Reversal of cylindrical bronchial dilatations in a subset of adults with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor.

BACKGROUND: This study sought to evaluate the impact of elexacaftor-tezacaftor-ivacaftor (ETI) on lung structural abnormalities in adults with cystic fibrosis (awCF) with a specific focus on the reversal of bronchial dilatations. METHODS: Chest computed tomography (CT) performed prior to, and ≥12 months after initiation of ETI were visually reviewed for possible reversal of bronchial dilatations. AwCF with and without reversal of bronchial dilatation (the latter served as controls with 3 controls per case) were selected. Visual Brody score, bronchial and arterial diameters, and lung volume were measured on CT. RESULTS: Reversal of bronchial dilatation was found in 12/235 (5%) awCF treated with ETI. Twelve awCF with and 36 without reversal of bronchial dilatations were further analyzed (male=56%, mean age=31.6±8.5 years, F508del/F508del CFTR =54% and mean %predicted forced expiratory volume in 1 s=58.8%±22.3). The mean±sd Brody score improved overall from 79.4±29.8 to 54.8±32.3 (p<0.001). Reversal of bronchial dilatations was confirmed by a decrease in bronchial lumen diameter in cases from 3.9±0.9 mm to 3.2±1.1 mm (p<0.001), whereas it increased in awCF without reversal of bronchial dilatation (from 3.5±1.1 mm to 3.6±1.2 mm, p=0.002). Reversal of bronchial dilatations occurred in cylindrical (not varicose or saccular) bronchial dilatations. Lung volumes decreased by -6.6±10.7% in awCF with reversal of bronchial dilatation but increased by +2.3±9.6% in controls (p=0.007). CONCLUSION: Although bronchial dilatations are generally considered irreversible, ETI was associated with reversal, which was limited to the cylindrical bronchial dilatations subtype, and occurred in a small subset of awCF. Initiating ETI earlier in life may reverse early bronchial dilatations.

Anti-U1RNP antibodies are associated with a distinct clinical phenotype and a worse survival in patients with systemic sclerosis.

OBJECTIVES: To clarify the impact of anti-U1RNP antibodies on the clinical features and prognosis of patients with SSc. METHODS: We conducted a monocentric case-control, retrospective, longitudinal study. For each patient with SSc and anti-U1RNP antibodies (SSc-RNP+), one patient with mixed connective tissue disease (MCTD) and 2 SSc patients without anti-U1RNP antibodies (SSc-RNP-) were matched for age, sex, and date of inclusion. RESULTS: Sixty-four SSc-RNP+ patients were compared to 128 SSc-RNP- and 64 MCTD patients. Compared to SSc-RNP-, SSc-RNP+ patients were more often of Afro-Caribbean origin (31.3% vs. 11%, p < 0.01), and more often had an overlap syndrome than SSc-RNP- patients (53.1 % vs. 22.7%, p < 0.0001), overlapping with Sjögren's syndrome (n = 23, 35.9%) and/or systemic lupus erythematosus (n = 19, 29.7%). SSc-RNP+ patients were distinctly different from MCTD patients but less often had joint involvement (p < 0.01). SSc-RNP+ patients more frequently developed interstitial lung disease (ILD) (73.4% vs. 55.5% vs. 31.3%, p < 0.05), pulmonary fibrosis (PF) (60.9% vs. 37.5% vs. 10.9%, p < 0.0001), SSc associated myopathy (29.7% vs. 6.3% vs. 7.8%, p < 0.0001), and kidney involvement (10.9% vs. 2.3% vs. 1.6%, p < 0.05). Over a 200-month follow-up period, SSc-RNP+ patients had worse overall survival (p < 0.05), worse survival without PF occurrence (p < 0.01), ILD or PF progression (p < 0.01 and p < 0.0001). CONCLUSIONS: In SSc patients, anti-U1RNP antibodies are associated with a higher incidence of overlap syndrome, a distinct clinical phenotype, and poorer survival compared to SSc-RNP- and MCTD patients. Our study suggests that SSc-RNP+ patients should be separated from MCTD patients and may constitute an enriched population for progressive lung disease.

Prognostic value of automated assessment of interstitial lung disease on CT in systemic sclerosis.

