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.

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.

ERS/ESTS/ESTRO/ESR/ESTI/EFOMP statement on management of incidental findings from low dose CT screening for lung cancer.

BACKGROUND: Screening for lung cancer with low radiation dose computed tomography has a strong evidence base, is being introduced in several European countries and is recommended as a new targeted cancer screening programme. The imperative now is to ensure that implementation follows an evidence-based process that will ensure clinical and cost effectiveness. This European Respiratory Society (ERS) task force was formed to provide an expert consensus for the management of incidental findings which can be adapted and followed during implementation. METHODS: A multi-European society collaborative group was convened. 23 topics were identified, primarily from an ERS statement on lung cancer screening, and a systematic review of the literature was conducted according to ERS standards. Initial review of abstracts was completed and full text was provided to members of the group for each topic. Sections were edited and the final document approved by all members and the ERS Science Council. RESULTS: Nine topics considered most important and frequent were reviewed as standalone topics (interstitial lung abnormalities, emphysema, bronchiectasis, consolidation, coronary calcification, aortic valve disease, mediastinal mass, mediastinal lymph nodes and thyroid abnormalities). Other topics considered of lower importance or infrequent were grouped into generic categories, suitable for general statements. CONCLUSIONS: This European collaborative group has produced an incidental findings statement that can be followed during lung cancer screening. It will ensure that an evidence-based approach is used for reporting and managing incidental findings, which will mean that harms are minimised and any programme is as cost-effective as possible.

Clinico-radiological correlation and prognostic value of baseline chest computed tomography in eosinophilic granulomatosis with polyangiitis.

OBJECTIVES: While chest high-resolution CT (HRCT) is correlated to severity and prognosis in asthma, it has not been studied in eosinophilic granulomatosis with polyangiitis (EGPA). Our objective is to study the prognostic value of baseline HRCT in EGPA patients. METHODS: Retrospective, multicentre observational study in three French hospitals, including EGPA patients with available chest HRCT before any systemic treatment. Two experienced radiologists blinded to clinical data evaluated HRCT images using semi-quantitative scoring. HRCT characteristics were correlated with clinical features and outcome. RESULTS: Among 46 patients, 38 (82.6%) had abnormal parenchymal findings on HRCT, including bronchial wall thickening (69.6%), mosaic perfusion (63.0%), ground-glass opacities (32.6%), bronchiectasis (30.4%), mucous plugging (21.7%) and consolidations (17.4%). Patients were clustered into three groups depending on HRCT features: ground-glass pattern, i.e. with ground-glass opacities with or without bronchial abnormalities (group 1, 28.3%), bronchial pattern (group 2, 41.3%) and extra-pulmonary pattern with no significant abnormality (group 3, 30.4%). Group 2 showed less frequent cardiac involvement (31.6 vs 46.2 and 42.9% in groups 1 and 3), more frequent positive ANCA (52.6 vs 0.0 and 14.3%) and higher eosinophil count (median 7510 vs 4000 and 4250/mm3). Group 1 showed worse prognosis with more frequent steroid-dependency (58.3 vs 11.1 and 28.6%) and requirement for mepolizumab (25.0 vs 11.1 and 7.1%). Conversely, group 2 showed a better outcome with higher rates of remission (88.9 vs 41.6 and 71.4%). CONCLUSION: Chest HRCT at diagnosis of EGPA may have prognostic value and help clinicians better manage these patients.

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.

Willingness to participate in combination screening for lung cancer, chronic obstructive pulmonary disease and cardiovascular disease in four European countries.

OBJECTIVES: Lung cancer screening (LCS), using low-dose computed tomography (LDCT), can be more efficient by simultaneously screening for chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD), the Big-3 diseases. This study aimed to determine the willingness to participate in (combinations of) Big-3 screening in four European countries and the relative importance of amendable participation barriers. METHODS: An online cross-sectional survey aimed at (former) smokers aged 50-75 years elicited the willingness of individuals to participate in Big-3 screening and used analytical hierarchy processing (AHP) to determine the importance of participation barriers. RESULTS: Respondents were from France (n = 391), Germany (n = 338), Italy (n = 399), and the Netherlands (n = 342), and consisted of 51.2% men. The willingness to participate in screening was marginally influenced by the diseases screened for (maximum difference of 3.1%, for Big-3 screening (73.4%) vs. lung cancer and COPD screening (70.3%)) and by country (maximum difference of 3.7%, between France (68.5%) and the Netherlands (72.3%)). The largest effect on willingness to participate was personal perceived risk of lung cancer. The most important barriers were the missed cases during screening (weight 0.19) and frequency of screening (weight 0.14), while diseases screened for (weight 0.11) ranked low. CONCLUSIONS: The difference in willingness to participate in LCS showed marginal increase with inclusion of more diseases and limited variation between countries. A marginal increase in participation might result in a marginal additional benefit of Big-3 screening. The amendable participation barriers are similar to previous studies, and the new criterion, diseases screened for, is relatively unimportant. CLINICAL RELEVANCE STATEMENT: Adding diseases to combination screening modestly improves participation, driven by personal perceived risk. These findings guide program design and campaigns for lung cancer and Big-3 screening. Benefits of Big-3 screening lie in long-term health and economic impact, not participation increase. KEY POINTS: • It is unknown whether or how combination screening might affect participation. • The addition of chronic obstructive pulmonary disease and cardiovascular disease to lung cancer screening resulted in a marginal increase in willingness to participate. • The primary determinant influencing individuals' engagement in such programs is their personal perceived risk of the disease.

