Association Between Fat Depletion and Prognosis of Amyotrophic Lateral Sclerosis: CT-Based Body Composition Analysis.

OBJECTIVE: The purpose of this study was to present the results of our investigation of the prognostic value of adipopenia and sarcopenia in patients with amyotrophic lateral sclerosis (ALS). METHODS: Consecutive patients with ALS with abdominal computed tomography (CT) were retrospectively identified at a single tertiary hospital between January 2010 and July 2021. Deep learning-based volumetric CT body composition analysis software was used to obtain abdominal waist fat volume, fat attenuation, and skeletal muscle area at the L3 level, then normalized to the fat volume index (FVI) and skeletal muscle index (SMI). Adipopenia and sarcopenia were defined as the sex-specific lowest quartile and SMI reference values, respectively. The associations of CT-derived body composition parameters with clinical variables, such as body mass index (BMI) and creatinine, were evaluated by Pearson correlation analyses, and associations with survival were assessed using the multivariable Cox regression analysis. RESULTS: Eighty subjects (40 men, 65.5 ± 9.4 years of age) were investigated (median interval between disease onset and CT examination = 25 months). The mean BMI at the CT examination was 20.3 ± 4.3 kg/m2 . The BMI showed a positive correlation with both FVI (R = 0.70, p < 0.001) and SMI (R = 0.63, p < 0.001), and the serum creatinine level was associated with SMI (R = 0.68, p < 0.001). After adjusting for sex, age, King's stage, BMI, creatinine, progression rate, and sarcopenia, adipopenia was associated with shorter survival (hazard ratio [HR] = 5.94, 95% confidence interval [CI] = 1.01, 35.0, p = 0.049). In a subgroup analysis for subjects with nutritional failure (stage 4a), the HR of adipopenia was 15.1 (95% CI = 2.45, 93.4, p = 0.003). INTERPRETATION: Deep learning-based CT-derived adipopenia in patients with ALS is an independent poor prognostic factor for survival. ANN NEUROL 2023;94:1116-1125.

Association between body fat decrease during the first year after diagnosis and the prognosis of idiopathic pulmonary fibrosis: CT-based body composition analysis.

BACKGROUND: The prognostic role of changes in body fat in patients with idiopathic pulmonary fibrosis (IPF) remains underexplored. We investigated the association between changes in body fat during the first year post-diagnosis and outcomes in patients with IPF. METHODS: This single-center, retrospective study included IPF patients with chest CT scan and pulmonary function test (PFT) at diagnosis and a one-year follow-up between January 2010 and December 2020. The fat area (cm2, sum of subcutaneous and visceral fat) and muscle area (cm2) at the T12-L1 level were obtained from chest CT images using a fully automatic deep learning-based software. Changes in the body composition were dichotomized using thresholds dividing the lowest quartile and others, respectively (fat area: -52.3 cm2, muscle area: -7.4 cm2). Multivariable Cox regression analyses adjusted for PFT result and IPF extent on CT images and the log-rank test were performed to assess the association between the fat area change during the first year post-diagnosis and the composite outcome of death or lung transplantation. RESULTS: In total, 307 IPF patients (69.3 ± 8.1 years; 238 men) were included. During the first year post-diagnosis, fat area, muscle area, and body mass index (BMI) changed by -15.4 cm2, -1 cm2, and - 0.4 kg/m2, respectively. During a median follow-up of 47 months, 146 patients had the composite outcome (47.6%). In Cox regression analyses, a change in the fat area < -52.3 cm2 was associated with composite outcome incidence in models adjusted with baseline clinical variables (hazard ratio [HR], 1.566, P = .022; HR, 1.503, P = .036 in a model including gender, age, and physiology [GAP] index). This prognostic value was consistent when adjusted with one-year changes in clinical variables (HR, 1.495; P = .030). However, the change in BMI during the first year was not a significant prognostic factor (P = .941). Patients with a change in fat area exceeding this threshold experienced the composite outcome more frequently than their counterparts (58.4% vs. 43.9%; P = .007). CONCLUSION: A ≥ 52.3 cm2 decrease in fat area, automatically measured using deep learning technique, at T12-L1 in one year post-diagnosis was an independent poor prognostic factor in IPF patients.

Effect of smoking on the diagnostic results and complication rates of percutaneous transthoracic needle biopsy.

