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Abstract The aim of our study was to assess the performance of content-based image retrieval (CBIR) for similar chest computed tomography (CT) in obstructive lung disease. This retrospective study included patients with obstructive lung disease who underwent volumetric chest CT scans. The CBIR database included 600 chest CT scans from 541 patients. To assess the system performance, follow-up chest CT scans of 50 patients were evaluated as query cases, which showed the stability of the CT findings between baseline and follow-up chest CT, as confirmed by thoracic radiologists. The CBIR system retrieved the top five similar CT scans for each query case from the database by quantifying and comparing emphysema extent and size, airway wall thickness, and peripheral pulmonary vasculatures in descending order from the database. The rates of retrieval of the same pairs of query CT scans in the top 1–5 retrievals were assessed. Two expert chest radiologists evaluated the visual similarities between the query and retrieved CT scans using a five-point scale grading system. The rates of retrieving the same pairs of query CTs were 60.0% (30/50) and 68.0% (34/50) for top-three and top-five retrievals. Radiologists rated 64.8% (95% confidence interval 58.8–70.4) of the retrieved CT scans with a visual similarity score of four or five and at least one case scored five points in 74% (74/100) of all query cases. The proposed CBIR system for obstructive lung disease integrating quantitative CT measures demonstrated potential for retrieving chest CT scans with similar imaging phenotypes. Further refinement and validation in this field would be valuable.

Abstract The validation of the accuracy of the quantification software in computed tomography (CT) images is very challenging. Therefore, we proposed a CT imaging phantom that accurately represents patient-specific anatomical structures and randomly integrates various lesions including disease-like patterns and lesions of various shapes and sizes using silicone casting and three-dimensional (3D) printing. Six nodules of various shapes and sizes were randomly added to the patient’s modeled lungs to evaluate the accuracy of the quantification software. By using silicone materials, CT intensities suitable for the lesions and lung parenchyma were realized, and their Hounsfield unit (HU) values were evaluated on a CT scan of the phantom. As a result, based on the CT scan of the imaging phantom model, the measured HU values for the normal lung parenchyma, each nodule, fibrosis, and emphysematous lesions were within the target value. The measurement error between the stereolithography model and 3D-printing phantoms was 0.2 ± 0.18 mm. In conclusion, the use of 3D printing and silicone casting allowed the application and evaluation of the proposed CT imaging phantom for the validation of the accuracy of the quantification software in CT images, which could be applied to CT-based quantification and development of imaging biomarkers.

Abstract The generative adversarial network (GAN) is a promising deep learning method for generating images. We evaluated the generation of highly realistic and high-resolution chest radiographs (CXRs) using progressive growing GAN (PGGAN). We trained two PGGAN models using normal and abnormal CXRs, solely relying on normal CXRs to demonstrate the quality of synthetic CXRs that were 1000 × 1000 pixels in size. Image Turing tests were evaluated by six radiologists in a binary fashion using two independent validation sets to judge the authenticity of each CXR, with a mean accuracy of 67.42% and 69.92% for the first and second trials, respectively. Inter-reader agreements were poor for the first (κ = 0.10) and second (κ = 0.14) Turing tests. Additionally, a convolutional neural network (CNN) was used to classify normal or abnormal CXR using only real images and/or synthetic images mixed datasets. The accuracy of the CNN model trained using a mixed dataset of synthetic and real data was 93.3%, compared to 91.0% for the model built using only the real data. PGGAN was able to generate CXRs that were identical to real CXRs, and this showed promise to overcome imbalances between classes in CNN training.

Abstract Background Chest MRI is a useful diagnostic modality for the evaluation of anterior mediastinal lesions but the outcomes of anterior mediastinal cystic lesions diagnosed on chest MRI are unclear. Methods In this multicenter retrospective study, patients who underwent contrast-enhanced chest MRI in two tertiary centers to assess anterior mediastinal cystic lesions were included after excluding overt solid tumors and thymic hyperplasia. Anterior mediastinal cystic lesions were classified into two categories: probable (simple) cyst or indeterminate lesion (complex cyst). Size and imaging features of lesions during follow-up were evaluated and clinical outcomes were assessed. Results A total of 204 patients (mean age, 59 ± 11 years; M:F = 111:93) were studied; 186 (91.2%) were classified as probable cysts and 18 (8.8%) as indeterminate lesions on MRI. Among patients with probable cysts and more than 2 years of follow-up, lesion size was unchanged in 39.6% (36/91), decreased in 16.5% (15/91), and fluctuated in 8.8% (8/91). All patients who underwent surgery were confirmed cysts. None developed mural nodules or irregular wall thickening, suspicious for malignancy during follow-up. In patients with indeterminate lesions, 16.7% (3/18) had pathologically confirmed thymoma and 44.4% (8/18) had proven cysts. Follow-up numbers and intervals after MRI in patients with probable cysts were variable among physicians and institutions in clinical practice (p

