Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology:A Comprehensive Review of Solutions Beyond Supervised Learning

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.

Generative Adversarial Network-Based Image Conversion Among Different Computed Tomography Protocols and Vendors: Effects on Accuracy and Variability in Quantifying Regional Disease Patterns of Interstitial Lung Disease

Objective@#To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. @*Materials and Methods@#This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1–7 according to acquisition conditions. CT images in groups 2–7 were converted into the target CT sty le (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. @*Results@#Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2–7 improved after CT conversion (original vs. converted: 0.63vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists’ scores were significantly higher (P < 0.001) and less variable on converted CT. @*Conclusion@#CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.

Prognosis for Pneumonic-Type Invasive Mucinous Adenocarcinoma in a Single Lobe on CT: Is It Reasonable to Designate It as Clinical T3?

Objective@#To compare pneumonic-type invasive mucinous adenocarcinoma (pIMA) confined to a single lobe with clinical T2, T3, and T4 stage lung cancer without pathological node metastasis regarding survival after curative surgery and to identify prognostic factors for pIMA. @*Materials and Methods@#From January 2010 to December 2017, 41 patients (15 male; mean age ± standard deviation, 66.0 ± 9.9 years) who had pIMA confined to a single lobe on computed tomography (CT) and underwent curative surgery were identified in two tertiary hospitals. Three hundred and thirteen patients (222 male; 66.3 ± 9.4 years) who had non-small cell lung cancer (NSCLC) without pathological node metastasis and underwent curative surgery in one participating institution formed a reference group. Relapse-free survival (RFS) and overall survival (OS) were calculated using the Kaplan–Meier method.Cox proportional hazard regression analysis was performed to identify factors associated with the survival of patients with pIMA. @*Results@#The 5-year RFS and OS rates in patients with pIMA were 33.1% and 56.0%, respectively, compared with 74.3% and 91%, 64.3% and 71.8%, and 46.9% and 49.5% for patients with clinical stage T2, T3, and T4 NSCLC in the reference group, respectively. The RFS of patients with pIMA was comparable to that of patients with clinical stage T4 NSCLC and significantly worse than that of patients with clinical stage T3 NSCLC (p = 0.012). The differences in OS between patients with pIMA and those with clinical stage T3 or T4 NSCLC were not significant (p = 0.11 and p = 0.37, respectively). In patients with pIMA, the presence of separate nodules was a significant factor associated with poor RFS and OS {unadjusted hazard ratio (HR), 4.66 (95% confidence interval [CI], 1.95–11.11), p < 0.001 for RFS; adjusted HR, 4.53 (95% CI, 1.59–12.89), p = 0.005 for OS}. @*Conclusion@#The RFS of patients with pIMA was comparable to that of patients with clinical stage T4 lung cancer. Separate nodules on CT were associated with poor RFS and OS in patients with pIMA.

New Method for Combined Quantitative Assessment of Air-Trapping and Emphysema on Chest Computed Tomography in Chronic Obstructive Pulmonary Disease: Comparison with Parametric Response Mapping

Objective@#Emphysema and small-airway disease are the two major components of chronic obstructive pulmonary disease (COPD). We propose a novel method of quantitative computed tomography (CT) emphysema air-trapping composite (EAtC) mapping to assess each COPD component. We analyzed the potential use of this method for assessing lung function in patients with COPD. @*Materials and Methods@#A total of 584 patients with COPD underwent inspiration and expiration CTs. Using pairwise analysis of inspiration and expiration CTs with non-rigid registration, EAtC mapping classified lung parenchyma into three areas: Normal, functional air trapping (fAT), and emphysema (Emph). We defined fAT as the area with a density change of less than 60 Hounsfield units (HU) between inspiration and expiration CTs among areas with a density less than -856 HU on inspiration CT. The volume fraction of each area was compared with clinical parameters and pulmonary function tests (PFTs). The results were compared with those of parametric response mapping (PRM) analysis. @*Results@#The relative volumes of the EAtC classes differed according to the Global Initiative for Chronic Obstructive Lung Disease stages (p < 0.001). Each class showed moderate correlations with forced expiratory volume in 1 second (FEV 1) and FEV 1/forced vital capacity (FVC) (r = -0.659–0.674, p < 0.001). Both fAT and Emph were significant predictors of FEV 1 and FEV 1/FVC (R2 = 0.352 and 0.488, respectively; p < 0.001). fAT was a significant predictor of mean forced expiratory flow between 25% and 75% and residual volume/total vital capacity (R2 = 0.264 and 0.233, respectively; p < 0.001), while Emph and age were significant predictors of carbon monoxide diffusing capacity (R2 = 0.303; p < 0.001). fAT showed better correlations with PFTs than with small-airway disease on PRM. @*Conclusion@#The proposed quantitative CT EAtC mapping provides comprehensive lung functional information on each disease component of COPD, which may serve as an imaging biomarker of lung function.