Implementation of artificial intelligence in thoracic imaging-a what, how, and why guide from the European Society of Thoracic Imaging (ESTI).

This statement from the European Society of Thoracic imaging (ESTI) explains and summarises the essentials for understanding and implementing Artificial intelligence (AI) in clinical practice in thoracic radiology departments. This document discusses the current AI scientific evidence in thoracic imaging, its potential clinical utility, implementation and costs, training requirements and validation, its' effect on the training of new radiologists, post-implementation issues, and medico-legal and ethical issues. All these issues have to be addressed and overcome, for AI to become implemented clinically in thoracic radiology. KEY POINTS: • Assessing the datasets used for training and validation of the AI system is essential. • A departmental strategy and business plan which includes continuing quality assurance of AI system and a sustainable financial plan is important for successful implementation. • Awareness of the negative effect on training of new radiologists is vital.

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).

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.

Case 294: Catastrophic Antiphospholipid Syndrome.

History A 50-year-old woman presented to the emergency department of our hospital with a 2-day history of lower limb pain associated with unusual asthenia and diffuse arthralgia over the past 3 weeks. She was a native of Guinea and had lived in France for most of her life, working as a personal care assistant. Her only medical history of note was an occurrence of fetal death at 12 weeks gestation when she was 35 years old. She had bilateral lower limb swelling, without changes in skin temperature or color. All proximal and distal arterial pulses were felt. General physical examination findings were otherwise unremarkable. Her laboratory tests showed a decreased hemoglobin concentration of 8.9 g/dL (normal range, 12-16 g/dL), a decreased platelet count of 45 × 109/L (normal range, [150-400] × 109/L), a C-reactive protein level of 158 mg/L (normal range, <5 mg/L), and a d-dimer level of 2000 mg/L (normal range, <500 mg/L]). Compression US of the lower limbs revealed bilateral calf vein thrombosis involving the fibular and posterior tibial veins. Curative anticoagulation using low-molecular-weight heparin (enoxaparin, subcutaneous injection of 100 units per kilogram of body weight twice a day) was started. The day after the start of anticoagulation therapy, the patient reported dyspnea and acute chest and abdominal pain. Her vital signs were assessed, and she had elevated blood pressure and increased heart rate and respiratory rate, but she remained afebrile. Her cardiac auscultation was unremarkable, besides tachycardia. Skin examination revealed small areas of necrosis on the fingertips of her right hand. Laboratory studies were repeated and showed an increase in serum creatinine level from a baseline value of 0.49 mg/dL to a new value of 1.01 mg/dL (normal range, 0.6-1.1 mg/dL), an apparition of low-grade proteinuria of 0.43 g per day (normal range, <0.3 g/ day), and a high serum troponin level of 1066 ng/L (normal range, <14 ng/L), whereas electrocardiography showed no ST segment modification and echocardiography revealed a moderately altered left ventricular ejection fraction (45%). There was no coronary occlusion seen at emergency coronarography. Contrast-enhanced CT of the chest, abdomen, and pelvis was performed (Figs 1, 2) together with cardiac MRI (Figs 3, 4).

Case 294.