Although deep learning-based computer-aided diagnosis systems have recently achieved expert level performance, developing a robust model requires large, high-quality data with annotations. Here, the authors present a framework which can improve the performance of vision transformer simultaneously with self-supervision and self-training.

Abstract Heterogeneous clinical manifestations and progression of chronic obstructive pulmonary disease (COPD) affect patient health risk assessment, stratification, and management. Pulmonary function tests are used to diagnose and classify the severity of COPD, but they cannot fully represent the type or range of pathophysiologic abnormalities of the disease. To evaluate whether deep radiomics from chest computed tomography (CT) images can predict mortality in patients with COPD, we designed a convolutional neural network (CNN) model for extracting representative features from CT images and then performed random survival forest to predict survival in COPD patients. We trained CNN-based binary classifier based on six-minute walk distance results (> 440 m or not) and extracted high-throughput image features (i.e., deep radiomics) directly from the last fully connected layer of it. The various sizes of fully connected layers and combinations of deep features were experimented using a discovery cohort with 344 patients from the Korean Obstructive Lung Disease cohort and an external validation cohort with 102 patients from Penang General Hospital in Malaysia. In the integrative analysis of discovery and external validation cohorts, with combining 256 deep features from the coronal slice of the vertebral body and two sagittal slices of the left/right lung, deep radiomics for survival prediction achieved concordance indices of 0.8008 (95% CI, 0.7642–0.8373) and 0.7156 (95% CI, 0.7024–0.7288), respectively. Deep radiomics from CT images could be used to predict mortality in COPD patients.

Abstract We used 3D printed-breast surgical guides (3DP-BSG) to designate the original tumor area from the pre-treatment magnetic resonance imaging (MRI) during breast-conserving surgery (BCS) in breast cancer patients who received neoadjuvant systemic therapy (NST). Targeting the original tumor area in such patients using conventional localization techniques is difficult. For precise BCS, a method that marks the tumor area found on MRI directly to the breast is needed. In this prospective study, patients were enrolled for BCS after receiving NST. Partial resection was performed using a prone/supine MRI-based 3DP-BSG. Frozen biopsies were analyzed to confirm clear tumor margins. The tumor characteristics, pathologic results, resection margins, and the distance between the tumor and margin were analyzed. Thirty-nine patients were enrolled with 3DP-BSG for BCS. The median nearest distance between the tumor and the resection margin was 3.9 cm (range 1.2–7.8 cm). Frozen sections showed positive margins in 4/39 (10.3%) patients. Three had invasive cancers, and one had carcinoma in situ; all underwent additional resection. Final pathology revealed clear margins. After 3-year surveillance, 3/39 patients had recurrent breast cancer. With 3DP-BSG for BCS in breast cancer patients receiving NST, the original tumor area can be identified and marked directly on the breast, which is useful for surgery. Trial Registration: Clinical Research Information Service (CRIS) Identifier Number: KCT0002272. First registration number and date: No. 1 (27/04/2016).

Abstract To facilitate precise tumor resection at the time of breast-conserving surgery (BCS), we developed and implemented a magnetic resonance imaging (MRI)-based three-dimensional-printed (3DP) breast surgical guide (BSG). This prospective cohort study was conducted at a single institution from July 2017 to February 2019 on women with breast cancer who underwent partial breast resection using patient-specific 3DP BSGs. Eighty-eight patients with invasive cancer were enrolled, of whom 1 patient had bilateral breast cancer. The mean size of the tumor long-axis on MRI before surgery was 2.8 ± 0.9 cm, and multiple tumors were observed in 34 patients. In 16 cases (18.0%), the resection margin was tumor-positive according to intraoperative frozen biopsy; all of these tumors were ductal carcinoma in situ and were re-excised intraoperatively. In 93.3% of the cases, the resection margin was tumor-free in the permanent pathology. The mean pathological tumor size was 1.7 ± 1.0 cm, and the mean distance from the tumor to the border was 1.5 ± 1.0 cm. This exploratory study showed that the tumor area on the MRI could be directly displayed on the breast when using a 3DP BSG for BCS, thereby allowing precise surgery and safe tumor removal. Trial Registration Clinical Research Information Service (CRIS) Identifier (No. KCT0002375, KCT0003043).

