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.

Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution

PURPOSE

The purpose of this study was to propose a deep learning-based approach to detect pulmonary embolism and quantify its severity using the Qanadli score and the right-to-left ventricle diameter (RV/LV) ratio on three-dimensional (3D) computed tomography pulmonary angiography (CTPA) examinations with limited annotations.

MATERIALS AND METHODS

Using a database of 3D CTPA examinations of 1268 patients with image-level annotations, and two other public datasets of CTPA examinations from 91 (CAD-PE) and 35 (FUME-PE) patients with pixel-level annotations, a pipeline consisting of: (i), detecting blood clots; (ii), performing PE-positive versus negative classification; (iii), estimating the Qanadli score; and (iv), predicting RV/LV diameter ratio was followed. The method was evaluated on a test set including 378 patients. The performance of PE classification and severity quantification was quantitatively assessed using an area under the curve (AUC) analysis for PE classification and a coefficient of determination (R²) for the Qanadli score and the RV/LV diameter ratio.

RESULTS

Quantitative evaluation led to an overall AUC of 0.870 (95% confidence interval [CI]: 0.850-0.900) for PE classification task on the training set and an AUC of 0.852 (95% CI: 0.810-0.890) on the test set. Regression analysis yielded R² value of 0.717 (95% CI: 0.668-0.760) and of 0.723 (95% CI: 0.668-0.766) for the Qanadli score and the RV/LV diameter ratio estimation, respectively on the test set.

CONCLUSION

This study shows the feasibility of utilizing AI-based assistance tools in detecting blood clots and estimating PE severity scores with 3D CTPA examinations. This is achieved by leveraging blood clots and cardiac segmentations. Further studies are needed to assess the effectiveness of these tools in clinical practice.

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.

Lung fibrosis is uncommon in primary Sjögren's disease: A retrospective analysis of computed tomography features in 77 patients

PURPOSE

The purpose of this study was to describe lung abnormalities observed on computed tomography (CT) in patients meeting the 2016 American College of Rheumatology/European League Against Rheumatism (EULAR) classification criteria for primary Sjögren's disease (pSD).

MATERIALS AND METHODS

All patients with pSD seen between January 2009 and December 2020 in the day care centre of our National Reference Center for rare systemic autoimmune diseases, who had at least one chest CT examination available for review and for whom the cumulative EULAR Sjögren's Syndrome Disease Activity Index (cumESSDAI) could be calculated were retrospectively evaluated. CT examinations were reviewed, together with clinical symptoms and pulmonary functional results.

RESULTS

Seventy-seven patients (73 women, four men) with a median age of 51 years at pSD diagnosis (age range: 17-79 years), a median follow-up time of 6 years and a median cumESSDAI of 7 were included. Sixty-six patients (86%) had anti-SSA antibodies. Thirty-three patients (33/77; 43%) had respiratory symptoms, without significant alteration in pulmonary function tests. Forty patients (40/77; 52%) had abnormal lung CT findings of whom almost half of them had no respiratory symptoms. Abnormalities on chest CT were more frequently observed in patients with anti-SSA positivity and a history of lymphoma. Air cysts (28/77; 36%) and mosaic perfusion (35/77; 35%) were the predominant abnormalities, whereas lung fibrosis was observed in five patients (5/77; 6%).

CONCLUSION

More than half of patients with pSD have abnormal CT findings, mainly air cysts and mosaic perfusion, indicative of small airways disease, whereas lung fibrosis is rare, observed in less than 10% of such patients.

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.

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.

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.

Ten reasons to screen women at risk of lung cancer

This opinion piece reviews major reasons for promoting lung cancer screening in at-risk women who are smokers or ex-smokers, from the age of 50. The epidemiology of lung cancer in European women is extremely worrying, with lung cancer mortality expected to surpass breast cancer mortality in most European countries. There are conflicting data as to whether women are at increased risk of developing lung cancer compared to men who have a similar tobacco exposure. The sharp increase in the incidence of lung cancer in women exceeds the increase in their smoking exposure which is in favor of greater susceptibility. Lung and breast cancer screening could be carried out simultaneously, as the screening ages largely coincide. In addition, lung cancer screening could be carried out every 2 years, as is the case for breast cancer screening, if the baseline CT scan is negative.As well as detecting early curable lung cancer, screening can also detect coronary heart disease and osteoporosis induced by smoking. This enables preventive measures to be taken in addition to smoking cessation assistance, to reduce morbidity and mortality in the female population. Key points • The epidemiology of lung cancer in European women is very worrying.• Lung cancer is becoming the leading cause of cancer mortality in European women.• Women benefit greatly from screening in terms of reduced risk of death from lung cancer.

