BACKGROUND: Coronavirus disease 2019 (COVID-19) may severely impair pulmonary function and cause hypoxia. However, the association of COVID-19 pneumonia on CT with impaired ventilation remains unexplained. This pilot study aims to demonstrate the relationship between the radiological findings on COVID-19 CT images and ventilation abnormalities simulated in a computational model linked to the patients' symptoms. METHODS: Twenty-five patients with COVID-19 and four test-negative healthy controls who underwent a baseline non-enhanced CT scan: 7 dyspneic patients, 9 symptomatic patients without dyspnea, and 9 asymptomatic patients were included. A 2D U-Net-based CT segmentation software was used to quantify radiological futures of COVID-19 pneumonia. The CT image-based full-scale airway network (FAN) flow model was employed to assess regional lung ventilation. Functional and radiological features were compared across groups and correlated with the clinical symptoms. Heterogeneity in ventilation distribution and ventilation defects associated with the pneumonia and the patients' symptoms were assessed. RESULTS: Median percentage ventilation defects were 0.2% for healthy controls, 0.7% for asymptomatic patients, 1.2% for symptomatic patients without dyspnea, and 11.3% for dyspneic patients. The median of percentage pneumonia was 13.2% for dyspneic patients and 0% for the other groups. Ventilation defects preferentially affected the posterior lung and worsened with increasing pneumonia linearly (y = 0.91x + 0.99, R2 = 0.73) except for one of the nine dyspneic patients who had disproportionally large ventilation defects (7.8% of the entire lung) despite mild pneumonia (1.2%). The symptomatic and dyspneic patients showed significantly right-skewed ventilation distributions (symptomatic without dyspnea: 0.86 +/- 0.61, dyspnea 0.91 +/- 0.79) compared to the patients without symptom (0.45 +/- 0.35). The ventilation defect analysis with the FAN model provided a comparable diagnostic accuracy to the percentage pneumonia in identifying dyspneic patients (area under the receiver operating characteristic curve, 0.94 versus 0.96). CONCLUSIONS: COVID-19 pneumonia segmentations from CT scans are accompanied by impaired pulmonary ventilation preferentially in dyspneic patients. Ventilation analysis with CT image-based computational modelling shows it is able to assess functional impairment in COVID-19 and potentially identify one of the aetiologies of hypoxia in patients with COVID-19 pneumonia.

OBJECTIVE: We conducted a systematic review and meta-analysis of the tissue adequacy and complication rates of percutaneous transthoracic needle biopsy (PTNB) for molecular analysis in patients with non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: We performed a literature search of the OVID-MEDLINE and Embase databases to identify original studies on the tissue adequacy and complication rates of PTNB for molecular analysis in patients with NSCLC published between January 2005 and January 2020. Inverse variance and random-effects models were used to evaluate and acquire meta-analytic estimates of the outcomes. To explore heterogeneity across the studies, univariable and multivariable meta-regression analyses were performed. RESULTS: A total of 21 studies with 2232 biopsies (initial biopsy, 8 studies; rebiopsy after therapy, 13 studies) were included. The pooled rates of tissue adequacy and complications were 89.3% (95% confidence interval [CI]: 85.6%-92.6%; I(2) = 0.81) and 17.3% (95% CI: 12.1%-23.1%; I(2) = 0.89), respectively. These rates were 93.5% and 22.2% for the initial biopsies and 86.2% and 16.8% for the rebiopsies, respectively. Severe complications, including pneumothorax requiring chest tube placement and massive hemoptysis, occurred in 0.7% of the cases (95% CI: 0%-2.2%; I(2) = 0.67). Multivariable meta-regression analysis showed that the tissue adequacy rate was not significantly lower in studies on rebiopsies (p = 0.058). The complication rate was significantly higher in studies that preferentially included older adults (p = 0.001). CONCLUSION: PTNB demonstrated an average tissue adequacy rate of 89.3% for molecular analysis in patients with NSCLC, with a complication rate of 17.3%. PTNB is a generally safe and effective diagnostic procedure for obtaining tissue samples for molecular analysis in NSCLC. Rebiopsy may be performed actively with an acceptable risk of complications if clinically required.