New Method for Combined Quantitative Assessment of Air-Trapping and Emphysema on Chest Computed Tomography in Chronic Obstructive Pulmonary Disease: Comparison with Parametric Response Mapping

Objective@#Emphysema and small-airway disease are the two major components of chronic obstructive pulmonary disease (COPD). We propose a novel method of quantitative computed tomography (CT) emphysema air-trapping composite (EAtC) mapping to assess each COPD component. We analyzed the potential use of this method for assessing lung function in patients with COPD. @*Materials and Methods@#A total of 584 patients with COPD underwent inspiration and expiration CTs. Using pairwise analysis of inspiration and expiration CTs with non-rigid registration, EAtC mapping classified lung parenchyma into three areas: Normal, functional air trapping (fAT), and emphysema (Emph). We defined fAT as the area with a density change of less than 60 Hounsfield units (HU) between inspiration and expiration CTs among areas with a density less than -856 HU on inspiration CT. The volume fraction of each area was compared with clinical parameters and pulmonary function tests (PFTs). The results were compared with those of parametric response mapping (PRM) analysis. @*Results@#The relative volumes of the EAtC classes differed according to the Global Initiative for Chronic Obstructive Lung Disease stages (p < 0.001). Each class showed moderate correlations with forced expiratory volume in 1 second (FEV 1) and FEV 1/forced vital capacity (FVC) (r = -0.659–0.674, p < 0.001). Both fAT and Emph were significant predictors of FEV 1 and FEV 1/FVC (R2 = 0.352 and 0.488, respectively; p < 0.001). fAT was a significant predictor of mean forced expiratory flow between 25% and 75% and residual volume/total vital capacity (R2 = 0.264 and 0.233, respectively; p < 0.001), while Emph and age were significant predictors of carbon monoxide diffusing capacity (R2 = 0.303; p < 0.001). fAT showed better correlations with PFTs than with small-airway disease on PRM. @*Conclusion@#The proposed quantitative CT EAtC mapping provides comprehensive lung functional information on each disease component of COPD, which may serve as an imaging biomarker of lung function.

Content-Based Image Retrieval of Chest CT with Convolutional Neural Network for Diffuse Interstitial Lung Disease: Performance Assessment in Three Major Idiopathic Interstitial Pneumonias

Objective@#To assess the performance of content-based image retrieval (CBIR) of chest CT for diffuse interstitial lung disease (DILD). @*Materials and Methods@#The database was comprised by 246 pairs of chest CTs (initial and follow-up CTs within two years) from 246 patients with usual interstitial pneumonia (UIP, n = 100), nonspecific interstitial pneumonia (NSIP, n = 101), and cryptogenic organic pneumonia (COP, n = 45). Sixty cases (30-UIP, 20-NSIP, and 10-COP) were selected as the queries. The CBIR retrieved five similar CTs as a query from the database by comparing six image patterns (honeycombing, reticular opacity, emphysema, ground-glass opacity, consolidation and normal lung) of DILD, which were automatically quantified and classified by a convolutional neural network. We assessed the rates of retrieving the same pairs of query CTs, and the number of CTs with the same disease class as query CTs in top 1–5 retrievals. Chest radiologists evaluated the similarity between retrieved CTs and queries using a 5-scale grading system (5-almost identical; 4-same disease; 3-likelihood of same disease is half; 2-likely different; and 1-different disease). @*Results@#The rate of retrieving the same pairs of query CTs in top 1 retrieval was 61.7% (37/60) and in top 1–5 retrievals was 81.7% (49/60). The CBIR retrieved the same pairs of query CTs more in UIP compared to NSIP and COP (p = 0.008 and 0.002).On average, it retrieved 4.17 of five similar CTs from the same disease class. Radiologists rated 71.3% to 73.0% of the retrieved CTs with a similarity score of 4 or 5. @*Conclusion@#The proposed CBIR system showed good performance for retrieving chest CTs showing similar patterns for DILD.