OBJECTIVE: Stratifying the risk of death in SSc-related interstitial lung disease (SSc-ILD) is a challenging issue. The extent of lung fibrosis on high-resolution CT (HRCT) is often assessed by a visual semiquantitative method that lacks reliability. We aimed to assess the potential prognostic value of a deep-learning-based algorithm enabling automated quantification of ILD on HRCT in patients with SSc. METHODS: We correlated the extent of ILD with the occurrence of death during follow-up, and evaluated the additional value of ILD extent in predicting death based on a prognostic model including well-known risk factors in SSc. RESULTS: We included 318 patients with SSc, among whom 196 had ILD; the median follow-up was 94 months (interquartile range 73-111). The mortality rate was 1.6% at 2 years and 26.3% at 10 years. For each 1% increase in the baseline ILD extent (up to 30% of the lung), the risk of death at 10 years was increased by 4% (hazard ratio 1.04, 95% CI 1.01, 1.07, P = 0.004). We constructed a risk prediction model that showed good discrimination for 10-year mortality (c index 0.789). Adding the automated quantification of ILD significantly improved the model for 10-year survival prediction (P = 0.007). Its discrimination was only marginally improved, but it improved prediction of 2-year mortality (difference in time-dependent area under the curve 0.043, 95% CI 0.002, 0.084, P = 0.040). CONCLUSION: The deep-learning-based, computer-aided quantification of ILD extent on HRCT provides an effective tool for risk stratification in SSc. It might help identify patients at short-term risk of death.

CT venography for the diagnosis of postpartum venous thromboembolism: a prospective multi-center cohort study.

OBJECTIVES: To assess the role of CT venography (CTV) in the diagnosis of venous thromboembolism (VTE) during the postpartum period. MATERIALS AND METHODS: This multicenter prospective cohort study was conducted between April 2016 and April 2020 in 14 university hospitals. All women referred for CT pulmonary angiography (CTPA) for suspected pulmonary embolism (PE) within the first 6 weeks postpartum were eligible. All CTPAs were performed on multidetector CT machines with the usual parameters and followed by CTV of the abdomen, pelvis, and proximal lower limbs. On-site reports were compared to expert consensus reading, and the added value of CTV was assessed for both. RESULTS: The final study population consisted of 123 women. On-site CTPA reports mentioned PE in seven women (7/123, 5.7%), all confirmed following expert consensus reading, three involving proximal pulmonary arteries and four limited to distal arteries. Positive CTV was reported on-site in nine women, five of whom had negative and two indeterminate CTPAs, bringing the VTE detection rate to 11.4% (14/123) (95%CI: 6.4-18.4, p = 0.03). Expert consensus reading confirmed all positive on-site CTV results, but detected a periuterine vein thrombosis in an additional woman who had a negative CTPA, increasing the VTE detection rate to 12.2% (15/123) (95%CI: 7.0-19.3, p = 0.008). Follow-up at 3 months revealed no adverse events in this woman, who was left untreated. Median Dose-Length-Product was 117 mGy.cm for CTPA and 675 mGy.cm for CTPA + CTV. CONCLUSION: Performing CTV in women suspected of postpartum PE doubles the detection of venous thromboembolism, at the cost of increased radiation exposure. CLINICAL RELEVANCE STATEMENT: CTV can help in the decision-making process concerning curative anticoagulation in women with suspected postpartum PE, particularly those whose CTPA results are indeterminate or whose PE is limited to the subsegmental level. KEY POINTS: Postpartum women are at risk of pulmonary embolism, and CT pulmonary angiography can give equivocal results. CT venography (CTV) positivity increased the venous thromboembolism detection rate from 5.7 to 11.4%. CTV may help clinical decision-making, especially in women with indeterminate CTPA results or subsegmental emboli.

Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs.