OBJECTIVE: To investigate the association of smoking with the outcomes of percutaneous transthoracic needle biopsy (PTNB). METHODS: In total, 4668 PTNBs for pulmonary lesions were retrospectively identified. The associations of smoking status (never, former, current smokers) and smoking intensity (≤ 20, 21-40, > 40 pack-years) with diagnostic results (malignancy, non-diagnostic pathologies, and false-negative results in non-diagnostic pathologies) and complications (pneumothorax and hemoptysis) were assessed using multivariable logistic regression analysis. RESULTS: Among the 4668 PTNBs (median age of the patients, 66 years [interquartile range, 58-74]; 2715 men), malignancies, non-diagnostic pathologies, and specific benign pathologies were identified in 3054 (65.4%), 1282 (27.5%), and 332 PTNBs (7.1%), respectively. False-negative results for malignancy occurred in 20.5% (236/1153) of non-diagnostic pathologies with decidable reference standards. Current smoking was associated with malignancy (adjusted odds ratio [OR], 1.31; 95% confidence interval [CI]: 1.02-1.69; p = 0.03) and false-negative results (OR, 2.64; 95% CI: 1.32-5.28; p = 0.006), while heavy smoking (> 40 pack-years) was associated with non-diagnostic pathologies (OR, 1.69; 95% CI: 1.19-2.40; p = 0.003) and false-negative results (OR, 2.12; 95% CI: 1.17-3.92; p = 0.02). Pneumothorax and hemoptysis occurred in 21.8% (1018/4668) and 10.6% (495/4668) of PTNBs, respectively. Heavy smoking was associated with pneumothorax (OR, 1.33; 95% CI: 1.01-1.74; p = 0.04), while heavy smoking (OR, 0.64; 95% CI: 0.40-0.99; p = 0.048) and current smoking (OR, 0.64; 95% CI: 0.42-0.96; p = 0.04) were inversely associated with hemoptysis. CONCLUSION: Smoking history was associated with the outcomes of PTNBs. Current and heavy smoking increased false-negative results and changed the complication rates of PTNBs. CLINICAL RELEVANCE STATEMENT: Smoking status and intensity were independently associated with the outcomes of PTNBs. Non-diagnostic pathologies should be interpreted cautiously in current or heavy smokers. A patient's smoking history should be ascertained before PTNB to predict and manage complications. KEY POINTS: • Smoking status and intensity might independently contribute to the diagnostic results and complications of PTNBs. • Current and heavy smoking (> 40 pack-years) were independently associated with the outcomes of PTNBs. • Operators need to recognize the association between smoking history and the outcomes of PTNBs.

Prognostic value of CT-defined ground-glass opacity in early-stage lung adenocarcinomas: a single-center study and meta-analysis.

OBJECTIVES: The prognostic value of ground-glass opacity at preoperative chest CT scans in early-stage lung adenocarcinomas is a matter of debate. We aimed to clarify the existing evidence through a single-center, retrospective cohort study and to quantitatively summarize the body of literature by conducting a meta-analysis. METHODS: In a retrospective cohort study, patients with clinical stage I lung adenocarcinoma were identified, and the prognostic value of ground-glass opacity was analyzed using multivariable Cox regression. Commercial artificial intelligence software was adopted as the second reader for the presence of ground-glass opacity. The primary end points were freedom from recurrence (FFR) and lung cancer-specific survival (LCSS). In a meta-analysis, we systematically searched Embase and OVID-MEDLINE up to December 30, 2021, for the studies based on the eighth-edition staging system. The pooled hazard ratios (HRs) of solid nodules (i.e., absence of ground-glass opacity) for various end points were calculated with a multi-level random effects model. RESULTS: In a cohort of 612 patients, solid nodules were associated with worse outcomes for FFR (adjusted HR, 1.98; 95% CI: 1.17-3.51; p = 0.01) and LCSS (adjusted HR, 1.937; 95% CI: 1.002-4.065; p = 0.049). The artificial intelligence assessment and multiple sensitivity analyses revealed consistent results. The meta-analysis included 13 studies with 12,080 patients. The pooled HR of solid nodules was 2.13 (95% CI: 1.69-2.67; I2 = 30.4%) for overall survival, 2.45 (95% CI: 1.52-3.95; I2 = 0.0%) for FFR, and 2.50 (95% CI: 1.28-4.91; I2 = 30.6%) for recurrence-free survival. CONCLUSIONS: The absence of ground-glass opacity in early-stage lung adenocarcinomas is associated with worse postoperative survival. CLINICAL RELEVANCE STATEMENT: Early-stage lung adenocarcinomas manifesting as solid nodules at preoperative chest CT, which indicates the absence of ground-glass opacity, were associated with poor postoperative survival. There is room for improvement of the clinical T categorization in the next edition staging system. KEY POINTS: • In a retrospective study of 612 patients with stage I lung adenocarcinoma, solid nodules were associated with shorter freedom from recurrence (adjusted hazard ratio [HR], 1.98; p = 0.01) and lung cancer-specific survival (adjusted HR, 1.937; p = 0.049). • Artificial intelligence-assessed solid nodules also showed worse prognosis (adjusted HR for freedom from recurrence, 1.94 [p = 0.01]; adjusted HR for lung cancer-specific survival, 1.93 [p = 0.04]). • In meta-analyses, the solid nodules were associated with shorter freedom from recurrence (HR, 2.45) and shorter overall survival (HR, 2.13).

Diagnostic performance and prognostic value of CT-defined visceral pleural invasion in early-stage lung adenocarcinomas.