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.

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.

Chronic Obstructive Pulmonary Disease (COPD) is a complex heterogeneous condition with various clinical and pathologic features. In recent years, technical advances in quantitative CT imaging have generated considerable interest because they can provide a more precise and objective assessment of COPD. Emphysema and small-airway disease, the two major components of COPD, and other comorbidities, including pulmonary vessel alterations, atherosclerosis, cachexia, and osteoporosis, can all be assessed by means of quantitative imaging parameters. Increasing numbers of studies provide promising reports indicating that such parameters are associated with clinical measures of disease severity, respiratory symptoms, COPD exacerbations, and mortality. Despite such optimistic results, there are still many obstacles to using this quantitative technology in everyday practice to manage COPD patients. In this article, we review the current technical status of quantitative CT assessment, emphasizing its clinical implications and limitations. We also discuss present challenges and the potential future role of quantitative CT imaging in assessing COPD.

PURPOSE:We aimed to evaluate the accuracy of a needle-placement robot for biopsy and radiofrequency ablation on an abdominal phantom.METHODS:A master-slave robotic system has been developed that includes a needle-path planning system and a needle-inserting robot arm with computed tomography (CT) and CT fluoroscopy guidance. For evaluation of its accuracy in needle placement, a commercially available abdominal phantom (Model 057A; CIRS Inc.) was used. The liver part of the phantom contains multiple spherical simulated tumors of three different size spheres. Various needle insertion trials were performed in the transverse plane and caudocranial plane two nodule sizes (10 mm and 20 mm in diameter) to test the reliability of this robot. To assess accuracy, a CT scan was performed after each trial with the needle in situ.RESULTS:The overall error was 2 mm (0–2.6 mm), which was calculated as the distance from the planned trajectory before insertion to the actual needle trajectory after insertion. The standard deviations of the insertions on two nodules (10 mm and 20 mm in diameter) were 0.5 mm and 0.2 mm, respectively.CONCLUSION:The CT-compatible needle placement robot for biopsy and radiofrequency ablation shows relatively acceptable accuracy and could be used for radiofrequency ablation of nodules ≥10 mm under CT fluoroscopy guidance.

For patients with chronic lower respiratory disease, hypobaric hypoxia at a high altitude is considered a risk factor for mortality. However, the effects of residing at moderately high altitudes remain unclear. We investigated the association between moderate altitude and chronic lower respiratory disease mortality. In particular, we examined the lower 48 United States counties for age-adjusted chronic lower respiratory disease mortality rates, altitude, and socioeconomic factors, including tobacco use, per capita income, population density, sex ratio, unemployment, poverty, and education between 1979 and 1998. The socioeconomic factors were incorporated into the correlation analysis as potential covariates. Considerable positive (R = 0.235; P

BACKGROUND:The ratio of the diameter of the main pulmonary artery (mPA) to the diameter of the aorta (Ao) on chest computed tomography is associated with diverse clinical conditions. Herein, we determined the functional and prognostic implications of the mPA/Ao ratio in Korean chronic obstructive pulmonary disease (COPD) patients. METHODS:The study population comprised 226 chronic obstructive pulmonary disease patients from the Korean Obstructive Lung Disease cohort who underwent chest computed tomography. We analyzed the relationships between the clinical characteristics, including pulmonary function, echocardiography findings, St. George's Respiratory Questionnaire, 6-minute walking (6MW) distance, and exacerbation with the mPA, Ao, and mPA/Ao ratio. RESULTS:The mean age was 65.8 years, and 219 (96.9%) patients were male. The mean FEV1% predicted and FEV1/FVC ratio were 61.2% and 47.3%, respectively. The mean mPA and Ao were 23.7 and 36.4 mm, respectively, and the mPA/Ao ratio was 0.66. The mPA/Ao ratio correlated negatively with the 6MW distance (G = -0.133, P = 0.025) and positively with the right ventricular pressure (G = 0.323, P = 0.001). After adjustment for potential confounders, the mPA/Ao ratio was significantly associated with 6MW distance (β = -107.7, P = 0.017). Moreover, an mPA/Ao ratio >0.8 was a significant predictor of exacerbation at the 1-year (odds ratio 2.12, 95% confidence interval 1.27-3.52) and 3-year follow-ups (odds ratio 2.04, 95% confidence interval 1.42-2.90). CONCLUSIONS:The mPA/Ao ratio is an independent predictor of exercise capacity and an mPA/Ao ratio >0.8 is a significant risk factor of COPD exacerbation.