Detection and quantification of pulmonary embolism with artificial intelligence: The SFR 2022 artificial intelligence data challenge

PURPOSE

In 2022, the French Society of Radiology together with the French Society of Thoracic Imaging and CentraleSupelec organized their 13th data challenge. The aim was to aid in the diagnosis of pulmonary embolism, by identifying the presence of pulmonary embolism and by estimating the ratio between right and left ventricular (RV/LV) diameters, and an arterial obstruction index (Qanadli's score) using artificial intelligence.

MATERIALS AND METHODS

The data challenge was composed of three tasks: the detection of pulmonary embolism, the RV/LV diameter ratio, and Qanadli's score. Sixteen centers all over France participated in the inclusion of the cases. A health data hosting certified web platform was established to facilitate the inclusion process of the anonymized CT examinations in compliance with general data protection regulation. CT pulmonary angiography images were collected. Each center provided the CT examinations with their annotations. A randomization process was established to pool the scans from different centers. Each team was required to have at least a radiologist, a data scientist, and an engineer. Data were provided in three batches to the teams, two for training and one for evaluation. The evaluation of the results was determined to rank the participants on the three tasks.

RESULTS

A total of 1268 CT examinations were collected from the 16 centers following the inclusion criteria. The dataset was split into three batches of 310, 580 and 378 C T examinations provided to the participants respectively on September 5, 2022, October 7, 2022 and October 9, 2022. Seventy percent of the data from each center were used for training, and 30% for the evaluation. Seven teams with a total of 48 participants including data scientists, researchers, radiologists and engineering students were registered for participation. The metrics chosen for evaluation included areas under receiver operating characteristic curves, specificity and sensitivity for the classification task, and the coefficient of determination r2 for the regression tasks. The winning team achieved an overall score of 0.784.

CONCLUSION

This multicenter study suggests that the use of artificial intelligence for the diagnosis of pulmonary embolism is possible on real data. Moreover, providing quantitative measures is mandatory for the interpretability of the results, and is of great aid to the radiologists especially in emergency settings.

Comparative performances of machine learning algorithms in radiomics and impacting factors

There are no current recommendations on which machine learning (ML) algorithms should be used in radiomics. The objective was to compare performances of ML algorithms in radiomics when applied to different clinical questions to determine whether some strategies could give the best and most stable performances regardless of datasets. This study compares the performances of nine feature selection algorithms combined with fourteen binary classification algorithms on ten datasets. These datasets included radiomics features and clinical diagnosis for binary clinical classifications including COVID-19 pneumonia or sarcopenia on CT, head and neck, orbital or uterine lesions on MRI. For each dataset, a train-test split was created. Each of the 126 (9 × 14) combinations of feature selection algorithms and classification algorithms was trained and tuned using a ten-fold cross validation, then AUC was computed. This procedure was repeated three times per dataset. Best overall performances were obtained with JMI and JMIM as feature selection algorithms and random forest and linear regression models as classification algorithms. The choice of the classification algorithm was the factor explaining most of the performance variation (10% of total variance). The choice of the feature selection algorithm explained only 2% of variation, while the train-test split explained 9%.

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 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, PM_2.5), 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.

Artificial intelligence: A critical review of applications for lung nodule and lung cancer

Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent years, the growth of deep learning, a successful technique for computer vision tasks that does not require explicit programming, coupled with the availability of large imaging databases fostered the development of multiple applications in the medical imaging field, especially for lung nodules and lung cancer, mostly through convolutional neural networks (CNN). Some of the first applications of AI is this field were dedicated to automated detection of lung nodules on X-ray and computed tomography (CT) examinations, with performances now reaching or exceeding those of radiologists. For lung nodule segmentation, CNN-based algorithms applied to CT images show excellent spatial overlap index with manual segmentation, even for irregular and ground glass nodules. A third application of AI is the classification of lung nodules between malignant and benign, which could limit the number of follow-up CT examinations for less suspicious lesions. Several algorithms have demonstrated excellent capabilities for the prediction of the malignancy risk when a nodule is discovered. These different applications of AI for lung nodules are particularly appealing in the context of lung cancer screening. In the field of lung cancer, AI tools applied to lung imaging have been investigated for distinct aims. First, they could play a role for the non-invasive characterization of tumors, especially for histological subtype and somatic mutation predictions, with a potential therapeutic impact. Additionally, they could help predict the patient prognosis, in combination to clinical data. Despite these encouraging perspectives, clinical implementation of AI tools is only beginning because of the lack of generalizability of published studies, of an inner obscure working and because of limited data about the impact of such tools on the radiologists' decision and on the patient outcome. Radiologists must be active participants in the process of evaluating AI tools, as such tools could support their daily work and offer them more time for high added value tasks.

Lung CAncer SCreening in French women using low-dose CT and Artificial intelligence for DEtection: the CASCADE study protocol

INTRODUCTION

Lung cancer screening (LCS) using low-dose CT has been demonstrated to reduce lung cancer-related mortality in large randomised controlled trials. Moving from trials to practice requires answering practical questions about the level of expertise of CT readers, the need for double reading as in trials and the potential role of artificial intelligence (AI). In addition, most LCS studies have predominantly included male participants with women being under-represented, even though the benefit of screening is greater for them. Thus, this study aims to compare the performance of a single CT reading by general radiologists trained in LCS using AI as a second reader to that of a double reading by expert thoracic radiologists, in a campaign for low-dose CT screening in high-risk women.

METHODS AND ANALYSIS

This observational cohort study will recruit 2400 asymptomatic women aged between 50 and 74 years, current or former smokers with at least a 20 pack-year smoking history, in 4 different French district areas. Assistance with smoking cessation will be offered to current smokers. An initial low-dose CT scan will be performed, with subsequent follow-ups at 1 year and 2 years. The primary objective is to compare CT scan readings by a single LCS-trained, AI-assisted radiologist to that of an expert double reading. The secondary objectives are: to evaluate the performance of AI as a stand-alone reader; the adherence to screening of female participants; the influence on smoking cessation; the psychological consequences of screening; the detection of chronic obstructive pulmonary disease (COPD), coronary artery disease and osteoporosis on low-dose CT scans and the costs incurred by screening.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the Comité de Protection des Personnes Sud-Est 1 (ethics approval number: 2021-A02265-36 with an amendment on 15 July 2022). Trial results will be disseminated at conferences, through relevant patient groups and published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT05195385.

Differentiating tracheobronchial involvement in granulomatosis with polyangiitis and relapsing polychondritis on chest CT: a cohort study

Background

In patients with tracheobronchial involvement, the differential diagnosis between granulomatosis with polyangiitis (GPA) and relapsing polychondritis (RP) can be challenging. The aim of this study was to describe the characteristics of airway abnormalities on chest computed tomography (CT) in patients with GPA or RP and to determine whether specific imaging criteria could be used to differentiate them.

Methods

GPA and RP patients with tracheobronchial involvement referred to a national referral center from 2008 to 2020 were evaluated. Their chest CT images were reviewed by two radiologists who were blinded to the final diagnosis in order to analyze the characteristics of airway involvement. The association between imaging features and a diagnosis of GPA rather than RP was analyzed using a generalized linear regression model.

Results

Chest CTs from 26 GPA and 19 RP patients were analyzed. Involvement of the subglottic trachea (odds ratio for GPA=28.56 [95% CI: 3.17; 847.63]; P=0.001) and extensive airway involvement (odds ratio for GPA=0.02 [95% CI: 0.00; 0.43]; P=0.008) were the two independent CT features that differentiated GPA from RP in multivariate analysis. Tracheal thickening sparing the posterior membrane was significantly associated to RP (odds ratio for GPA=0.09 [95% CI: 0.02; 0.39]; P=0.003) but only in the univariate analysis and suffered from only moderate interobserver agreement (kappa=0.55). Tracheal calcifications were also associated with RP only in the univariate analysis (odds ratio for GPA=0.21 [95% CI: 0.05; 0.78]; P=0.045).