Visual and Quantitative Assessments of Regional Xenon-Ventilation Using Dual-Energy CT in Asthma-ChronicObstructive Pulmonary Disease Overlap Syndrome:A Comparison with Chronic Obstructive PulmonaryDisease

Objective@#To assess the regional ventilation in patients with asthma-chronic obstructive pulmonary disease (COPD) overlapsyndrome (ACOS) using xenon-ventilation dual-energy CT (DECT), and to compare it to that in patients with COPD. @*Materials and Methods@#Twenty-one patients with ACOS and 46 patients with COPD underwent xenon-ventilation DECT. Theventilation abnormalities were visually determined to be 1) peripheral wedge/diffuse defect, 2) diffuse heterogeneous defect,3) lobar/segmental/subsegmental defect, and 4) no defect on xenon-ventilation maps. Emphysema index (EI), airway wallthickness (Pi10), and mean ventilation values in the whole lung, peripheral lung, and central lung areas were quantified andcompared between the two groups using the Student’s t test. @*Results@#Most patients with ACOS showed the peripheral wedge/diffuse defect (n = 14, 66.7%), whereas patients with COPDcommonly showed the diffuse heterogeneous defect and lobar/segmental/subsegmental defect (n = 21, 45.7% and n = 20,43.5%, respectively). The prevalence of ventilation defect patterns showed significant intergroup differences (p< 0.001). Thequantified ventilation values in the peripheral lung areas were significantly lower in patients with ACOS than in patients withCOPD (p= 0.045). The quantified Pi10 was significantly higher in patients with ACOS than in patients with COPD (p= 0.041);however, EI was not significantly different between the two groups. @*Conclusion@#The ventilation abnormalities on the visual and quantitative assessments of xenon-ventilation DECT differed betweenpatients with ACOS and patients with COPD. Xenon-ventilation DECT may demonstrate the different physiologic changes ofpulmonary ventilation in patients with ACOS and COPD.

Quantitative Vertebral Bone Density Seen on Chest CT in Chronic Obstructive Pulmonary Disease Patients: Association with Mortality in the Korean Obstructive Lung Disease Cohort

Objective@#Patients with chronic obstructive pulmonary disease (COPD) are known to be at risk of osteoporosis. The purpose of this study was to evaluate the association between thoracic vertebral bone density measured on chest CT (DThorax) and clinical variables, including survival, in patients with COPD. @*Materials and Methods@#A total of 322 patients with COPD were selected from the Korean Obstructive Lung Disease (KOLD) cohort. DThorax was measured by averaging the CT values of three consecutive vertebral bodies at the level of the left main coronary artery with a round region of interest as large as possible within the anterior column of each vertebral body using an in-house software. Associations between DThorax and clinical variables, including survival, pulmonary function test (PFT) results, and CT densitometry, were evaluated. @*Results@#The median follow-up time was 7.3 years (range: 0.1–12.4 years). Fifty-six patients (17.4%) died. DThroax differed significantly between the different Global Initiative for Chronic Obstructive Lung Disease stages. DThroax correlated positively with body mass index (BMI), some PFT results, and the six-minute walk distance, and correlated negatively with the emphysema index (EI) (all p < 0.05). In the univariate Cox analysis, older age (hazard ratio [HR], 3.617; 95% confidence interval [CI], 2.119–6.173, p < 0.001), lower BMI (HR, 3.589; 95% CI, 2.122–6.071, p < 0.001), lower forced expiratory volume in one second (FEV1) (HR, 2.975; 95% CI, 1.682–5.262, p < 0.001), lower diffusing capacity of the lung for carbon monoxide corrected with hemoglobin (DLCO) (HR, 4.595; 95% CI, 2.665–7.924, p < 0.001), higher EI (HR, 3.722; 95% CI, 2.192–6.319, p < 0.001), presence of vertebral fractures (HR, 2.062; 95% CI, 1.154–3.683, p = 0.015), and lower DThorax (HR, 2.773; 95% CI, 1.620–4.746, p < 0.001) were significantly associated with all-cause mortality and lung-related mortality. In the multivariate Cox analysis, lower DThorax (HR, 1.957; 95% CI, 1.075–3.563, p = 0.028) along with older age, lower BMI, lower FEV1, and lower DLCO were independent predictors of all-cause mortality. @*Conclusion@#The thoracic vertebral bone density measured on chest CT demonstrated significant associations with the patients’ mortality and clinical variables of disease severity in the COPD patients included in KOLD cohort.