Background Chest radiography remains the most common radiologic examination, and interpretation of its results can be difficult. Purpose To explore the potential benefit of artificial intelligence (AI) assistance in the detection of thoracic abnormalities on chest radiographs by evaluating the performance of radiologists with different levels of expertise, with and without AI assistance. Materials and Methods Patients who underwent both chest radiography and thoracic CT within 72 hours between January 2010 and December 2020 in a French public hospital were screened retrospectively. Radiographs were randomly included until reaching 500 radiographs, with about 50% of radiographs having abnormal findings. A senior thoracic radiologist annotated the radiographs for five abnormalities (pneumothorax, pleural effusion, consolidation, mediastinal and hilar mass, lung nodule) based on the corresponding CT results (ground truth). A total of 12 readers (four thoracic radiologists, four general radiologists, four radiology residents) read half the radiographs without AI and half the radiographs with AI (ChestView; Gleamer). Changes in sensitivity and specificity were measured using paired t tests. Results The study included 500 patients (mean age, 54 years ± 19 [SD]; 261 female, 239 male), with 522 abnormalities visible on 241 radiographs. On average, for all readers, AI use resulted in an absolute increase in sensitivity of 26% (95% CI: 20, 32), 14% (95% CI: 11, 17), 12% (95% CI: 10, 14), 8.5% (95% CI: 6, 11), and 5.9% (95% CI: 4, 8) for pneumothorax, consolidation, nodule, pleural effusion, and mediastinal and hilar mass, respectively (P < .001). Specificity increased with AI assistance (3.9% [95% CI: 3.2, 4.6], 3.7% [95% CI: 3, 4.4], 2.9% [95% CI: 2.3, 3.5], and 2.1% [95% CI: 1.6, 2.6] for pleural effusion, mediastinal and hilar mass, consolidation, and nodule, respectively), except in the diagnosis of pneumothorax (-0.2%; 95% CI: -0.36, -0.04; P = .01). The mean reading time was 81 seconds without AI versus 56 seconds with AI (31% decrease, P < .001). Conclusion AI-assisted chest radiography interpretation resulted in absolute increases in sensitivity for all radiologists of various levels of expertise and reduced the reading times; specificity increased with AI, except in the diagnosis of pneumothorax. © RSNA, 2023 Supplemental material is available for this article.

The association between air pollution and the severity at diagnosis and progression of systemic sclerosis-associated interstitial lung disease: results from the retrospective ScleroPol study.

OBJECTIVE: To investigate the association of air pollution exposure with the severity of interstitial lung disease (ILD) at diagnosis and ILD progression among patients with systemic sclerosis (SSc)-associated ILD. METHODS: We conducted a retrospective two-center study of patients with SSc-associated ILD diagnosed between 2006 and 2019. Exposure to the air pollutants particulate matter of up to 10 and 2.5 µm in diameter (PM10, PM2.5), nitrogen dioxide (NO2), and ozone (O3) was assessed at the geolocalization coordinates of the patients' residential address. Logistic regression models were used to evaluate the association between air pollution and severity at diagnosis according to the Goh staging algorithm, and progression at 12 and 24 months. RESULTS: We included 181 patients, 80% of whom were women; 44% had diffuse cutaneous scleroderma, and 56% had anti-topoisomerase I antibodies. ILD was extensive, according to the Goh staging algorithm, in 29% of patients. O3 exposure was associated with the presence of extensive ILD at diagnosis (adjusted OR: 1.12, 95% CI 1.05-1.21; p value = 0.002). At 12 and 24 months, progression was noted in 27/105 (26%) and 48/113 (43%) patients, respectively. O3 exposure was associated with progression at 24 months (adjusted OR: 1.10, 95% CI 1.02-1.19; p value = 0.02). We found no association between exposure to other air pollutants and severity at diagnosis and progression. CONCLUSION: Our findings suggest that high levels of O3 exposure are associated with more severe SSc-associated ILD at diagnosis, and progression at 24 months.

Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation.

OBJECTIVES: To assess whether a computer-aided detection (CADe) system could serve as a learning tool for radiology residents in chest X-ray (CXR) interpretation. METHODS: Eight radiology residents were asked to interpret 500 CXRs for the detection of five abnormalities, namely pneumothorax, pleural effusion, alveolar syndrome, lung nodule, and mediastinal mass. After interpreting 150 CXRs, the residents were divided into 2 groups of equivalent performance and experience. Subsequently, group 1 interpreted 200 CXRs from the "intervention dataset" using a CADe as a second reader, while group 2 served as a control by interpreting the same CXRs without the use of CADe. Finally, the 2 groups interpreted another 150 CXRs without the use of CADe. The sensitivity, specificity, and accuracy before, during, and after the intervention were compared. RESULTS: Before the intervention, the median individual sensitivity, specificity, and accuracy of the eight radiology residents were 43% (range: 35-57%), 90% (range: 82-96%), and 81% (range: 76-84%), respectively. With the use of CADe, residents from group 1 had a significantly higher overall sensitivity (53% [n = 431/816] vs 43% [n = 349/816], p < 0.001), specificity (94% [i = 3206/3428] vs 90% [n = 3127/3477], p < 0.001), and accuracy (86% [n = 3637/4244] vs 81% [n = 3476/4293], p < 0.001), compared to the control group. After the intervention, there were no significant differences between group 1 and group 2 regarding the overall sensitivity (44% [n = 309/696] vs 46% [n = 317/696], p = 0.666), specificity (90% [n = 2294/2541] vs 90% [n = 2285/2542], p = 0.642), or accuracy (80% [n = 2603/3237] vs 80% [n = 2602/3238], p = 0.955). CONCLUSIONS: Although it improves radiology residents' performances for interpreting CXRs, a CADe system alone did not appear to be an effective learning tool and should not replace teaching. CLINICAL RELEVANCE STATEMENT: Although the use of artificial intelligence improves radiology residents' performance in chest X-rays interpretation, artificial intelligence cannot be used alone as a learning tool and should not replace dedicated teaching. KEY POINTS: • With CADe as a second reader, residents had a significantly higher sensitivity (53% vs 43%, p < 0.001), specificity (94% vs 90%, p < 0.001), and accuracy (86% vs 81%, p < 0.001), compared to residents without CADe. • After removing access to the CADe system, residents' sensitivity (44% vs 46%, p = 0.666), specificity (90% vs 90%, p = 0.642), and accuracy (80% vs 80%, p = 0.955) returned to that of the level for the group without CADe.

