy was 90.47 % for whole lung segmentation. Correlations between mean iodine values of total lung, 50–97.5th (%) attenuation and forced vital capacity or 6MWT were significant. Volume of GGO, reticulation and consolidation had significant correlation with DLco or SpO2 on 6MWT. Significant differences were noted between IPF/UIP and non-IPF/UIP in 6MWT distance, mean iodine value of total lung, 25–75th (%) attenuation and entropy. IPF/UIP diagnosis, GGO ratio, DILD extent, 25–75th (%) attenuation and SpO2 on 6MWT showed significant correlations with survival. DECT combined with pattern analysis is useful for analysing DILD and predicting survival by provision of morphology and enhancement. • Dual-energy CT (DECT) produces morphologic and parenchymal enhancement information. • Automated lung segmentation enables analysis of disease extent and severity. • This prospective study showed value of DECT in DILD patients. • Parameters on DECT enable characterization and survival prediction of DILD.

One objective was to evaluate the air trapping index (ATI), measured by inspiration/expiration CT, in COPD patients and nonsmokers. Another objective was to assess the association between the pulmonary function test (PFT) and CT parameters such as ATI or other indices, separately in the whole lung, in emphysema, and in hyperinflated and normal lung areas. One hundred and thirty-eight COPD patients and 29 nonsmokers were included in our study. The ATI, the emphysema index (EI), the gas trapping index (Exp -856) and expiration/inspiration ratio of mean lung density (E/Iratio of MLD) were measured on CT. The values of the whole lung, of emphysema, and of hyperinflated and normal lung areas were compared and then correlated with various PFT parameters. Compared with nonsmokers, COPD patients showed a higher ATI in the whole lung and in each lung lesion (all P

To evaluate the usefulness of a texture-based automated quantification system (AQS) for evaluating the extent and interval change of regional disease patterns on initial and follow-up high-resolution computed tomographies (HRCTs) of fibrotic interstitial pneumonia (FIP).Eighty-nine patients with clinically and/or biopsy confirmed usual interstitial pneumonia (UIP) (n = 71) and non-specific interstitial pneumonia (NSIP) (n = 18) were included. An AQS to quantify five disease patterns (ground-glass opacity [GGO], reticular opacity [RO], honeycombing [HC], emphysema [EMPH], consolidation [CONS]) and normal lung was developed. The extent and interval changes of each disease pattern, FS (fibrosis score), TA (total abnormal lung fraction) of entire lung on initial and 1-year follow-up HRCTs were quantified. The agreement between the results of AQS and two readers was assessed. Results of AQS were correlated with forced vital capacity (FVC) and carbon monoxide diffusing capacity (DLco).The Intraclass correlation coefficient (ICC) study revealed acceptable agreement between visual assessment and AQS (r = 0.78, 0.66 for HC; 0.76, 0.61 for FS; 0.64, 0.68 for TA, initial and follow-up HRCTs, respectively). Linear regression analysis revealed the extent of HC, TA on initial CT, interval changes of FS contributed negatively to DLco, and interval changes of FS, TA contributed negatively to FVC.Our AQS is comparable with visual assessment for evaluating the disease extent and the interval changes of FIP on HRCT.

To evaluate the sensitivity of computer-aided detection(CAD) and dual-energy software(‘Lung PBV’, ‘Lung Vessels’) for detecting peripheral pulmonary embolism(PE). Between Jan-2007 and Jan-2008, 309 patients underwent dual-energy CT angiography(DECTA) for the evaluation of suspected PE. Among them, 37 patients were retrospectively selected; 21 with PE at segmental-or-below levels and 16 without PE according to clinical reports. A standard computer assisted detection (CAD) package and two new types of software(‘Lung PBV’, ‘Lung Vessels’) were applied on a dedicated workstation. This resulted in four alternative tests for detecting PE: DECTA alone and DECTA with CAD, ‘Lung Vessels’ and ‘Lung PBV’. Two radiologists independently read all cases at different reading sessions. Two thoracic radiologists set the reference standard by combining all information from DECTA and software. The sensitivity of detection for all, segmental and subsegmental-or-below PE were assessed. The reference standard contained 136 PE(segmental 65, subsegmental-or-below 71). With DECTA alone, the sensitivity of detection for all, segmental and subsegmental-or-below pulmonary arteries was 54.5%/73.7%/34.4%; DECTA with CAD, 57.8%/76.8%/37.9%; DECTA with ‘Lung PBV’, 61.1%/79.9%/41.4%; DECTA with ‘Lung Vessels’, 64.0%/78.3%/48.5% respectively. The use of CAD, Lung Vessels and Lung PBV shows improved capability to detect peripheral PE.

Our objective was to ascertain whether displacement of the aortic knob on chest radiographs could be used as a sign to detect a left paratracheal esophageal mass. Sixty-one consecutive pathologically proven esophageal cancer patients were included in this study according to the following criteria: tumor at the aortic arch level; chest radiographs in the neutral position; no unilateral volume loss in the lung. Sixty-one sex- and age-matched subjects served as the control group. To measure the extent of displacement of the aortic arch, we drew a circle over the aortic knob, fitting more than one-quarter of the circumference on the chest radiograph. The distance between the medial end of the circle and the left margin of the trachea (aortic displacement value, ADV) was measured. The difference of the ADV between the study group and the control group was analyzed using a paired t test. The aortic displacement value was significantly larger in the study group (11.7+/-4.5 mm) than in the control group (5.6+/-2.9 mm). When we applied 10 mm as a threshold level, sensitivity and specificity on detection of esophageal cancer were 78.7 and 78.7%, respectively. Displacement of the aortic knob may be a useful sign to indicate a left paratracheal esophageal mass.