Conclusion

The presence of subglottic involvement and diffuse airway involvement are the two most relevant criteria in differentiating between GPA and RP on chest CT. Although generally considered to be a highly suggestive sign of RP, posterior tracheal membrane sparing is a nonspecific and an overly subjective sign.

• The presence of subglottic involvement is in favor of GPA.

• Extensive airway involvement is in favor of RP.

• Posterior tracheal membrane sparing is a nonspecific and an overly subjective sign.

How Artificial Intelligence in Imaging Can Better Serve Patients with Bronchial and Parenchymal Lung Diseases?

With the rapid development of computing today, artificial intelligence has become an essential part of everyday life, with medicine and lung health being no exception. Big data-based scientific research does not mean simply gathering a large amount of data and letting the machines do the work by themselves. Instead, scientists need to identify problems whose solution will have a positive impact on patients’ care. In this review, we will discuss the role of artificial intelligence from both physiological and anatomical standpoints, starting with automatic quantitative assessment of anatomical structures using lung imaging and considering disease detection and prognosis estimation based on machine learning. The evaluation of current strengths and limitations will allow us to have a broader view for future developments.

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.

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.

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.

Computed tomography after severe COVID-19 pneumonia: findings at 6 months and beyond

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects the alveolar epithelial cells causing coronavirus disease (COVID-19) pneumonia of varying severity [1, 2]. 15–30% of patients develop acute respiratory distress syndrome (ARDS) requiring hospitalisation in intensive care units (ICU) and mechanical ventilation [2, 3]. At 3 months, there are persisting computed tomography (CT) abnormalities in 17 to 91% of discharged COVID-19 patients [4–8], mainly consistent with an organising pneumonia (OP) pattern. These anomalies are more frequently reported in patients who were admitted to ICU [9]. Pulmonary fibrosis has been reported at autopsy of patients deceased from COVID-19 pneumonia, along with pulmonary microvascular thrombosis [10].

Parenchymal bands and ground-glass opacities consistent with a pattern of late organising pneumonia are frequently observed 6 months after ICU admission for #COVID19, whereas fibrotic changes of limited extent are only observed in about 1/3 of patientshttps://bit.ly/2UGOsbr

Deep learning for lung disease segmentation on CT: Which reconstruction kernel should be used?

PURPOSE

The purpose of this study was to determine whether a single reconstruction kernel or both high and low frequency kernels should be used for training deep learning models for the segmentation of diffuse lung disease on chest computed tomography (CT).

MATERIALS AND METHODS

Two annotated datasets of COVID-19 pneumonia (323,960 slices) and interstitial lung disease (ILD) (4,284 slices) were used. Annotated CT images were used to train a U-Net architecture to segment disease. All CT slices were reconstructed using both a lung kernel (LK) and a mediastinal kernel (MK). Three different trainings, resulting in three different models were compared for each disease: training on LK only, MK only or LK+MK images. Dice similarity scores (DSC) were compared using the Wilcoxon signed-rank test.

RESULTS

Models only trained on LK images performed better on LK images than on MK images (median DSC = 0.62 [interquartile range (IQR): 0.54, 0.69] vs. 0.60 [IQR: 0.50, 0.70], P < 0.001 for COVID-19 and median DSC = 0.62 [IQR: 0.56, 0.69] vs. 0.50 [IQR 0.43, 0.57], P < 0.001 for ILD). Similarly, models only trained on MK images performed better on MK images (median DSC = 0.62 [IQR: 0.53, 0.68] vs. 0.54 [IQR: 0.47, 0.63], P < 0.001 for COVID-19 and 0.69 [IQR: 0.61, 0.73] vs. 0.63 [IQR: 0.53, 0.70], P < 0.001 for ILD). Models trained on both kernels performed better or similarly than those trained on only one kernel. For COVID-19, median DSC was 0.67 (IQR: =0.59, 0.73) when applied on LK images and 0.67 (IQR: 0.60, 0.74) when applied on MK images (P < 0.001 for both). For ILD, median DSC was 0.69 (IQR: 0.63, 0.73) when applied on LK images (P = 0.006) and 0.68 (IQR: 0.62, 0.72) when applied on MK images (P > 0.99).

CONCLUSION

Reconstruction kernels impact the performance of deep learning-based models for lung disease segmentation. Training on both LK and MK images improves the performance.