Strategies to safely rule out pulmonary embolism in COVID-19 outpatients: a multicenter retrospective study.

OBJECTIVES: The objective was to define a safe strategy to exclude pulmonary embolism (PE) in COVID-19 outpatients, without performing CT pulmonary angiogram (CTPA). METHODS: COVID-19 outpatients from 15 university hospitals who underwent a CTPA were retrospectively evaluated. D-Dimers, variables of the revised Geneva and Wells scores, as well as laboratory findings and clinical characteristics related to COVID-19 pneumonia, were collected. CTPA reports were reviewed for the presence of PE and the extent of COVID-19 disease. PE rule-out strategies were based solely on D-Dimer tests using different thresholds, the revised Geneva and Wells scores, and a COVID-19 PE prediction model built on our dataset were compared. The area under the receiver operating characteristics curve (AUC), failure rate, and efficiency were calculated. RESULTS: In total, 1369 patients were included of whom 124 were PE positive (9.1%). Failure rate and efficiency of D-Dimer > 500 µg/l were 0.9% (95%CI, 0.2-4.8%) and 10.1% (8.5-11.9%), respectively, increasing to 1.0% (0.2-5.3%) and 16.4% (14.4-18.7%), respectively, for an age-adjusted D-Dimer level. D-dimer > 1000 µg/l led to an unacceptable failure rate to 8.1% (4.4-14.5%). The best performances of the revised Geneva and Wells scores were obtained using the age-adjusted D-Dimer level. They had the same failure rate of 1.0% (0.2-5.3%) for efficiency of 16.8% (14.7-19.1%), and 16.9% (14.8-19.2%) respectively. The developed COVID-19 PE prediction model had an AUC of 0.609 (0.594-0.623) with an efficiency of 20.5% (18.4-22.8%) when its failure was set to 0.8%. CONCLUSIONS: The strategy to safely exclude PE in COVID-19 outpatients should not differ from that used in non-COVID-19 patients. The added value of the COVID-19 PE prediction model is minor. KEY POINTS: • D-dimer level remains the most important predictor of pulmonary embolism in COVID-19 patients. • The AUCs of the revised Geneva and Wells scores using an age-adjusted D-dimer threshold were 0.587 (95%CI, 0.572 to 0.603) and 0.588 (95%CI, 0.572 to 0.603). • The AUC of COVID-19-specific strategy to rule out pulmonary embolism ranged from 0.513 (95%CI: 0.503 to 0.522) to 0.609 (95%CI: 0.594 to 0.623).

The strategic roles of four enzymes in the interconnection between metabolism and oncogene activation in non-small cell lung cancer: Therapeutic implications.