OBJECTIVES: To analyze the diagnostic performance and prognostic value of CT-defined visceral pleural invasion (CT-VPI) in early-stage lung adenocarcinomas. METHODS: Among patients with clinical stage I lung adenocarcinomas, half of patients were randomly selected for a diagnostic study, in which five thoracic radiologists determined the presence of CT-VPI. Probabilities for CT-VPI were obtained using deep learning (DL). Areas under the receiver operating characteristic curve (AUCs) and binary diagnostic measures were calculated and compared. Inter-rater agreement was assessed. For all patients, the prognostic value of CT-VPI by two radiologists and DL (using high-sensitivity and high-specificity cutoffs) was investigated using Cox regression. RESULTS: In 681 patients (median age, 65 years [interquartile range, 58-71]; 382 women), pathologic VPI was positive in 130 patients. For the diagnostic study (n = 339), the pooled AUC of five radiologists was similar to that of DL (0.78 vs. 0.79; p = 0.76). The binary diagnostic performance of radiologists was variable (sensitivity, 45.3-71.9%; specificity, 71.6-88.7%). Inter-rater agreement was moderate (weighted Fleiss κ, 0.51; 95%CI: 0.43-0.55). For overall survival (n = 680), CT-VPI by radiologists (adjusted hazard ratio [HR], 1.27 and 0.99; 95%CI: 0.84-1.92 and 0.63-1.56; p = 0.26 and 0.97) or DL (HR, 1.44 and 1.06; 95%CI: 0.86-2.42 and 0.67-1.68; p = 0.17 and 0.80) was not prognostic. CT-VPI by an attending radiologist was prognostic only in radiologically solid tumors (HR, 1.82; 95%CI: 1.07-3.07; p = 0.03). CONCLUSION: The diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas. This feature may be applied for radiologically solid tumors, but substantial reader variability should be overcome. CLINICAL RELEVANCE STATEMENT: Although the diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas, this parameter may be applied for radiologically solid tumors with appropriate caution regarding inter-reader variability. KEY POINTS: • Use of CT-defined visceral pleural invasion in clinical staging should be cautious, because prognostic value of CT-defined visceral pleural invasion remains unexplored. • Diagnostic performance and prognostic value of CT-defined visceral pleural invasion varied among radiologists and deep learning. • Role of CT-defined visceral pleural invasion in clinical staging may be limited to radiologically solid tumors.

Usual Interstitial Pneumonia: Associations With Complications After Percutaneous Transthoracic Needle Lung Biopsy.

BACKGROUND. Changes in lung parenchyma elasticity in usual interstitial pneumonia (UIP) may increase the risk for complications after percutaneous transthoracic needle biopsy (PTNB) of the lung. OBJECTIVE. The purpose of this article was to investigate the association of UIP findings on CT with complications after PTNB, including pneumothorax, pneumothorax requiring chest tube insertion, and hemoptysis. METHODS. This retrospective single-center study included 4187 patients (mean age, 63.8 ± 11.9 [SD] years; 2513 men, 1674 women) who underwent PTNB between January 2010 and December 2015. Patients were categorized into a UIP group and non-UIP group by review of preprocedural CT. In the UIP group, procedural CT images were reviewed to assess for traversal of UIP findings by needle. Multivariable logistic regression analyses were performed to identify associations between the UIP group and needle traversal with postbiopsy complications, controlling for a range of patient, lesion, and procedural characteristics. RESULTS. The UIP and non-UIP groups included 148 and 4039 patients, respectively; in the UIP group, traversal of UIP findings by needle was observed in 53 patients and not observed in 95 patients. The UIP group, in comparison with the non-UIP group, had a higher frequency of pneumothorax (35.1% vs 17.9%, p < .001) and pneumothorax requiring chest tube placement (6.1% vs 1.5%, p = .001) and lower frequency of hemoptysis (2.0% vs 6.1%, p = .03). In multivariable analyses, the UIP group with traversal of UIP findings by needle, relative to the non-UIP group, showed independent associations with pneumothorax (OR, 5.25; 95% CI, 2.94-9.37; p < .001) and pneumothorax requiring chest tube placement (OR, 9.55; 95% CI, 3.74-24.38; p < .001). The UIP group without traversal of UIP findings by needle, relative to the non-UIP group, was not independently associated with pneumothorax (OR, 1.18; 95% CI, 0.71-1.97; p = .51) or pneumothorax requiring chest tube placement (OR, 1.08; 95% CI, 0.25-4.72; p = .92). The UIP group, with or without traversal of UIP findings by needle, was not independently associated with hemoptysis. No patient experienced air embolism or procedure-related death. CONCLUSION. Needle traversal of UIP findings is a risk factor for pneumothorax and pneumothorax requiring chest tube placement after PTNB. CLINICAL IMPACT. When performing PTNB in patients with UIP, radiologists should plan a needle trajectory that does not traverse UIP findings, when possible.

Pulse versus non-pulse corticosteroid therapy in patients with acute exacerbation of idiopathic pulmonary fibrosis.

BACKGROUND AND OBJECTIVE: Corticosteroids are commonly used for the treatment of acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF); however, the optimal initial dose of corticosteroids remains uncertain due to a lack of sufficient evidence. We evaluated whether the administration of a pulse dose of corticosteroids resulted in improved survival outcomes compared with conventional non-pulse dose of corticosteroids. METHODS: We retrospectively analysed 238 patients with AE-IPF treated with corticosteroids at a tertiary referral hospital between January 2013 and December 2021. Based on whether a pulse dose of corticosteroids (methylprednisolone of ≥250 mg/day or equivalent) was administered within 7 days of hospitalization for AE-IPF, the patients were divided into the pulse and non-pulse regimen groups. The survival outcomes were compared between the two groups using multivariable regression and propensity score-matched analyses. RESULTS: Among the 238 patients, 59 patients received pulse dose of corticosteroids, whereas 179 patients received conventional non-pulse dose of corticosteroids. After adjusting for the confounding factors related to the baseline clinical and radiographic severity, compared with the conventional non-pulse regimen, the pulse regimen of corticosteroids did not reduce the risk of mortality at the 3-month (aHR 0.84, 95% CI 0.45-1.38) or 12-month (aHR 0.96, 95% CI 0.60-1.25) follow-ups. Propensity score-matched analysis revealed similar results. CONCLUSION: The survival outcomes of patients with AE-IPF who received a pulse dose of corticosteroids did not differ from those of patients who received conventional non-pulse dose of corticosteroids. Further prospective studies are required to establish the optimal initial dose of corticosteroids for the treatment of AE-IPF.