Long-Term Imaging Follow-Up in DIPNECH: Multicenter Experience

Diffuse pulmonary neuroendocrine cell hyperplasia (DIPNECH) is a rare pre-invasive disease whose pathophysiology remains unclear. We aimed to assess long-term evolution in imaging of DIPNECH, in order to propose follow-up recommendations. Patients with histologically confirmed DIPNECH from four centers, evaluated between 2001 and 2020, were enrolled if they had at least two available chest computed tomography (CT) exams performed at least 24 months apart. CT exams were analyzed for the presence and the evolution of DIPNECH-related CT findings. Twenty-seven patients, mostly of female gender (n = 25/27; 93%) were included. Longitudinal follow-up over a median 63-month duration (IQR: 31–80 months) demonstrated an increase in the size of lung nodules in 19 patients (19/27, 70%) and the occurrence of metastatic spread in three patients (3/27, 11%). The metastatic spread was limited to mediastinal lymph nodes in one patient, whereas the other two patients had both lymph node and distant metastases. The mean time interval between baseline CT scan and metastatic spread was 70 months (14, 74 and 123 months). Therefore, long-term annual imaging follow-up of DIPNECH might be appropriate to encompass the heterogeneous longitudinal behavior of this disease.

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

Background There are conflicting data regarding the diagnostic performance of Chest computed tomography (CT) for COVID-19 pneumonia. Disease extent on CT has been reported to influence prognosis. Purpose To create a large publicly available dataset and assess the diagnostic and prognostic value of CT in COVID-19 pneumonia. Materials and Methods This multicenter observational retrospective cohort study (ClinicalTrials.gov: NCT04355507) involved 20 French university hospitals. Eligible subjects presented at the emergency departments of the hospitals involved between March 1st and April 30th, 2020 and underwent both thoracic CT and RT-PCR 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 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 subjects positive for both RT-PCR and CT, using clinical and radiological features. Results Of 10,930 subjects screened for eligibility, 10,735 (median age 65 years, interquartile range, 51-77 years; 6,147 men) were included and 6,448 (60.0%) had a positive RT-PCR result. With RT-PCR as reference, the sensitivity and specificity and 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's AC1 coefficient: 0.79) Of all the variables analysed, the extent of pneumonia on CT (OR 3.25, 95%CI: 2.71, 3.89) was the best predictor of severe outcome at one month. A score based solely on clinical variables predicted a severe outcome with an AUC 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 on CT. Conclusion Using pre-defined criteria, CT reading is not influenced by reader's experience and helps predict the outcome at one month. Published under a CC BY 4.0 license. See also the editorial by Rubin.

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

OBJECTIVES

Eosinophilic granulomatosis with polyangiitis (EGPA) is a necrotizing eosinophil-rich vasculitis. Specific cardiomyopathy (CM) was described in old studies as the most important predictor of mortality. We aimed to revisit EGPA-related CM and 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 analyzed. 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. Median age was 47 years (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 edema. Abnormal EKG, TTE and cardiac magnetic resonance imaging (CMRI) were found in 72%, 72% and 99% in CM+ patients, respectively, contrasting with abnormalities in 32%, 38% and 60% in CM-negative patients. 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 (MACE) occurred in 13%, both in CM+ and CM- patients. Abnormal EKG and LGE on CMRI were associated with the occurrence of MACE. Four patients died, but none from cardiac causes.

CONCLUSIONS

Specific cardiomyopathy is frequent in EGPA, especially in ANCA-negative patients with high eosinophil counts. Long-term outcome was better than previously reported. Abnormal EKG and LGE on CMRI were associated with the occurrence of MACE.

Ongoing challenges in implementation of lung cancer screening

Lung cancer is the leading cause of cancer deaths in Europe and around the world. Although available therapies have undergone considerable development in the past decades, the five-year survival rate for lung cancer remains low. This sobering outlook results mainly from the advanced stages of cancer most patients are diagnosed with. As the population at risk is relatively well defined and early stage disease is potentially curable, lung cancer outcomes may be improved by screening. Several studies already show that lung cancer screening (LCS) with low-dose computed tomography (LDCT) reduces lung cancer mortality. However, for a successful implementation of LCS programmes, several challenges have to be overcome: selection of high-risk individuals, standardization of nodule classification and measurement, specific training of radiologists, optimization of screening intervals and screening duration, handling of ancillary findings are some of the major points which should be addressed. Last but not least, the psychological impact of screening on screened individuals and the impact of potential false positive findings should not be neglected. The aim of this review is to discuss the different challenges of implementing LCS programmes and to give some hints on how to overcome them. Finally, we will also discuss the psychological impact of screening on quality of life and the importance of smoking cessation.