NSCLC is the leading cause of cancer mortality and represents a major challenge in cancer therapy. Intrinsic and acquired anticancer drug resistance are promoted by hypoxia and HIF-1α. Moreover, chemoresistance is sustained by the activation of key signaling pathways (such as RAS and its well-known downstream targets PI3K/AKT and MAPK) and several mutated oncogenes (including KRAS and EGFR among others). In this review, we highlight how these oncogenic factors are interconnected with cell metabolism (aerobic glycolysis, glutaminolysis and lipid synthesis). Also, we stress the key role of four metabolic enzymes (PFK1, dimeric-PKM2, GLS1 and ACLY), which promote the activation of these oncogenic pathways in a positive feedback loop. These four tenors orchestrating the coordination of metabolism and oncogenic pathways could be key druggable targets for specific inhibition. Since PFK1 appears as the first tenor of this orchestra, its inhibition (and/or that of its main activator PFK2/PFKFB3) could be an efficacious strategy against NSCLC. Citrate is a potent physiologic inhibitor of both PFK1 and PFKFB3, and NSCLC cells seem to maintain a low citrate level to sustain aerobic glycolysis and the PFK1/PI3K/EGFR axis. Awaiting the development of specific non-toxic inhibitors of PFK1 and PFK2/PFKFB3, we propose to test strategies increasing citrate levels in NSCLC tumors to disrupt this interconnection. This could be attempted by evaluating inhibitors of the citrate-consuming enzyme ACLY and/or by direct administration of citrate at high doses. In preclinical models, this "citrate strategy" efficiently inhibits PFK1/PFK2, HIF-1α, and IGFR/PI3K/AKT axes. It also blocks tumor growth in RAS-driven lung cancer models, reversing dedifferentiation, promoting T lymphocytes tumor infiltration, and increasing sensitivity to cytotoxic drugs.

Revisiting characteristics, treatment and outcome of cardiomyopathy in eosinophilic granulomatosis with polyangiitis (formerly Churg-Strauss).

OBJECTIVES: Eosinophilic granulomatosis with polyangiitis (EGPA) is a necrotizing eosinophil-rich vasculitis. Specific cardiomyopathy (CM) was described in early studies as the most important predictor of mortality. We aimed to revisit EGPA-related CM and investigate its outcome in recent decades. METHODS: We reviewed all EGPA patients managed from 2000 to 2019 in our vasculitis clinic. Baseline characteristics and outcomes were analysed. EGPA-related CM was defined as clinical or extra-clinical manifestations of patent myocardial involvement, after exclusion of other causes. RESULTS: We included 176 patients. The median age was 47 years [interquartile range (IQR) 36-58 years]. Specific CM was observed in 70 patients (40%). Cardiac symptoms were observed in 81% of CM+ patients, including mainly typical or atypical chest pain and peripheral oedema. Abnormal ECG, transthoracic echocardiography and cardiac MRI (CMRI) were found in 72%, 72% and 99% of CM+ patients, respectively, contrasting with abnormalities in 32%, 38% and 60% of CM-negative patients, respectively. Late gadolinium enhancement (LGE) was the most frequent abnormality on CMRI (70%). CM+ patients were less frequently ANCA-positive, had less frequent peripheral neuropathy and had higher eosinophil count. Major adverse cardiovascular events (MACEs) occurred in 13% of patients, both in CM+ and CM- patients. Abnormal ECG and LGE on CMRI were associated with the occurrence of MACEs. Four patients died, but none from cardiac causes. CONCLUSION: Specific cardiomyopathy is frequent in EGPA, especially in ANCA-negative patients with high eosinophil counts. Long-term outcome was better than previously reported. Abnormal ECG and LGE on CMRI were associated with the occurrence of MACEs.

What level of D-dimers can safely exclude pulmonary embolism in COVID-19 patients presenting to the emergency department?

OBJECTIVES: To identify which level of D-dimer would allow the safe exclusion of pulmonary embolism (PE) in COVID-19 patients presenting to the emergency department (ED). METHODS: This retrospective study was conducted on the COVID database of Assistance Publique - Hôpitaux de Paris (AP-HP). COVID-19 patients who presented at the ED of AP-HP hospitals between March 1 and May 15, 2020, and had CTPA following D-dimer dosage within 48h of presentation were included. The D-dimer sensitivity, specificity, and positive and negative predictive values were calculated for different D-dimer thresholds, as well as the false-negative and failure rates, and the number of CTPAs potentially avoided. RESULTS: A total of 781 patients (mean age 62.0 years, 53.8% men) with positive RT-PCR for SARS-Cov-2 were included and 60 of them (7.7%) had CTPA-confirmed PE. Their median D-dimer level was significantly higher than that of patients without PE (4,013 vs 1,198 ng·mL-1, p < 0.001). Using 500 ng·mL-1, or an age-adjusted cut-off for patients > 50 years, the sensitivity and the NPV were above 90%. With these thresholds, 17.1% and 31.5% of CTPAs could have been avoided, respectively. Four of the 178 patients who had a D-dimer below the age-adjusted cutoff had PE, leading to an acceptable failure rate of 2.2%. Using higher D-dimer cut-offs could have avoided more CTPAs, but would have lowered the sensitivity and increased the failure rate. CONCLUSION: The same D-Dimer thresholds as those validated in non-COVID outpatients should be used to safely rule out PE. KEY POINTS: • The median D-dimer level was significantly higher in COVID-19 patients with PE as compared to those without PE (4,013 ng·mL-1 vs 1,198 ng·mL-1 respectively, p < 0.001). • Using 500 ng·mL-1, or an age-adjusted D-dimer cut-off to exclude pulmonary embolism, the sensitivity and negative predictive value were above 90%. • Higher cut-offs would lead to a reduction in the sensitivity below 85% and an increase in the failure rate, especially for patients under 50 years.