History A 50-year-old woman presented to the emergency department of our hospital with a 2-day history of lower limb pain associated with unusual asthenia and diffuse arthralgia over the past 3 weeks. She was a native of Guinea and had lived in France for most of her life, working as a personal care assistant. Her only medical history of note was an occurrence of fetal death at 12 weeks gestation when she was 35 years old. She had bilateral lower limb swelling, without changes in skin temperature or color. All proximal and distal arterial pulses were felt. General physical examination findings were otherwise unremarkable. Her laboratory tests showed a decreased hemoglobin concentration of 8.9 g/dL (normal range, 12-16 g/dL), a decreased platelet count of 45 × 109/L (normal range, 150-400 × 109/L), a C-reactive protein level of 158 mg/L (normal range, <5 mg/L) and a d-dimer level of 2000 mg/L (normal range, <500 mg/L). Compression US of the lower limbs revealed bilateral calf vein thrombosis involving the fibular and posterior tibial veins. Curative anticoagulation using low-molecular-weight heparin (enoxaparin, subcutaneous injection of 100 units per kilogram of body weight twice a day) was started. The day after the start of anticoagulation therapy, the patient reported dyspnea and acute chest and abdominal pain. Her vital signs were assessed, and she had elevated blood pressure and increased heart rate and respiratory rate, but she remained afebrile. Her cardiac auscultation was unremarkable, besides tachycardia. Skin examination revealed small areas of necrosis on the fingertips of her right hand. Laboratory studies were repeated and showed an increase in serum creatinine level from a baseline value of 0.49 mg/dL to a new value of 1.01 mg/dL (normal range, 0.6-1.1 mg/dL), an apparition of low-grade proteinuria of 0.43 g per day (normal range, <0.3 g/day), and a high serum troponin level of 1066 ng/L (normal range, <14 ng/L), whereas electrocardiography showed no ST segment modification and echocardiography revealed a moderately altered left ventricular ejection fraction (45%). There was no coronary occlusion seen at emergency coronarography. Contrast-enhanced CT of the chest, abdomen, and pelvis was performed (Figs 1, 2) together with cardiac MRI (Figs 3, 4).

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.

Pulmonary Embolism and Deep Vein Thrombosis in COVID-19: A Systematic Review and Meta-Analysis.

Background The association of pulmonary embolism (PE) with deep vein thrombosis (DVT) in patients with coronavirus disease 2019 (COVID-19) remains unclear, and the diagnostic accuracy of D-dimer tests for PE is unknown. Purpose To conduct meta-analysis of the study-level incidence of PE and DVT and to evaluate the diagnostic accuracy of D-dimer tests for PE from multicenter individual patient data. Materials and Methods A systematic literature search identified studies evaluating the incidence of PE or DVT in patients with COVID-19 from January 1, 2020, to June 15, 2020. These outcomes were pooled using a random-effects model and were further evaluated using metaregression analysis. The diagnostic accuracy of D-dimer tests for PE was estimated on the basis of individual patient data using the summary receiver operating characteristic curve. Results Twenty-seven studies with 3342 patients with COVID-19 were included in the analysis. The pooled incidence rates of PE and DVT were 16.5% (95% CI: 11.6, 22.9; I2 = 0.93) and 14.8% (95% CI: 8.5, 24.5; I2 = 0.94), respectively. PE was more frequently found in patients who were admitted to the intensive care unit (ICU) (24.7% [95% CI: 18.6, 32.1] vs 10.5% [95% CI: 5.1, 20.2] in those not admitted to the ICU) and in studies with universal screening using CT pulmonary angiography. DVT was present in 42.4% of patients with PE. D-dimer tests had an area under the receiver operating characteristic curve of 0.737 for PE, and D-dimer levels of 500 and 1000 µg/L showed high sensitivity (96% and 91%, respectively) but low specificity (10% and 24%, respectively). Conclusion Pulmonary embolism (PE) and deep vein thrombosis (DVT) occurred in 16.5% and 14.8% of patients with coronavirus disease 2019 (COVID-19), respectively, and more than half of patients with PE lacked DVT. The cutoffs of D-dimer levels used to exclude PE in preexisting guidelines seem applicable to patients with COVID-19. © RSNA, 2020 Supplemental material is available for this article. See also the editorial by Woodard 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.

ESR/ERS statement paper on lung cancer screening.

In Europe, lung cancer ranks third among the most common cancers, remaining the biggest killer. Since the publication of the first European Society of Radiology and European Respiratory Society joint white paper on lung cancer screening (LCS) in 2015, many new findings have been published and discussions have increased considerably. Thus, this updated expert opinion represents a narrative, non-systematic review of the evidence from LCS trials and description of the current practice of LCS as well as aspects that have not received adequate attention until now. Reaching out to the potential participants (persons at high risk), optimal communication and shared decision-making will be key starting points. Furthermore, standards for infrastructure, pathways and quality assurance are pivotal, including promoting tobacco cessation, benefits and harms, overdiagnosis, quality, minimum radiation exposure, definition of management of positive screen results and incidental findings linked to respective actions as well as cost-effectiveness. This requires a multidisciplinary team with experts from pulmonology and radiology as well as thoracic oncologists, thoracic surgeons, pathologists, family doctors, patient representatives and others. The ESR and ERS agree that Europe's health systems need to adapt to allow citizens to benefit from organised pathways, rather than unsupervised initiatives, to allow early diagnosis of lung cancer and reduce the mortality rate. Now is the time to set up and conduct demonstration programmes focusing, among other points, on methodology, standardisation, tobacco cessation, education on healthy lifestyle, cost-effectiveness and a central registry.

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.