This paper presents a new end-effector as a key component for a robotic needle insertion-type intervention system and its kinematic analysis. The mechanism is designed as a spherical mechanism with a revolute joint and a curved sliding joint, and its links always move on the surface of a sphere. The remote centre of motion (RCM) of the designed mechanism is placed below the base of the mechanism to avoid contact with the patient's body, unlike the conventional end-effectors developed for needle insertion. For the proposed mechanism, the forward kinematics are solved in terms of input joint parameters and then the reverse kinematics are solved by using the cross-product relationship between each joint vector and a vector mutually perpendicular to the vectors. The kinematic solutions are confirmed by numerical examples.

BACKGROUND AND PURPOSE: Several imaging-based indices were constructed quantitatively using the emphysema index (EI) and fibrosis score (FS) on high-resolution computed tomography (HRCT). We evaluated the ability of these indices to predict mortality compared to physiologic results. Additionally, prognostic predictive factors were compared among subgroups with biopsy-proven fibrotic idiopathic interstitial pneumonia (IIP) (biopsy-proven CPFE) and in a separate cohort with subclinical CPFE. MATERIALS AND METHODS: Three chest radiologists independently determined FS. EI was automatically quantified. PFTs, smoking history, and composite physiologic index (CPI) were reviewed. Predictors of time to death were determined based on clinico-physiologic factors and CT-based CPFE indices. RESULTS: The prevalence of biopsy-proven CPFE was 26% (66/254), with an EI of 9.1±7.1 and a FS of 19.3±14.2. In patients with CPFE, median survival and 5-year survival rates were 6.0 years and 34.8%, respectively, whereas those in fibrotic IIP without emphysema were 10.0 years and 60.9% (p = 0.013). However, the extent of fibrosis did not differ significantly between the two cohorts. In subclinical CPFE, prevalence was 0.04% (93/20,372), EI was 11.3±10.4, and FS was 9.1±7.1. FVC and a fibrosis-weighted CT index were independent predictors of survival in the biopsy-proven CPFE cohort, whereas only the fibrosis-weighted CT index was a significant prognostic factor in the subclinical CPFE cohort. CONCLUSIONS: Recognition and stratification using CT quantification can be utilized as a prognostic predictor. Prognostic factors vary according to fibrosis severity and among cohorts of patients with biopsy-proven and subclinical CPFE.

Rationale: The optimal follow-up computed tomography (CT) interval for detecting the progression of interstitial lung abnormality (ILA) is unknown. Objectives: To identify optimal follow-up strategies and extent thresholds on CT relevant to outcomes. Methods: This retrospective study included self-referred screening participants aged 50 years or older, including nonsmokers, who had imaging findings relevant to ILA on chest CT scans. Consecutive CT scans were evaluated to determine the dates of the initial CT showing ILA and the CT showing progression. Deep learning-based ILA quantification was performed. Cox regression was used to identify risk factors for the time to ILA progression and progression to usual interstitial pneumonia (UIP). Measurements and Main Results: Of the 305 participants with a median follow-up duration of 11.3 years (interquartile range, 8.4-14.3 yr), 239 (78.4%) had ILA on at least one CT scan. In participants with serial follow-up CT studies, ILA progression was observed in 80.5% (161 of 200), and progression to UIP was observed in 17.3% (31 of 179), with median times to progression of 3.2 years (95% confidence interval [CI], 3.0-3.4 yr) and 11.8 years (95% CI, 10.8-13.0 yr), respectively. The extent of fibrosis on CT was an independent risk factor for ILA progression (hazard ratio, 1.12 [95% CI, 1.02-1.23]) and progression to UIP (hazard ratio, 1.39 [95% CI, 1.07-1.80]). Risk groups based on honeycombing and extent of fibrosis (1% in the whole lung or 5% per lung zone) showed significant differences in 10-year overall survival (P = 0.02). Conclusions: For individuals with initially detected ILA, followup CT at 3-year intervals may be appropriate to monitor radiologic progression; however, those at high risk of adverse outcomes on the basis of the quantified extent of fibrotic ILA and the presence of honeycombing may benefit from shortening the interval for follow-up scans. [ABSTRACT FROM AUTHOR]