Elastic Registration-driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT.

Background Longitudinal follow-up of interstitial lung diseases (ILDs) at CT mainly relies on the evaluation of the extent of ILD, without accounting for lung shrinkage. Purpose To develop a deep learning-based method to depict worsening of ILD based on lung shrinkage detection from elastic registration of chest CT scans in patients with systemic sclerosis (SSc). Materials and Methods Patients with SSc evaluated between January 2009 and October 2017 who had undergone at least two unenhanced supine CT scans of the chest and pulmonary function tests (PFTs) performed within 3 months were retrospectively included. Morphologic changes on CT scans were visually assessed by two observers and categorized as showing improvement, stability, or worsening of ILD. Elastic registration between baseline and follow-up CT images was performed to obtain deformation maps of the whole lung. Jacobian determinants calculated from the deformation maps were given as input to a deep learning-based classifier to depict morphologic and functional worsening. For this purpose, the set was randomly split into training, validation, and test sets. Correlations between mean Jacobian values and changes in PFT measurements were evaluated with the Spearman correlation. Results A total of 212 patients (median age, 53 years; interquartile range, 45-62 years; 177 women) were included as follows: 138 for the training set (65%), 34 for the validation set (16%), and 40 for the test set (21%). Jacobian maps demonstrated lung parenchyma shrinkage of the posterior lung bases in patients found to have worsened ILD at visual assessment. The classifier detected morphologic and functional worsening with an accuracy of 80% (32 of 40 patients; 95% confidence interval [CI]: 64%, 91%) and 83% (33 of 40 patients; 95% CI: 67%, 93%), respectively. Jacobian values correlated with changes in forced vital capacity (R = -0.38; 95% CI: -0.25, -0.49; P < .001) and diffusing capacity for carbon monoxide (R = -0.42; 95% CI: -0.27, -0.54; P < .001). Conclusion Elastic registration of CT scans combined with a deep learning classifier aided in the diagnosis of morphologic and functional worsening of interstitial lung disease in patients with systemic sclerosis. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Verschakelen in this issue.

Study of Thoracic CT in COVID-19: The STOIC Project.

Background There are conflicting data regarding the diagnostic performance of chest CT for COVID-19 pneumonia. Disease extent at CT has been reported to influence prognosis. Purpose To create a large publicly available data set and assess the diagnostic and prognostic value of CT in COVID-19 pneumonia. Materials and Methods This multicenter, observational, retrospective cohort study involved 20 French university hospitals. Eligible patients presented at the emergency departments of the hospitals involved between March 1 and April 30th, 2020, and underwent both thoracic CT and reverse transcription-polymerase chain reaction (RT-PCR) testing for suspected COVID-19 pneumonia. CT images were read blinded to initial reports, RT-PCR, demographic characteristics, clinical symptoms, and outcome. Readers classified CT scans as either positive or negative for COVID-19 based on criteria published by the French Society of Radiology. Multivariable logistic regression was used to develop a model predicting severe outcome (intubation or death) at 1-month follow-up in patients positive for both RT-PCR and CT, using clinical and radiologic features. Results Among 10 930 patients screened for eligibility, 10 735 (median age, 65 years; interquartile range, 51-77 years; 6147 men) were included and 6448 (60%) had a positive RT-PCR result. With RT-PCR as reference, the sensitivity and specificity of CT were 80.2% (95% CI: 79.3, 81.2) and 79.7% (95% CI: 78.5, 80.9), respectively, with strong agreement between junior and senior radiologists (Gwet AC1 coefficient, 0.79). Of all the variables analyzed, the extent of pneumonia at CT (odds ratio, 3.25; 95% CI: 2.71, 3.89) was the best predictor of severe outcome at 1 month. A score based solely on clinical variables predicted a severe outcome with an area under the curve of 0.64 (95% CI: 0.62, 0.66), improving to 0.69 (95% CI: 0.6, 0.71) when it also included the extent of pneumonia and coronary calcium score at CT. Conclusion Using predefined criteria, CT reading is not influenced by reader's experience and helps predict the outcome at 1 month. ClinicalTrials.gov identifier: NCT04355507 Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Rubin in this issue.