Lung carcinoid tumors with Diffuse Idiopathic Pulmonary NeuroEndocrine Cell Hyperplasia (DIPNECH) exhibit pejorative pathological features

INTRODUCTION

Diffuse Idiopathic Pulmonary NeuroEndocrine Cell Hyperplasia (DIPNECH) is a rare disease often associated with carcinoid tumors. We aimed at evaluating the impact of DIPNECH on characteristics and prognosis of patients who underwent radical treatment of pulmonary carcinoid tumors.

MATERIAL AND METHODS

We reviewed all patients operated on for curative-intent resection of carcinoid tumor in our department from 2001 to 2020. Cases exhibiting both pathological and radiological features of DIPNECH, as assessed by respective thoracic expert physicians, were analyzed separately.

RESULTS

172 cases of resected carcinoid tumors were identified, including 25 (14.5 %) harboring pathological criteria of DIPNECH and radiologic features like mosaic attenuation (92.0 %), multiple nodules < 5 mm (76.0 %), and mucoid impactions (32 %). In DIPNECH patients, major pulmonary resections were usually performed (92.0 %) and resected tumors were mostly classified as pT1 (92 %). Mean Ki67 staining was 3.7 ± 5.2 %. The early postoperative period was mostly uneventful (96.0 %) and 5-year survival was 92.9 ± 6.9 %. Compared to non-DIPNECH cases, we found that patients were older (mean 65.6 ± 9.3 versus 54.1 ± 17.9, p = 0.002), more frequently female (84.0 % versus 56.5 %, p = 0.009), and exhibiting diabetes mellitus (45.8 % versus 18.5 %, p < 0.001) or hypertension (45.8 % versus 24.1 %, p = 0.039). The rate of atypical carcinoid tumors was significantly higher in DIPNECH patients (40.0 % versus 19.9 %, p = 0.027), as well as rate of mediastinal lymph-nodes involvement (pN2+) (36.0 % versus 4.1 %, p < 0.001). At multivariate analysis, only DIPNECH pattern and atypical histology were independent factors of pN2 invasion which was the only predictor of poorer prognosis on Log-Rank test.

CONCLUSION

Carcinoid tumors with proven DIPNECH are associated with negative pathological features and may deserve a dedicated perioperative management.

Total Psoas Area and Total Muscular Parietal Area Affect Long-Term Survival of Patients Undergoing Pneumonectomy for Non-Small Cell Lung Cancer

There is no standardization in methods to assess sarcopenia; in particular the prognostic significance of muscular fatty infiltration in lung cancer patients undergoing surgery has not been evaluated so far. We thus performed several computed tomography (CT)-based morphometric measurements of sarcopenia in 238 consecutive non-small cell lung-cancer patients undergoing pneumonectomy from 1 January 2007 to 31 December 2015. Sarcopenia was assessed by the following CT-based parameters: cross-sectional total psoas area (TPA), cross-sectional total muscle area (TMA), and total parietal muscle area (TPMA), defined as TMA without TPA. Measures were performed at the level of the third lumbar vertebra and were obtained for the entire muscle surface, as well as by excluding fatty infiltration based on CT attenuation. Findings were stratified for gender, and a threshold of the 33rd percentile was set to define sarcopenia. Furthermore, we assessed the possibility of being sarcopenic at both the TPA and TPMA level, or not, by taking into account of not fatty infiltration. Five-year survival was 39.1% for the whole population. Lower TPA, TMA, and TPA were associated with lower survival at univariate analysis; taking into account muscular fatty infiltration did not result in more powerful discrimination. Being sarcopenic at both psoas and parietal muscle level had the optimum discriminating power. At the multivariable analysis, being sarcopenic at both psoas and parietal muscles (considering the whole muscle areas, including muscular fat), male sex, increasing age, and tumor stage, as well as Charlson Comorbidity Index (CCI), were independently associated with worse long-term outcomes. We conclude that sarcopenia is a powerful negative prognostic factor in patients with lung cancer treated by pneumonectomy.