Purpose Although chest CT has been discussed as a first-line test for coronavirus disease 2019 (COVID-19), little research has explored the implications of CT exposure in the population. To review chest CT protocols and radiation doses in COVID-19 publications and explore the number needed to diagnose (NND) and the number needed to predict (NNP) if CT is used as a first-line test. Materials and Methods We searched nine highly cited radiology journals to identify studies discussing the CT-based diagnosis of COVID-19 pneumonia. Study-level information on the CT protocol and radiation dose was collected, and the doses were compared with each national diagnostic reference level (DRL). The NND and NNP, which depends on the test positive rate (TPR), were calculated, given a CT sensitivity of 94% (95% confidence interval [CI]: 91%–96%) and specificity of 37% (95% CI: 26%–50%), and applied to the early outbreak in Wuhan, New York, and Italy. Results From 86 studies, the CT protocol and radiation dose were reported in 81 (94.2%) and 17 studies (19.8%), respectively. Low-dose chest CT was used more than twice as often as standard-dose chest CT (39.5% vs.18.6%), while the remaining studies (44.2%) did not provide relevant information. The radiation doses were lower than the national DRLs in 15 of the 17 studies (88.2%) that reported doses. The NND was 3.2 scans (95% CI: 2.2–6.0). The NNPs at TPRs of 50%, 25%, 10%, and 5% were 2.2, 3.6, 8.0, 15.5 scans, respectively. In Wuhan, 35418 (TPR, 58%; 95% CI: 27710–56755) to 44840 (TPR, 38%; 95% CI: 35161–68164) individuals were estimated to have undergone CT examinations to diagnose 17365 patients. During the early surge in New York and Italy, daily NNDs changed up to 5.4 and 10.9 times, respectively, within 10 weeks. Conclusion Low-dose CT protocols were described in less than half of COVID-19 publications, and radiation doses were frequently lacking. The number of populations involved in a first-line diagnostic CT test could vary dynamically according to daily TPR; therefore, caution is required in future planning.

OBJECTIVE: This study aimed to stratify the early pneumonia trajectory on chest radiographs and compare patient characteristics in dyspneic patients with coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: We retrospectively included 139 COVID-19 patients with dyspnea (87 men, 62.7+/-16.3 years) and serial chest radiographs from January to September 2020. Radiographic pneumonia extent was quantified as a percentage using a previously-developed deep learning algorithm. A group-based trajectory model was used to categorize the pneumonia trajectory after symptom onset during hospitalization. Clinical findings, and outcomes were compared, and Cox regression was performed for survival analysis. RESULTS: Radiographic pneumonia trajectories were categorized into four groups. Group 1 (n = 83, 59.7%) had negligible pneumonia, and group 2 (n = 29, 20.9%) had mild pneumonia. Group 3 (n = 13, 9.4%) and group 4 (n = 14, 10.1%) showed similar considerable pneumonia extents at baseline, but group 3 had decreasing pneumonia extent at 1-2 weeks, while group 4 had increasing pneumonia extent. Intensive care unit admission and mortality were significantly more frequent in groups 3 and 4 than in groups 1 and 2 (P < .05). Groups 3 and 4 shared similar clinical and laboratory findings, but thrombocytopenia ( < 150x103/muL) was exclusively observed in group 4 (P = .016). When compared to groups 1 and 2, group 4 (hazard ratio, 63.3; 95% confidence interval, 7.9-504.9) had a two-fold higher risk for mortality than group 3 (hazard ratio, 31.2; 95% confidence interval, 3.5-280.2), and this elevated risk was maintained after adjusting confounders. CONCLUSION: Monitoring the early radiologic trajectory beyond baseline further prognosticated at-risk COVID-19 patients, who potentially had thrombo-inflammatory responses.