Artificial Intelligence in Health Care: Current Applications and Issues

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low;moreover, there are various concerns regarding the safety and reliability of AI technologyimplementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.

A Review of Three-Dimensional Printing Technology for Medical Applications

Three-dimensional (3D) printing technology, with additive manufacturing, can aid in the production of various kinds of patient-specific medical devices and implants in medical fields, which cannot be covered by mass production systems for producing conventional devices/implants. The simulator-based medical image demonstrates the anatomical structure of the disease, which can be used for education, diagnosis, preparation of treatment plan and preoperative surgical guide, etc. The surgical guide is used as a patient-specific medical device for guiding incision, resection, insertion, and marking. As 3D printers can output materials that can be inserted into the human body, the patient-specific implant device that reflects the patient's anatomy and surgical plan could be of relevance. In addition, patient-specific aids, including gibs, splints, prostheses, and epitheses, could be used for a better outcome. Finally, bio-printing is also used to cultivate cells to produce functional artificial tissues.

The Role of medical doctor in the era of artificial intelligence

Recent advances in new technologies such as artificial intelligence, big data, and virtual reality have led to significant innovations in various industries. Artificial intelligence, particularly in applications using deep learning algorithms, has shown performance superior to that of humans in several contexts. Accordingly, many researchers and companies have tried to apply artificial intelligence to the healthcare system, with applications including image interpretation, voice recognition, clinical decision support, risk prediction, drug discovery, medical robotics, and workflow improvement. However, several important technical, ethical, and social barriers must be overcome, such as overfitting, lack of interpretability, privacy, security, and safety. Doctors should be prepared to play a key role in applying artificial intelligence through the full course of development, validation, clinical performance, and monitoring.

Utility of Computed Tomography in a Differential Diagnosis for the Patients with an Initial Diagnosis of Chronic Obstructive Pulmonary Disease Exacerbation / 결핵

BACKGROUND: The utility of computed tomography (CT) in the differential diagnosis of patients with chronic obstructive pulmonary disease (COPD) exacerbation remains uncertain. However, due to the low cost associated with CT scan along with the impact of Koreas' health insurance system, there has been a rise in the number of CT scans in the patients with initial diagnosis of COPD exacerbations. Therefore, the utility of CT in the differential diagnosis was investigated to determine whether performing CT scans affect the clinical outcomes of the patients with an initial diagnosis of COPD exacerbation. METHODS: This study involved 202 COPD patients hospitalized with an initial diagnosis of COPD exacerbation. We evaluated the change in diagnosis or treatment after performing a CT scan, and compared the clinical outcomes of patient groups with vs. without performing CT (non-CT group vs. CT group). RESULTS: After performing CT, the diagnosis was changed for two (3.0%) while additional diagnoses were made for 27 of the 64 patients (42.1%). However, the treatment changed for only one (1.5%), and six patients (9.3%) received supplementary medication. There were no difference in the median length of hospital stay (8 [6–13] days vs. 8 [6–12] days, p=0.786) and intensive care unit care (14 [10.1%] vs. 11 [16.7%], p=0.236) between the CT and non-CT groups, respectively. These findings remained consistent even after the propensity score matching. CONCLUSION: Utility of CT in patients with acute COPD exacerbation might not be helpful; therefore, we do not recommend chest CT scan as a routine initial diagnostic tool.

The Role of medical doctor in the era of artificial intelligence

Recent advances in new technologies such as artificial intelligence, big data, and virtual reality have led to significant innovations in various industries. Artificial intelligence, particularly in applications using deep learning algorithms, has shown performance superior to that of humans in several contexts. Accordingly, many researchers and companies have tried to apply artificial intelligence to the healthcare system, with applications including image interpretation, voice recognition, clinical decision support, risk prediction, drug discovery, medical robotics, and workflow improvement. However, several important technical, ethical, and social barriers must be overcome, such as overfitting, lack of interpretability, privacy, security, and safety. Doctors should be prepared to play a key role in applying artificial intelligence through the full course of development, validation, clinical performance, and monitoring.