Chronic Lung Allograft Dysfunction Post Lung Transplantation: A Review of Computed Tomography Quantitative Methods for Detection and Follow-Up

Chronic lung allograft dysfunction (CLAD) remains the leading cause of morbidity and mortality after lung transplantation. The term encompasses both obstructive and restrictive phenotypes, as well as mixed and undefined phenotypes. Imaging, in addition to pulmonary function tests, plays a major role in identifying the CLAD phenotype and is essential for follow-up after lung transplantation. Quantitative imaging allows for the performing of reader-independent precise evaluation of CT examinations. In this review article, we will discuss the role of quantitative imaging methods for evaluating the airways and the lung parenchyma on computed tomography (CT) images, for an early identification of CLAD and for prognostic estimation. We will also discuss their limits and the need for novel approaches to predict, understand, and identify CLAD in its early stages.

Artificial intelligence: a critical review of current applications in pancreatic imaging

The applications of artificial intelligence (AI), including machine learning and deep learning, in the field of pancreatic disease imaging are rapidly expanding. AI can be used for the detection of pancreatic ductal adenocarcinoma and other pancreatic tumors but also for pancreatic lesion characterization. In this review, the basic of radiomics, recent developments and current results of AI in the field of pancreatic tumors are presented. Limitations and future perspectives of AI are discussed.

A Dose Simulation X-Ray Software: An Innovating Tool to Reduce Chest Radiograph Exposure in Children

PURPOSE

Chest radiography is one of the most frequent x-ray examinations performed on children. Reducing the delivered dose is always a major task. The objective of our study was to determine the minimum dose to be delivered while maintaining the image quality of chest radiographs, using dose reduction simulation software.

MATERIALS AND METHODS

We included 60 children who had had a chest radiography in 5 groups established according to the diagnostic reference levels equitably represented by weight ranges. The software simulated for each radiograph 6 additional simulated photonic noise images corresponding to 100%, 80%, 64%, 50%, 40%, and 32% of the initial dose. The 360 radiographs were blindly scored by 2 radiologists, according to the 7 European quality criteria and a subjective criterion of interpretability, using a semiquantitative visual Lickert scale.

RESULTS

There was no significant difference in scoring between the reference radiograph (100%) and simulated radiographs at 80% of the dose in children between 5 and 20 kg, 50% of the dose in children between 20 and 30 kg, and between simulated radiographs at 40% of the dose in children over 30 kg. Interobserver reproducibility was moderate to excellent.

CONCLUSION

Chest radiography dose might be reduced by 20% in children between 5 and 20 kg, 50% in children between 20 and 30 kg, and 60% in children over 30 kg, without any difference in the image quality appreciation. Software that produced simulated x-ray with decreasing delivered dose is an innovating tool for an optimization process.

CT features of lung metastases from pancreatic adenocarcinoma: Correlation with histopathologic findings

PURPOSE

The purpose of this study was to evaluate the prevalence of an atypical, alveolar presentation of pulmonary metastases from pancreatic adenocarcinoma (PDAC) on computed tomography (CT) and to correlate CT features with those obtained at histopathologic analysis.

MATERIAL AND METHODS

A total of 76 patients with lung metastases from PDAC over a 10-year period (2009-2019) in a French university hospital were retrospectively included. There were 34 men and 42 women with a mean age of 67.6±11.3 (SD) years (range: 38-89 years). CT features of PDAC were classified according to their presentations as usual metastatic pattern or atypical alveolar pattern; the atypical alveolar pattern corresponding to either ground glass nodules or opacities, solid nodules with a halo sign, "air-space" nodules with air bronchogram, or parenchymal consolidation. Imaging-histopathologic correlation was performed when tissue samples were available.

RESULTS

Pulmonary metastases were synchronous in 36 patients (36/76; 47%) and metachronous in 40 patients (40/76; 53%). A predominant alveolar presentation on CT was observed in 17 patients (17/76, 22%). Nodules with halo sign were the predominant alveolar pattern in 7 patients (7/17; 41%), air-space nodules were predominant in 4 patients (4/17; 24%) whereas pure ground glass nodules and consolidations were observed as predominant features in 3 patients (3/17; 18%) each. For 5 patients who had histopathological confirmation, alveolar metastases of PDAC were characterized by columnar tumor cells lining the alveolar wall, which was not seen in other radiological presentations, whereas there were no differences regarding mucin secretion between pulmonary metastases with alveolar presentation and those with typical pattern.