The purpose of this study was to investigate the impact of sarcopenia and body composition change during primary treatment on survival outcomes in patients with early cervical cancer. We retrospectively identified patients diagnosed with 2009 International Federation of Gynecology and Obstetrics stage IB1-IIA2 cervical cancer who underwent primary radical hysterectomy between 2007 and 2019. From pre-treatment CT scans (n = 306), the skeletal muscle area at the third lumbar vertebra (L3) and the waist skeletal muscle volume were measured using an artificial intelligence-based tool. These values were converted to the L3 and volumetric skeletal muscle indices by normalization. We defined L3 and volumetric sarcopenia using 39.0 cm(2)/m(2) and the first quartile (Q1) value, respectively. From pre- and post-treatment CT scan images (n = 192), changes (%) in waist skeletal muscle and fat volumes were assessed. With the use of Cox regression models, factors associated with progression-free survival (PFS) and overall survival (OS) were analyzed. Between the L3 sarcopenia and non-sarcopenia groups, no differences in PFS and OS were observed. In contrast, volumetric sarcopenia was identified as a poor prognostic factor for PFS (adjusted hazard ratio [aHR], 1.874; 95% confidence interval [CI], 1.028-3.416; p = 0.040) and OS (aHR, 3.001; 95% CI, 1.016-8.869; p = 0.047). During primary treatment, significant decreases in waist skeletal muscle (median, -3.9%; p < 0.001) and total fat (median, -5.3%; p < 0.001) were observed. Of the two components, multivariate analysis revealed that the waist fat gain was associated with worse PFS (aHR, 2.007; 95% CI, 1.009-3.993; p = 0.047). The coexistence of baseline volumetric sarcopenia and waist fat gain further deteriorated PFS (aHR, 2.853; 95% CI, 1.257-6.474; p = 0.012). In conclusion, baseline volumetric sarcopenia might be associated with poor survival outcomes in patients with early cervical cancer undergoing primary RH. Furthermore, sarcopenia patients who gained waist fat during primary treatment were at a high risk of disease recurrence.

Chest radiography (CR) is the primary examination for the evaluation and follow-up of various thoracic diseases. The number of examinations is steadily on the increase, as is evidenced by the national health insurance data in Korea. However, due to the relative shortage of experienced radiologists, many institutions cannot provide timely interpretation of CRs or depend on outsourcing for interpretation. In this background, artificial intelligence (AI) for the evaluation of CR has been actively investigated, and several AI-based software as medical devices (AI-SaMDs) have begun to be used in clinical practice. However, there has been limited discussion on how to use AI-SaMDs in clinical practice; there are also concerns about inappropriate use or abuse of AI-SaMDs resulting in patient harm and liability for physicians. This article introduces the current situation regarding the application of AI-SaMD for CR in clinical practice and presents the opinion of the Korean Society of Thoracic Radiology (KSTR) toward use of this application.

Background: Because obesity is associated with the risk of posttransplant diabetes mellitus (PTDM), the precise estimation of visceral fat mass before transplantation may be helpful. Herein, we addressed whether a deep-learning based volumetric fat quantification on pretransplant computed tomographic images predicted the risk of PTDM more precisely than body mass index (BMI). Methods: We retrospectively included a total of 718 nondiabetic kidney recipients who underwent pretransplant abdominal computed tomography. The 2D (waist) and 3D (waist or abdominal) volumes of visceral, subcutaneous, and total fat masses were automatically quantified using the deep neural network. The predictability of the PTDM risk was estimated using a multivariate Cox model and compared among the fat parameters using the areas under the receiver operating characteristic curves (AUROCs). Results: PTDM occurred in 179 patients (24.9%) during the median follow-up period of 5 years (interquartile range, 2.5-8.6 years). All the fat parameters predicted the risk of PTDM, but the visceral and total fat volumes from 2D and 3D evaluations had higher AUROC values than BMI did, and the best predictor of PTDM was the 3D abdominal visceral fat volumes [AUROC, 0.688 (0.636-0.741)]. The addition of the 3D abdominal VF volume to the model with clinical risk factors increased the predictability of PTDM, but BMI did not. Conclusions: A deep-learning based quantification of visceral fat volumes on computed tomographic images better predicts the risk of PTDM after kidney transplantation than BMI.