Prediction of Pulmonary Function in Patients with Chronic Obstructive Pulmonary Disease: Correlation with Quantitative CT Parameters

OBJECTIVE: We aimed to evaluate correlations between computed tomography (CT) parameters and pulmonary function test (PFT) parameters according to disease severity in patients with chronic obstructive pulmonary disease (COPD), and to determine whether CT parameters can be used to predict PFT indices. MATERIALS AND METHODS: A total of 370 patients with COPD were grouped based on disease severity according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) I–IV criteria. Emphysema index (EI), air-trapping index, and airway parameters such as the square root of wall area of a hypothetical airway with an internal perimeter of 10 mm (Pi10) were measured using automatic segmentation software. Clinical characteristics including PFT results and quantitative CT parameters according to GOLD criteria were compared using ANOVA. The correlations between CT parameters and PFT indices, including the ratio of forced expiratory volume in one second to forced vital capacity (FEV1/FVC) and FEV1, were assessed. To evaluate whether CT parameters can be used to predict PFT indices, multiple linear regression analyses were performed for all patients, Group 1 (GOLD I and II), and Group 2 (GOLD III and IV). RESULTS: Pulmonary function deteriorated with increase in disease severity according to the GOLD criteria (p < 0.001). Parenchymal attenuation parameters were significantly worse in patients with higher GOLD stages (p < 0.001), and Pi10 was highest for patients with GOLD III (4.41 ± 0.94 mm). Airway parameters were nonlinearly correlated with PFT results, and Pi10 demonstrated mild correlation with FEV1/FVC in patients with GOLD II and III (r = 0.16, p = 0.06 and r = 0.21, p = 0.04, respectively). Parenchymal attenuation parameters, airway parameters, EI, and Pi10 were identified as predictors of FEV1/FVC for the entire study sample and for Group 1 (R2 = 0.38 and 0.22, respectively; p < 0.001). However, only parenchymal attenuation parameter, EI, was identified as a predictor of FEV1/FVC for Group 2 (R2 = 0.37, p < 0.001). Similar results were obtained for FEV1. CONCLUSION: Airway and parenchymal attenuation parameters are independent predictors of pulmonary function in patients with mild COPD, whereas parenchymal attenuation parameters are dominant independent predictors of pulmonary function in patients with severe COPD.

CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network

OBJECTIVE: The aim of our study was to develop and validate a convolutional neural network (CNN) architecture to convert CT images reconstructed with one kernel to images with different reconstruction kernels without using a sinogram. MATERIALS AND METHODS: This retrospective study was approved by the Institutional Review Board. Ten chest CT scans were performed and reconstructed with the B10f, B30f, B50f, and B70f kernels. The dataset was divided into six, two, and two examinations for training, validation, and testing, respectively. We constructed a CNN architecture consisting of six convolutional layers, each with a 3 × 3 kernel with 64 filter banks. Quantitative performance was evaluated using root mean square error (RMSE) values. To validate clinical use, image conversion was conducted on 30 additional chest CT scans reconstructed with the B30f and B50f kernels. The influence of image conversion on emphysema quantification was assessed with Bland–Altman plots. RESULTS: Our scheme rapidly generated conversion results at the rate of 0.065 s/slice. Substantial reduction in RMSE was observed in the converted images in comparison with the original images with different kernels (mean reduction, 65.7%; range, 29.5–82.2%). The mean emphysema indices for B30f, B50f, converted B30f, and converted B50f were 5.4 ± 7.2%, 15.3 ± 7.2%, 5.9 ± 7.3%, and 16.8 ± 7.5%, respectively. The 95% limits of agreement between B30f and other kernels (B50f and converted B30f) ranged from −14.1% to −2.6% (mean, −8.3%) and −2.3% to 0.7% (mean, −0.8%), respectively. CONCLUSION: CNN-based CT kernel conversion shows adequate performance with high accuracy and speed, indicating its potential clinical use.