CONCLUSIONS

Lung metastases from PDAC may present with a so-called "alveolar" pattern on CT. This misleading CT features is found in 22% of patients with lung metastases from PDAC and is due to lepidic growth of the metastatic cells.

Quantification of Cystic Fibrosis Lung Disease with Radiomics-based CT Scores

Purpose

To develop radiomics-based CT scores for assessing lung disease severity and exacerbation risk in adult patients with cystic fibrosis (CF).

Materials and Methods

This two-center retrospective observational study was approved by an institutional ethics committee, and the need for patient consent was waived. A total of 215 outpatients with CF referred for unenhanced follow-up chest CT were evaluated in two different centers between January 2013 and December 2016. After lung segmentation, chest CT scans from center 1 (training cohort, 162 patients [median age, 29 years; interquartile range {IQR}, 24-36 years; 84 men]) were used to build CT scores from 38 extracted CT features, using five different machine learning techniques trained to predict a clinical prognostic score, the Nkam score. The correlations between the developed CT scores, two different clinical prognostic scores (Liou and CF-ABLE), forced expiratory volume in 1 second (FEV₁), and risk of respiratory exacerbations were evaluated in the test cohort (center 2, 53 patients [median age, 27 years; IQR, 22-35 years; 34 men]) using the Spearman rank coefficient.

Results

In the test cohort, all radiomics-based CT scores showed moderate to strong correlation with the Nkam score (R = 0.57 to 0.63, P < .001) and Liou scores (R = -0.55 to -0.65, P < .001), whereas the correlation with CF-ABLE score was weaker (R = 0.28 to 0.38, P = .005 to .048). The developed CT scores showed strong correlation with predicted FEV₁ (R = -0.62 to -0.66, P < .001) and weak to moderate correlation with the number of pulmonary exacerbations to occur in the 12 months after the CT examination (R = 0.38 to 0.55, P < .001 to P = .006).

Conclusion

Radiomics can be used to build automated CT scores that correlate to clinical severity and exacerbation risk in adult patients with CF.Supplemental material is available for this article.See also the commentary by Elicker and Sohn in this issue.© RSNA, 2020.

Pre-test probability for SARS-Cov-2-related infection score: The PARIS score

INTRODUCTION

In numerous countries, large population testing is impossible due to the limited availability of RT-PCR kits and CT-scans. This study aimed to determine a pre-test probability score for SARS-CoV-2 infection.

METHODS

This multicenter retrospective study (4 University Hospitals) included patients with clinical suspicion of SARS-CoV-2 infection. Demographic characteristics, clinical symptoms, and results of blood tests (complete white blood cell count, serum electrolytes and CRP) were collected. A pre-test probability score was derived from univariate analyses of clinical and biological variables between patients and controls, followed by multivariate binary logistic analysis to determine the independent variables associated with SARS-CoV-2 infection.

RESULTS

605 patients were included between March 10th and April 30th, 2020 (200 patients for the training cohort, 405 consecutive patients for the validation cohort). In the multivariate analysis, lymphocyte (<1.3 G/L), eosinophil (<0.06 G/L), basophil (<0.04 G/L) and neutrophil counts (<5 G/L) were associated with high probability of SARS-CoV-2 infection but no clinical variable was statistically significant. The score had a good performance in the validation cohort (AUC = 0.918 (CI: [0.891-0.946]; STD = 0.014) with a Positive Predictive Value of high-probability score of 93% (95%CI: [0.89-0.96]). Furthermore, a low-probability score excluded SARS-CoV-2 infection with a Negative Predictive Value of 98% (95%CI: [0.93-0.99]). The performance of the score was stable even during the last period of the study (15-30th April) with more controls than infected patients.

CONCLUSIONS

The PARIS score has a good performance to categorize the pre-test probability of SARS-CoV-2 infection based on complete white blood cell count. It could help clinicians adapt testing and for rapid triage of patients before test results.