Using chest CT scan and unsupervised machine learning for predicting and evaluating response to lumacaftor-ivacaftor in people with cystic fibrosis.

OBJECTIVES: Lumacaftor-ivacaftor is a cystic fibrosis transmembrane conductance regulator (CFTR) modulator known to improve clinical status in people with cystic fibrosis (CF). This study aimed to assess lung structural changes after one year of lumacaftor-ivacaftor treatment, and to use unsupervised machine learning to identify morphological phenotypes of lung disease that are associated with response to lumacaftor-ivacaftor. METHODS: Adolescents and adults with CF from the French multicenter real-world prospective observational study evaluating the first year of treatment with lumacaftor-ivacaftor were included if they had pretherapeutic and follow-up chest computed tomography (CT)-scans available. CT scans were visually scored using a modified Bhalla score. A k-mean clustering method was performed based on 120 radiomics features extracted from unenhanced pretherapeutic chest CT scans. RESULTS: A total of 283 patients were included. The Bhalla score significantly decreased after 1 year of lumacaftor-ivacaftor (-1.40±1.53 points compared with pretherapeutic CT; p<0.001). This finding was related to a significant decrease in mucus plugging (-0.35±0.62 points; p<0.001), bronchial wall thickening (-0.24±0.52 points; p<0.001) and parenchymal consolidations (-0.23±0.51 points; p<0.001). Cluster analysis identified 3 morphological clusters. Patients from cluster C were more likely to experience an increase in percent predicted forced expiratory volume in 1 sec (ppFEV1) ≥5 under lumacaftor-ivacaftor than those in the other clusters (54% of responders versus 32% and 33%; p=0.01). CONCLUSION: One year treatment with lumacaftor-ivacaftor was associated with a significant visual improvement of bronchial disease on chest CT. Radiomics features on pretherapeutic CT scan may help in predicting lung function response under lumacaftor-ivacaftor.

Deep learning: definition and perspectives for thoracic imaging.

Relevance and penetration of machine learning in clinical practice is a recent phenomenon with multiple applications being currently under development. Deep learning-and especially convolutional neural networks (CNNs)-is a subset of machine learning, which has recently entered the field of thoracic imaging. The structure of neural networks, organized in multiple layers, allows them to address complex tasks. For several clinical situations, CNNs have demonstrated superior performance as compared with classical machine learning algorithms and in some cases achieved comparable or better performance than clinical experts. Chest radiography, a high-volume procedure, is a natural application domain because of the large amount of stored images and reports facilitating the training of deep learning algorithms. Several algorithms for automated reporting have been developed. The training of deep learning algorithm CT images is more complex due to the dimension, variability, and complexity of the 3D signal. The role of these methods is likely to increase in clinical practice as a complement of the radiologist's expertise. The objective of this review is to provide definitions for understanding the methods and their potential applications for thoracic imaging. KEY POINTS: • Deep learning outperforms other machine learning techniques for number of tasks in radiology. • Convolutional neural network is the most popular deep learning architecture in medical imaging. • Numerous deep learning algorithms are being currently developed; some of them may become part of clinical routine in the near future.

Can We Reduce Frame Rate to 15 Images per Second in Pediatric Videofluoroscopic Swallow Studies?

Videofluoroscopic Swallow studies (VFSS) are useful radiological examinations to explore swallowing disorders but which require ionizing radiation. The aim of our study was to evaluate the comparability of pediatric VFSS at 15 frames per second (fps) with 30 fps. Fifty-five loops including 190 swallowings of VFSS at 30 fps performed on 32 consecutive pediatric patients in a University Hospital Center were retrospectively modified by a software to delete one image out of two to obtain secondary loops with a frame rate of 15 fps. An otorhinolaryngologist-phonatrician and a radiologist reviewed all swallowings blindly and randomly using the penetration and aspiration scale (PAS). In case of discordance, they concluded a consensual interpretation. Fifteen girls and seventeen boys were included. The median age was 4 years and 8 months (range = 4 months-16 yr.). 144 swallowings were normal. Swallowing disorder was confirmed in 46 swallowings, (23 supraglottic penetrations and 23 aspirations). Considering each swallowing at 15 fps, sensitivity and specificity were, respectively, 93% (CI 0.82-0.98) and 98% (CI 0.94-0.99). The Cohen'Kappa coefficient between each interpretation at 15 and 30 fps was "almost perfect" (κ = 0.95; CI 0.88-0.99). Considering each loop, conclusion was identical. Reducing frame rate at 15 fps during pediatric VFSS seemed to be acceptable with comparable diagnostic performances without clinical impact compared to 30 fps, while being an efficient way to reduce the ionizing radiation exposition in children. We would suggest reconsidering the possibility of using VFSS with a 15 fps in a pediatric population.