Chest CT Findings in Hospitalized Patients with SARS-CoV-2: Delta versus Omicron Variants.

Background CT manifestations of SARS-CoV-2 may differ among variants. Purpose To compare the chest CT findings of SARS-CoV-2 between the Delta and Omicron variants. Materials and Methods This retrospective study collected consecutive baseline chest CT images of hospitalized patients with SARS-CoV-2 from a secondary referral hospital when the Delta and Omicron variants were predominant. Two radiologists categorized CT images according to the RSNA classification system for COVID-19 and visually graded pneumonia extent. Pneumonia, pleural effusion, and intrapulmonary vessels were segmented and quantified on CT images using a priori-developed neural networks, followed by reader confirmation. Multivariable logistic and linear regression analyses were performed to examine the associations between the variants and CT category, distribution, severity, and peripheral vascularity. Results In total, 88 patients with the Delta variant (mean age, 67 years ± 15 [SD]; 46 men) and 88 patients with the Omicron variant (mean age, 62 years ± 19; 51 men) were included. Omicron was associated with less frequent, typical peripheral bilateral ground-glass opacity (32% [28 of 88] vs 57% [50 of 88], P = .001), more frequent peribronchovascular predilection (38% [25 of 66] vs 7% [five of 71], P < .001), lower visual pneumonia extent (5.4 ± 6.0 vs 7.7 ± 6.6, P = .02), similar pneumonia volume (5% ± 1 vs 7% ± 11, P = .14), and a higher proportion of vessels with a cross-sectional area smaller than 5 mm2 relative to the total pulmonary blood volume (BV5%; 48% ± 11 vs 44% ± 8; P = .004). In adjusted analyses, Omicron was associated with a nontypical appearance (odds ratio, 0.34; P = .006), peribronchovascular predilection (odds ratio, 9.2; P < .001), and higher BV5% (ß = 3.8; P = .01) but similar visual pneumonia extent (P = .17) and pneumonia volume (P = .67) relative to the Delta variant. Conclusion At chest CT, the Omicron SARS-COV-2 variant showed nontypical peribronchovascular pneumonia and less pulmonary vascular involvement than did the Delta variant in hospitalized patients with similar disease severity. © RSNA, 2022 Online supplemental material is available for this article.

Effect of Human-AI Interaction on Detection of Malignant Lung Nodules on Chest Radiographs.

Background The factors affecting radiologists' diagnostic determinations in artificial intelligence (AI)-assisted image reading remain underexplored. Purpose To assess how AI diagnostic performance and reader characteristics influence detection of malignant lung nodules during AI-assisted reading of chest radiographs. Materials and Methods This retrospective study consisted of two reading sessions from April 2021 to June 2021. Based on the first session without AI assistance, 30 readers were assigned into two groups with equivalent areas under the free-response receiver operating characteristic curve (AUFROCs). In the second session, each group reinterpreted radiographs assisted by either a high or low accuracy AI model (blinded to the fact that two different AI models were used). Reader performance for detecting lung cancer and reader susceptibility (changing the original reading following the AI suggestion) were compared. A generalized linear mixed model was used to identify the factors influencing AI-assisted detection performance, including readers' attitudes and experiences of AI and Grit score. Results Of the 120 chest radiographs assessed, 60 were obtained in patients with lung cancer (mean age, 67 years ± 12 [SD]; 32 male; 63 cancers) and 60 in controls (mean age, 67 years ± 12; 36 male). Readers included 20 thoracic radiologists (5-18 years of experience) and 10 radiology residents (2-3 years of experience). Use of the high accuracy AI model improved readers' detection performance to a greater extent than use of the low accuracy AI model (area under the receiver operating characteristic curve, 0.77 to 0.82 vs 0.75 to 0.75; AUFROC, 0.71 to 0.79 vs 0.7 to 0.72). Readers who used the high accuracy AI showed a higher susceptibility (67%, 224 of 334 cases) to changing their diagnosis based on the AI suggestions than those using the low accuracy AI (59%, 229 of 386 cases). Accurate readings at the first session, correct AI suggestions, high accuracy Al, and diagnostic difficulty were associated with accurate AI-assisted readings, but readers' characteristics were not. Conclusion An AI model with high diagnostic accuracy led to improved performance of radiologists in detecting lung cancer on chest radiographs and increased radiologists' susceptibility to AI suggestions. © RSNA, 2023 Supplemental material is available for this article.

An Integrated Radiologic-Pathologic Understanding of COVID-19 Pneumonia.