Prediction of Treatment Response in Patients with Chronic Obstructive Pulmonary Disease by Determination of Airway Dimensions with Baseline Computed Tomography

OBJECTIVE: To determine the predictive factors for treatment responsiveness in patients with chronic obstructive pulmonary disease (COPD) at 1-year follow-up by performing quantitative analyses of baseline CT scans. MATERIALS AND METHODS: COPD patients (n = 226; 212 men, 14 women) were recruited from the Korean Obstructive Lung Disease cohort. Patients received a combination of inhaled long-acting beta-agonists and corticosteroids twice daily for 3 months and subsequently received medications according to the practicing clinician's decision. The emphysema index, air-trapping indices, and airway parameter (Pi10), calculated using both full-width-half-maximum and integral-based half-band (IBHB) methods, were obtained with baseline CT scans. Clinically meaningful treatment response was defined as an absolute increase of ≥ 0.225 L in the forced expiratory volume in 1 second (FEV1) at the one-year follow-up. Multivariate logistic regression analysis was performed to investigate the predictors of an increase in FEV1, and receiver operating characteristic (ROC) analysis was performed to evaluate the performance of the suggested models. RESULTS: Treatment response was noted in 47 patients (20.8%). The mean FEV1 increase in responders was 0.36 ± 0.10 L. On univariate analysis, the air-trapping index (ATI) obtained by the subtraction method, ATI of the emphysematous area, and IBHB-measured Pi10 parameter differed significantly between treatment responders and non-responders (p = 0.048, 0.042, and 0.002, respectively). Multivariate analysis revealed that the IBHB-measured Pi10 was the only independent variable predictive of an FEV1 increase (p = 0.003). The adjusted odds ratio was 1.787 (95% confidence interval: 1.220–2.619). The area under the ROC curve was 0.641. CONCLUSION: Measurement of standardized airway dimensions on baseline CT by using a recently validated quantification method can predict treatment responsiveness in COPD patients.

CT Evaluation for Clinical Lung Cancer Staging: Do Multiplanar Measurements Better Reflect Pathologic T-Stage than Axial Measurements?

OBJECTIVE: To retrospectively investigate whether tumor size assessment on multiplanar reconstruction (MPR) CT images better reflects pathologic T-stage than evaluation on axial images and evaluate the additional value of measurement in three-dimensional (3D) space. MATERIALS AND METHODS: From 1661 patients who had undergone surgical resection for primary lung cancer between June 2013 and November 2016, 210 patients (145 men; mean age, 64.4 years) were randomly selected and 30 were assigned to each pathologic T-stage. Two readers independently measured the maximal lesion diameters on MPR CT. The longest diameters on 3D were obtained using volume segmentation. T-stages determined on CT images were compared with pathologic T-stages (overall and subgroup—Group 1, T1a/b; Group 2, T1c or higher), with differences in accuracy evaluated using McNemar's test. Agreement between readers was evaluated with intraclass correlation coefficients (ICC). RESULTS: The diagnostic accuracy of MPR measurements for determining T-stage was significantly higher than that of axial measurement alone for both reader 1 (74.3% [156/210] vs. 63.8% [134/210]; p = 0.001) and reader 2 (68.1% [143/210] vs. 61.9% [130/210]; p = 0.049). In the subgroup analysis, diagnostic accuracy with MPR diameter was significantly higher than that with axial diameter in only Group 2 (p < 0.05). Inter-reader agreements for the ICCs on axial and MPR measurements were 0.98 and 0.98. The longest diameter on 3D images showed a significantly lower performance than MPR, with an accuracy of 54.8% (115/210) (p < 0.05). CONCLUSION: Size measurement on MPR CT better reflected the pathological T-stage, specifically for T1c or higher stage lung cancer. Measurements in a 3D plane showed no added value.

Clinical Utility of Quantitative CT Analysis for Fissure Completeness in Bronchoscopic Lung Volume Reduction: Comparison between CT and Chartis™