Use of Elastic Registration in Pulmonary MRI for the Assessment of Pulmonary Fibrosis in Patients with Systemic Sclerosis.

Background Current imaging methods are not sensitive to changes in pulmonary function resulting from fibrosis. MRI with ultrashort echo time can be used to image the lung parenchyma and lung motion. Purpose To evaluate elastic registration of inspiratory-to-expiratory lung MRI for the assessment of pulmonary fibrosis in study participants with systemic sclerosis (SSc). Materials and Methods This prospective study was performed from September 2017 to March 2018 and recruited healthy volunteers and participants with SSc and high-resolution CT (within the previous 3 months) of the chest for lung MRI. Two breath-hold, coronal, three-dimensional, ultrashort-echo-time, gradient-echo sequences of the lungs were acquired after full inspiration and expiration with a 3.0-T unit. Images were registered from inspiration to expiration by using an elastic registration algorithm. Jacobian determinants were calculated from deformation fields and represented on color maps. Similarity between areas with marked shrinkage and logarithm of Jacobian determinants less than -0.15 were compared between healthy volunteers and study participants with SSc. Receiver operating characteristic curve analysis was performed to determine the best Dice similarity coefficient threshold for diagnosis of fibrosis. Results Sixteen participants with SSc (seven with pulmonary fibrosis at high-resolution CT) and 11 healthy volunteers were evaluated. Areas of marked shrinkage during expiration with logarithm of Jacobian determinants less than -0.15 were found in the posterior lung bases of healthy volunteers and in participants with SSc without fibrosis, but not in participants with fibrosis. The sensitivity and specificity of MRI for presence of fibrosis at high-resolution CT were 86% and 75%, respectively (area under the curve, 0.81; P = .04) by using a threshold of 0.36 for Dice similarity coefficient. Conclusion Elastic registration of inspiratory-to-expiratory MRI shows less lung base respiratory deformation in study participants with systemic sclerosis-related pulmonary fibrosis compared with participants without fibrosis. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Biederer in this issue.

Does Ipsilateral-Dependent Positioning During Percutaneous Lung Biopsy Decrease the Risk of Pneumothorax?

OBJECTIVE: The purpose of this study is to determine whether placing patients in an ipsilateral-dependent position during percutaneous CT-guided transthoracic biopsy reduces the pneumothorax rate. MATERIALS AND METHODS: Between July 2013 and August 2017, a total of 516 patients (317 men and 199 women; mean age, 66.4 years) underwent core needle biopsies performed using 17- and 18-gauge needles. The overall pneumothorax rate and the rate of pneumothorax requiring drainage catheter insertion were compared between group A (patients placed in an ipsilateral-dependent position) and group B (patients placed in a position other than the ipsilateral-dependent position), with use of a chi-square test or Fisher exact test, as appropriate. Linear regression analysis and multiple regression analysis were performed for risk factors of pneumothorax, including patient characteristics (e.g., emphysema along the needle track), lesion characteristics (e.g., size and position), and biopsy technique characteristics (e.g., needle path length, needle-pleura angle, and fissure crossing). RESULTS: For patients in group A and group B, the overall pneumothorax rate (21/94 [22.3%] and 95/422 [22.5%], respectively; p = 0.97) and the rate of pneumothorax requiring drainage catheter insertion (6/94 [6.4%] and 28/422 [6.6%], respectively; p = 0.90) were not statistically different. After multiple regression analysis, the only independent risk factors for pneumothorax and insertion of a drainage catheter were needle path length (p < 0.001 and p = 0.02, respectively), emphysema along the needle track (p = 0.01 and p < 0.001, respectively), and fissure crossing (p = 0.04 and p < 0.001, respectively). CONCLUSION: Even though the pneumothorax rate does not appear to be reduced, with the limits of a retrospective evaluation considered, other advantages of the ipsilateral decubitus position exist, including protection of the contralateral lung in patients with severe hemoptysis.