This article reviews the radiologic and pathologic findings of the epithelial and endothelial injuries in COVID-19 pneumonia to help radiologists understand the fundamental nature of the disease. The radiologic and pathologic manifestations of COVID-19 pneumonia result from epithelial and endothelial injuries based on viral toxicity and immunopathologic effects. The pathologic features of mild and reversible COVID-19 pneumonia involve nonspecific pneumonia or an organizing pneumonia pattern, while the pathologic features of potentially fatal and irreversible COVID-19 pneumonia are characterized by diffuse alveolar damage followed by fibrosis or acute fibrinous organizing pneumonia. These pathologic responses of epithelial injuries observed in COVID-19 pneumonia are not specific to SARS-CoV-2 but rather constitute universal responses to viral pneumonia. Endothelial injury in COVID-19 pneumonia is a prominent feature compared with other types of viral pneumonia and encompasses various vascular abnormalities at different levels, including pulmonary thromboembolism, vascular engorgement, peripheral vascular reduction, a vascular tree-in-bud pattern, and lung perfusion abnormality. Chest CT with different imaging techniques (eg, CT quantification, dual-energy CT perfusion) can fully capture the various manifestations of epithelial and endothelial injuries. CT can thus aid in establishing prognosis and identifying patients at risk for deterioration.

AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled Trial.

Background The impact of artificial intelligence (AI)-based computer-aided detection (CAD) software has not been prospectively explored in real-world populations. Purpose To investigate whether commercial AI-based CAD software could improve the detection rate of actionable lung nodules on chest radiographs in participants undergoing health checkups. Materials and Methods In this single-center, pragmatic, open-label randomized controlled trial, participants who underwent chest radiography between July 2020 and December 2021 in a health screening center were enrolled and randomized into intervention (AI group) and control (non-AI group) arms. One of three designated radiologists with 13-36 years of experience interpreted each radiograph, referring to the AI-based CAD results for the AI group. The primary outcome was the detection rate, that is, the number of true-positive radiographs divided by the total number of radiographs, of actionable lung nodules confirmed on CT scans obtained within 3 months. Actionable nodules were defined as solid nodules larger than 8 mm or subsolid nodules with a solid portion larger than 6 mm (Lung Imaging Reporting and Data System, or Lung-RADS, category 4). Secondary outcomes included the positive-report rate, sensitivity, false-referral rate, and malignant lung nodule detection rate. Clinical outcomes were compared between the two groups using univariable logistic regression analyses. Results A total of 10 476 participants (median age, 59 years [IQR, 50-66 years]; 5121 men) were randomized to an AI group (n = 5238) or non-AI group (n = 5238). The trial met the predefined primary outcome, demonstrating an improved detection rate of actionable nodules in the AI group compared with the non-AI group (0.59% [31 of 5238 participants] vs 0.25% [13 of 5238 participants], respectively; odds ratio, 2.4; 95% CI: 1.3, 4.7; P = .008). The detection rate for malignant lung nodules was higher in the AI group compared with the non-AI group (0.15% [eight of 5238 participants] vs 0.0% [0 of 5238 participants], respectively; P = .008). The AI and non-AI groups showed similar false-referral rates (45.9% [56 of 122 participants] vs 56.0% [56 of 100 participants], respectively; P = .14) and positive-report rates (2.3% [122 of 5238 participants] vs 1.9% [100 of 5238 participants]; P = .14). Conclusion In health checkup participants, artificial intelligence-based software improved the detection of actionable lung nodules on chest radiographs. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Auffermann in this isssue.

Value of CT quantification in progressive fibrosing interstitial lung disease: a deep learning approach.

OBJECTIVES: To evaluate the relationship of changes in the deep learning-based CT quantification of interstitial lung disease (ILD) with changes in forced vital capacity (FVC) and visual assessments of ILD progression, and to investigate their prognostic implications. METHODS: This study included ILD patients with CT scans at intervals of over 2 years between January 2015 and June 2021. Deep learning-based texture analysis software was used to segment ILD findings on CT images (fibrosis: reticular opacity + honeycombing cysts; total ILD extent: ground-glass opacity + fibrosis). Patients were grouped according to the absolute decline of predicted FVC (< 5%, 5-10%, and ≥ 10%) and ILD progression assessed by thoracic radiologists, and their quantification results were compared among these groups. The associations between quantification results and survival were evaluated using multivariable Cox regression analysis. RESULTS: In total, 468 patients (239 men; 64 ± 9.5 years) were included. Fibrosis and total ILD extents more increased in patients with larger FVC decline (p < .001 in both). Patients with ILD progression had higher fibrosis and total ILD extent increases than those without ILD progression (p < .001 in both). Increases in fibrosis and total ILD extent were significant prognostic factors when adjusted for absolute FVC declines of ≥ 5% (hazard ratio [HR] 1.844, p = .01 for fibrosis; HR 2.484, p < .001 for total ILD extent) and ≥ 10% (HR 2.918, p < .001 for fibrosis; HR 3.125, p < .001 for total ILD extent). CONCLUSION: Changes in ILD CT quantification correlated with changes in FVC and visual assessment of ILD progression, and they were independent prognostic factors in ILD patients. CLINICAL RELEVANCE STATEMENT: Quantifying the CT features of interstitial lung disease using deep learning techniques could play a key role in defining and predicting the prognosis of progressive fibrosing interstitial lung disease. KEY POINTS: • Radiologic findings on high-resolution CT are important in diagnosing progressive fibrosing interstitial lung disease. • Deep learning-based quantification results for fibrosis and total interstitial lung disease extents correlated with the decline in forced vital capacity and visual assessments of interstitial lung disease progression, and emerged as independent prognostic factors. • Deep learning-based interstitial lung disease CT quantification can play a key role in diagnosing and prognosticating progressive fibrosing interstitial lung disease.

Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.