OBJECTIVE: The absence of collateral ventilation (CV) is crucial for effective bronchoscopic lung volume reduction (BLVR) with an endobronchial valve. Here, we assessed whether CT can predict the Chartis™ results. MATERIALS AND METHODS: This study included 69 patients (mean age: 70.9 ± 6.6 years; 66 [95.7%] males) who had undergone CT to assess BLVR eligibility. The Chartis™ system (Pulmonox Inc.) was used to check CV. Experienced thoracic radiologists independently determined the completeness of fissures on volumetric CT images. RESULTS: The comparison between the visual and quantitative analyses revealed that 5% defect criterion showed good agreement. The Chartis™ assessment was performed for 129 lobes; 11 (19.6%) of 56 lobes with complete fissures on CT showed positive CV, while this rate was significantly higher (40 of 49 lobes, i.e., 81.6%) for lobes with incomplete fissures. The size of the fissure defect did not affect the rate of CV. Of the patients who underwent BLVR, 22 of 24 patients (91.7%) with complete fissures and three of four patients with incomplete fissures (75%) achieved target lobe volume reduction (TLVR). CONCLUSION: The quantitative analysis of fissure shows that incomplete fissures increased the probability of CV on Chartis™, while the defect size did not affect the overall rates. TLVR could be achieved even in some patients with relatively large fissure defect, if they showed negative CV on Chartis™.

Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer

OBJECTIVE: To retrospectively assess the effect of CT slice thickness on the reproducibility of radiomic features (RFs) of lung cancer, and to investigate whether convolutional neural network (CNN)-based super-resolution (SR) algorithms can improve the reproducibility of RFs obtained from images with different slice thicknesses. MATERIALS AND METHODS: CT images with 1-, 3-, and 5-mm slice thicknesses obtained from 100 pathologically proven lung cancers between July 2017 and December 2017 were evaluated. CNN-based SR algorithms using residual learning were developed to convert thick-slice images into 1-mm slices. Lung cancers were semi-automatically segmented and a total of 702 RFs (tumor intensity, texture, and wavelet features) were extracted from 1-, 3-, and 5-mm slices, as well as the 1-mm slices generated from the 3- and 5-mm images. The stabilities of the RFs were evaluated using concordance correlation coefficients (CCCs). RESULTS: The mean CCCs for the comparisons of original 1 mm vs. 3 mm, 1 mm vs. 5 mm, and 3 mm vs. 5 mm images were 0.41, 0.27, and 0.65, respectively (p < 0.001 for all comparisons). Tumor intensity features showed the best reproducibility while wavelets showed the lowest reproducibility. The majority of RFs failed to achieve reproducibility (CCC ≥ 0.85; 3.6%, 1.0%, and 21.5%, respectively). After applying the CNN-based SR algorithms, the reproducibility significantly improved in all three pairings (mean CCCs: 0.58, 0.45, and 0.72; p < 0.001 for all comparisons). The reproducible RFs also increased (36.3%, 17.4%, and 36.9%, respectively). CONCLUSION: The reproducibility of RFs in lung cancer is significantly influenced by CT slice thickness, which can be improved by the CNN-based SR algorithms.

Utility of Computed Tomography in a Differential Diagnosis for the Patients with an Initial Diagnosis of Chronic Obstructive Pulmonary Disease Exacerbation / 결핵및호흡기질환

BACKGROUND@#The utility of computed tomography (CT) in the differential diagnosis of patients with chronic obstructive pulmonary disease (COPD) exacerbation remains uncertain. However, due to the low cost associated with CT scan along with the impact of Koreas' health insurance system, there has been a rise in the number of CT scans in the patients with initial diagnosis of COPD exacerbations. Therefore, the utility of CT in the differential diagnosis was investigated to determine whether performing CT scans affect the clinical outcomes of the patients with an initial diagnosis of COPD exacerbation.@*METHODS@#This study involved 202 COPD patients hospitalized with an initial diagnosis of COPD exacerbation. We evaluated the change in diagnosis or treatment after performing a CT scan, and compared the clinical outcomes of patient groups with vs. without performing CT (non-CT group vs. CT group).@*RESULTS@#After performing CT, the diagnosis was changed for two (3.0%) while additional diagnoses were made for 27 of the 64 patients (42.1%). However, the treatment changed for only one (1.5%), and six patients (9.3%) received supplementary medication. There were no difference in the median length of hospital stay (8 [6–13] days vs. 8 [6–12] days, p=0.786) and intensive care unit care (14 [10.1%] vs. 11 [16.7%], p=0.236) between the CT and non-CT groups, respectively. These findings remained consistent even after the propensity score matching.@*CONCLUSION@#Utility of CT in patients with acute COPD exacerbation might not be helpful; therefore, we do not recommend chest CT scan as a routine initial diagnostic tool.