OBJECTIVES: To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model. METHODS: DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes. Discrimination was evaluated using the time-dependent area under the receiver operating characteristic curve (TD-AUC) and compared with the DL discrete-time survival model. Additionally, we performed multivariable Cox regression analysis. RESULTS: In the temporal test set (640 patients; median age, 64 years), the TD-AUC was 0.79 for 5-year FFR and 0.73 for 5-year OS. In the external test set (846 patients; median age, 65 years), the TD-AUC was 0.71 for 5-year OS, equivalent to the pathologic stage (0.71 vs. 0.71 [p = 0.74]). The prognostic value of the DL score was independent of clinical factors (adjusted per-percentage hazard ratio for FFR (temporal test), 1.02 [95% CI: 1.01-1.03; p < 0.001]; OS (temporal test), 1.01 [95% CI: 1.002-1.02; p = 0.01]; OS (external test), 1.01 [95% CI: 1.005-1.02; p < 0.001]). Our model showed a higher TD-AUC than the DL discrete-time survival model, but without statistical significance (2.5-year OS: 0.73 vs. 0.68; p = 0.13). CONCLUSION: The CT-based prognostic score from collective deep learning of morphological and histopathological features showed potential in predicting survival in lung adenocarcinomas. CLINICAL RELEVANCE STATEMENT: Collective CT-based deep learning of morphological and histopathological features presents potential for enhancing lung adenocarcinoma prognostication and optimizing pre-/postoperative management. KEY POINTS: • A CT-based prognostic model was developed using collective deep learning of morphological and histopathological features from preoperative CT scans of 3181 patients with resected lung adenocarcinoma. • The prognostic performance of the model was comparable-to-higher performance than the pathologic T category or stage. • Our approach yielded a higher discrimination performance than the direct survival prediction model, but without statistical significance (0.73 vs. 0.68; p=0.13).

Complications and Technical Consideration of Ultrasound-Guided Rectus Sheath Blocks: A Retrospective Analysis of 4033 Patients.

BACKGROUND: Although the ultrasound-guided rectus sheath block (RSB) is usually regarded as an easy and safe procedure in clinical settings, there is currently no report on complications incidence. Therefore, the present study investigated complications in a large cohort and described the technical considerations to minimize complications of real-time ultrasound-guided RSBs. METHODS: This was a retrospective cohort study of patients who underwent real-time ultrasound-guided RSBs for perioperative pain control in laparoscopic surgery with an umbilical port between February 1, 2017, and February 28, 2021, at the Asan Medical Center in South Korea. All RSBs were performed bilaterally using a 23-gauge Quincke needle, and a bilateral 2-block placement was regarded as 1 RSB. Patient data, including demographics, preoperative laboratory data, preoperative antiplatelet or anticoagulant medication with the duration of discontinuation, and type of surgery, were collected to show the study population characteristics and explore potential factors associated with adverse events such as hematoma. Ultrasound images of patients and adverse events of RSBs, including extrarectus sheath injections, vascular injuries, bowel injury, or local anesthetic systemic toxicity, were also analyzed accordingly. RESULTS: A total of 4033 procedures were analyzed. The mean body mass index of the patients was 24.1 (21.8-26.5) kg/m2. The preoperative laboratory data were within normal range in 4028 (99.9%) patients. Preoperative antiplatelets or anticoagulants were administered in 17.3% of the patients. Overall, 96 complications (2.4%) were observed. Among them, extrarectus sheath injection occurred in 88 cases (2.2%), which included preperitoneal injection (0.9%) and intraperitoneal injection (1.3%). Vascular injuries constituted 8 cases (0.2%) and all vascular injuries resulted in hematoma: 7 cases of inferior epigastric artery injury with rectus sheath hematoma and 1 of inferior mesenteric artery injury with retroperitoneal hematoma. Bowel injury or local anesthetic systemic toxicity was not reported. CONCLUSIONS: In this study of RSBs performed on 4033 patients using a 23-gauge Quincke needle in patients with low body mass index, there were 8 cases (0.2%) of vascular injury, all of which accompanied hematoma.

Specific Signal Transduction of Constitutively Activating (D576G) and Inactivating (R476H) Mutants of Agonist-Stimulated Luteinizing Hormone Receptor in Eel.

We investigated the mechanism of signal transduction using inactivating (R476H) and activating (D576G) mutants of luteinizing hormone receptor (LHR) of eel at the conserved regions of intracellular loops II and III, respectively, naturally occurring in mammalian LHR. The expression of D576G and R476H mutants was approximately 58% and 59%, respectively, on the cell surface compared to those of eel LHR-wild type (wt). In eel LHR-wt, cAMP production increased upon agonist stimulation. Cells expressing eel LHR-D576G, a highly conserved aspartic acid residue, exhibited a 5.8-fold increase in basal cAMP response; however, the maximal cAMP response by high-agonist stimulation was approximately 0.62-fold. Mutation of a highly conserved arginine residue in the second intracellular loop of eel LHR (LHR-R476H) completely impaired the cAMP response. The rate of loss in cell-surface expression of eel LHR-wt and D576G mutant was similar to the agonist recombinant (rec)-eel LH after 30 min. However, the mutants presented rates of loss higher than eel LHR-wt did upon rec-eCG treatment. Therefore, the activating mutant constitutively induced cAMP signaling. The inactivating mutation resulted in the loss of LHR expression on the cell surface and no cAMP signaling. These data provide valuable information regarding the structure-function relationship of LHR-LH complexes.

Deep Learning for Detecting Pneumothorax on Chest Radiographs after Needle Biopsy: Clinical Implementation.

Background Accurate detection of pneumothorax on chest radiographs, the most common complication of percutaneous transthoracic needle biopsies (PTNBs), is not always easy in practice. A computer-aided detection (CAD) system may help detect pneumothorax. Purpose To investigate whether a deep learning-based CAD system can improve detection performance for pneumothorax on chest radiographs after PTNB in clinical practice. Materials and Methods A CAD system for post-PTNB pneumothorax detection on chest radiographs was implemented in an institution in February 2020. This retrospective cohort study consecutively included chest radiographs interpreted with CAD assistance (CAD-applied group; February 2020 to November 2020) and those interpreted before implementation (non-CAD group; January 2018 to January 2020). The reference standard was defined by consensus reading by two radiologists. The diagnostic accuracy for pneumothorax was compared between the two groups using generalized estimating equations. Matching was performed according to whether the radiograph reader and PTNB operator were the same using the greedy method. Results A total of 676 radiographs from 655 patients (mean age: 67 years ± 11; 390 men) in the CAD-applied group and 676 radiographs from 664 patients (mean age: 66 years ± 12; 400 men) in the non-CAD group were included. The incidence of pneumothorax was 18.2% (123 of 676 radiographs) in the CAD-applied group and 22.5% (152 of 676 radiographs) in the non-CAD group (P = .05). The CAD-applied group showed higher sensitivity (85.4% vs 67.1%), negative predictive value (96.8% vs 91.3%), and accuracy (96.8% vs 92.3%) than the non-CAD group (all P < .001). The sensitivity for a small amount of pneumothorax improved in the CAD-applied group (pneumothorax of <10%: 74.5% vs 51.4%, P = .009; pneumothorax of 10%-15%: 92.7% vs 70.2%, P = .008). Among patients with pneumothorax, 34 of 655 (5.0%) in the non-CAD group and 16 of 664 (2.4%) in the CAD-applied group (P = .009) required subsequent drainage catheter insertion. Conclusion A deep learning-based computer-aided detection system improved the detection performance for pneumothorax on chest radiographs after lung biopsy. © RSNA, 2022 See also the editorial by Schiebler and Hartung in this issue.

Deep Learning to Optimize Candidate Selection for Lung Cancer CT Screening: Advancing the 2021 USPSTF Recommendations.

Background A deep learning (DL) model to identify lung cancer screening candidates based on their chest radiographs requires external validation with a recent real-world non-U.S. sample. Purpose To validate the DL model and identify added benefits to the 2021 U.S. Preventive Services Task Force (USPSTF) recommendations in a health check-up sample. Materials and Methods This single-center retrospective study included consecutive current and former smokers aged 50-80 years who underwent chest radiography during a health check-up between January 2004 and June 2018. Discrimination performance, including receiver operating characteristic curve analysis and area under the receiver operating characteristic curve (AUC) calculation, of the model for incident lung cancers was evaluated. The added value of the model to the 2021 USPSTF recommendations was investigated for lung cancer inclusion rate, proportion of selected CT screening candidates, and positive predictive value (PPV). Results For model validation, a total of 19 488 individuals (mean age, 58 years ± 6 [SD]; 18 467 [95%] men) and the subset of USPSTF-eligible individuals (n = 7835; mean age, 57 years ± 6; 7699 [98%] men) were assessed, and the AUCs for incident lung cancers were 0.68 (95% CI: 0.62, 0.73) and 0.75 (95% CI: 0.68, 0.81), respectively. In individuals with pack-year information (n = 17 390), when excluding low- and indeterminate-risk categories from the USPSTF-eligible sample, the proportion of selected CT screening candidates was reduced to 35.8% (6233 of 17 390) from 45.1% (7835 of 17 390, P < .001), with three missed lung cancers (0.2%). The cancer inclusion rate (0.3% [53 of 17 390] vs 0.3% [56 of 17 390], P = .85) and PPV (0.9% [53 of 6233] vs 0.7% [56 of 7835], P = .42) remained unaffected. Conclusion An externally validated deep learning model showed the added value to the 2021 U.S. Preventive Services Task Force recommendations for low-dose CT lung cancer screening in reducing the number of screening candidates while maintaining the inclusion rate and positive predictive value for incident lung cancer. © RSNA, 2022 Online supplemental material is available for this article.

Pulmonary function and chest computed tomography abnormalities 6-12 months after recovery from COVID-19: a systematic review and meta-analysis.

BACKGROUND: Some coronavirus disease 2019 (COVID-19) survivors experience prolonged and varying symptoms, a condition termed post-acute COVID-19 syndrome (PACS). However, the prevalence of chronic pulmonary sequelae of PACS during long-term follow-up remains unclear. Several studies have examined this issue and reported heterogeneous results. METHODS: We conducted a systematic review and meta-analysis using a random-effects model to estimate the pooled prevalence of the pulmonary sequelae of COVID-19, as demonstrated by pulmonary function testing (PFT) and chest computed tomography (CT) performed at least 6 months after initial infection. PubMed, Embase, and Cochrane Library databases were searched from January 1, 2020 to December 31, 2021 to identify related studies. We investigated whether the prevalence of pulmonary sequelae decreased over time and attempted to identify the factors associated with their development by performing multiple subgroup and meta-regression analyses. RESULTS: Of the 18,062 studies identified, 30 met our eligibility criteria. Among these studies, 25 and 22 had follow-up PFT and chest CT data, respectively. The follow-up durations were approximately 6 and 12 months in 18 and 12 studies, respectively. Impaired diffusion capacity was the most common abnormality on PFT (pooled prevalence 35%, 95% confidence interval [CI] 30-41%) with a prevalence of 39% (95% CI 34-45%) and 31% (95% CI 21-40%) in the 6-month and 12-month follow-up studies, respectively (P = 0.115). Restrictive pulmonary dysfunction evident as reduced forced vital capacity was less frequent (pooled prevalence 8%, 95% CI 6-11%); however, its prevalence was lower in the 12-month follow-up studies than in the 6-month follow-up studies (5% [95% CI 3-7%] vs. 13% [95% CI 8-19%], P = 0.006). On follow-up chest CT, the pooled prevalence of persistent ground-glass opacities and pulmonary fibrosis was 34% (95% CI 24-44%) and 32% (95% CI 23-40%), respectively, and the prevalence did not decrease over time. As every meta-analysis showed significant between-study heterogeneity, subgroup and meta-regression analyses were performed to identify potential effect modifiers; the severity of index infection was associated with the prevalence of impaired diffusion capacity and pulmonary fibrosis. CONCLUSIONS: A substantial number of COVID-19 survivors displayed pulmonary sequelae as part of PACS. Except for restrictive pulmonary dysfunction, the prevalence of these sequelae did not decrease until 1 year after initial infection. Considering the association between the severity of acute COVID-19 and risk of pulmonary sequelae, patients who recover from severe COVID-19 require close respiratory follow-up. Systematic review registration number PROSPERO CRD42021234357.

Percutaneous transthoracic catheter drainage for lung abscess: a systematic review and meta-analysis.

OBJECTIVES: To evaluate the outcomes of patients receiving image-guided percutaneous catheter drainage (PCD) for lung abscesses in terms of treatment success, major complications, and mortality as well as the predictors of those outcomes. METHODS: Embase and OVID-MEDLINE databases were searched to identify studies on lung abscesses treated with PCD that had extractable outcomes. The outcomes were pooled using a random-intercept logistic regression model. Multivariate Firth's bias-reduced penalised-likelihood logistic regression analyses were performed to identify predictors of treatment success and complications. Methodological quality was assessed by summing scores of binary responses to items regarding selection, ascertainment of exposure and outcome, causality of follow-up duration, and reporting. RESULTS: From 26 studies with acceptable methodological quality (median score, 4; range, 3-5), 194 patients were included. The pooled rates of treatment success and major complications were 86.5% (95% confidence interval [CI], 78.5-91.8%; I2 = 23%) and 8.1% (95% CI, 4.1-15.3%; I2 = 26%), respectively. Four patients eventually died from uncontrolled lung abscesses (pooled rate, 1.5%; 95% CI, 0.2-11.1%; I2 = 36%). Malignancy-related abscess (odds ratio [OR], 0.129; 95% CI, 0.024-0.724; p = .022) and the occurrence of a major complication (OR, 0.065; 95% CI, 0.02-0.193; p < .001) were significant predictors of treatment failure. Traversing normal lung parenchyma was the only significant risk factor for major complications (OR, 27.69; 95% CI, 7.196-123.603; p < .001). CONCLUSION: PCD under imaging guidance was effective for lung abscess treatment, with a low complication rate. Traversal of normal lung parenchyma was the sole risk factor for complications, and malignancy-related abscesses and the occurrence of major complications were predictors of treatment failure. KEY POINTS: • The pooled treatment success rate of PCD for lung abscess was reasonably high (86.5%); malignancy-related abscesses and the occurrence of a major complication were predictors of treatment failure. • The pooled rate of percutaneous transthoracic catheter drainage-related major complications was 8.1% and traversing normal lung parenchyma by the catheter was the only risk factor. • The pooled mortality rate from uncontrolled lung abscesses with percutaneous transthoracic catheter drainage was low.

Cockayne syndrome group A and B proteins function in rRNA transcription through nucleolin regulation.

Cockayne Syndrome (CS) is a rare neurodegenerative disease characterized by short stature, accelerated aging and short lifespan. Mutations in two human genes, ERCC8/CSA and ERCC6/CSB, are causative for CS and their protein products, CSA and CSB, while structurally unrelated, play roles in DNA repair and other aspects of DNA metabolism in human cells. Many clinical and molecular features of CS remain poorly understood, and it was observed that CSA and CSB regulate transcription of ribosomal DNA (rDNA) genes and ribosome biogenesis. Here, we investigate the dysregulation of rRNA synthesis in CS. We report that Nucleolin (Ncl), a nucleolar protein that regulates rRNA synthesis and ribosome biogenesis, interacts with CSA and CSB. In addition, CSA induces ubiquitination of Ncl, enhances binding of CSB to Ncl, and CSA and CSB both stimulate the binding of Ncl to rDNA and subsequent rRNA synthesis. CSB and CSA also increase RNA Polymerase I loading to the coding region of the rDNA and this is Ncl dependent. These findings suggest that CSA and CSB are positive regulators of rRNA synthesis via Ncl regulation. Most CS patients carry mutations in CSA and CSB and present with similar clinical features, thus our findings provide novel insights into disease mechanism.