Osteoporosis and Fragility Fractures in Patients With Cirrhosis Evaluated for Liver Transplantation: Identification of High-Risk Patients Based on Computed Tomography at Evaluation.

INTRODUCTION: Osteoporosis in candidates for liver transplantation (LT) is often underdiagnosed despite the important consequences of morbidity. METHODS: We included 376 patients with cirrhosis evaluated for LT with available computed tomography (CT) scans. Prevalent vertebral fractures (VFs) were identified on CT reconstructions, and bone density was assessed by measuring CT attenuation of the L1 vertebra (L1-CT). RESULTS: We identified 139 VFs in 55 patients (14.6%). Logistic regression models showed that low L1-CT was the only independent determinant of VF. DISCUSSION: In patients with cirrhosis evaluated for LT, CT scans identified persons with severe osteoporosis without additional costs.

Overview of Periosteal Reaction by Imaging.

The periosteum is a membrane that covers almost all bones in the body. It is a living structure but attracts little attention unless it reacts excessively. We highlight the important points in the anatomy, histology, and physiology of the periosteum, the stimuli and various aspects of periosteal reaction, and the main conditions underlying periosteal reaction.

Bone in Anorexia.

Anorexia nervosa is a feeding disorder involving intentional weight loss. Restricted dietary intake leads to disturbed bone metabolism due to various factors, notably endocrine, that affect bone microarchitecture and incur risk of fracture. Mild to moderate anorexia shows a paradoxical increase in bone marrow adipose tissue, whereas severe forms show gelatinous transformation known as serous atrophy of bone marrow (SABM). Imaging assessment of the mineralized and adipose components uses several techniques: dual-energy X-ray absorptiometry, computed tomography, chemical shift magnetic resonance imaging (MRI), and single-voxel MR spectroscopy. SABM induces MRI bone signal disturbances that can be hard to interpret and may hinder visualization of the fracture line.

Artificial Intelligence for Detecting Acute Fractures in Patients Admitted to an Emergency Department: Real-Life Performance of Three Commercial Algorithms.

RATIONALE AND OBJECTIVES: Interpreting radiographs in emergency settings is stressful and a burden for radiologists. The main objective was to assess the performance of three commercially available artificial intelligence (AI) algorithms for detecting acute peripheral fractures on radiographs in daily emergency practice. MATERIALS AND METHODS: Radiographs were collected from consecutive patients admitted for skeletal trauma at our emergency department over a period of 2 months. Three AI algorithms-SmartUrgence, Rayvolve, and BoneView-were used to analyze 13 body regions. Four musculoskeletal radiologists determined the ground truth from radiographs. The diagnostic performance of the three AI algorithms was calculated at the level of the radiography set. Accuracies, sensitivities, and specificities for each algorithm and two-by-two comparisons between algorithms were obtained. Analyses were performed for the whole population and for subgroups of interest (sex, age, body region). RESULTS: A total of 1210 patients were included (mean age 41.3 ± 18.5 years; 742 [61.3%] men), corresponding to 1500 radiography sets. The fracture prevalence among the radiography sets was 23.7% (356/1500). Accuracy was 90.1%, 71.0%, and 88.8% for SmartUrgence, Rayvolve, and BoneView, respectively; sensitivity 90.2%, 92.6%, and 91.3%, with specificity 92.5%, 70.4%, and 90.5%. Accuracy and specificity were significantly higher for SmartUrgence and BoneView than Rayvolve for the whole population (P < .0001) and for subgroups. The three algorithms did not differ in sensitivity (P = .27). For SmartUrgence, subgroups did not significantly differ in accuracy, specificity, or sensitivity. For Rayvolve, accuracy and specificity were significantly higher with age 27-36 than ≥53 years (P = .0029 and P = .0019). Specificity was higher for the subgroup knee than foot (P = .0149). For BoneView, accuracy was significantly higher for the subgroups knee than foot (P = .0006) and knee than wrist/hand (P = .0228). Specificity was significantly higher for the subgroups knee than foot (P = .0003) and ankle than foot (P = .0195). CONCLUSION: The performance of AI detection of acute peripheral fractures in daily radiological practice in an emergency department was good to high and was related to the AI algorithm, patient age, and body region examined.

Safety and incremental prognostic value of stress cardiovascular magnetic resonance in patients with known chronic kidney disease.

BACKGROUND: Cardiovascular disease (CVD) is the main cause of mortality in patients with chronic kidney disease (CKD). Although several studies have demonstrated the consistently high prognostic value of stress cardiovascular magnetic resonance (CMR), its prognostic value in patients with CKD is not well established. We aimed to assess the safety and the incremental prognostic value of vasodilator stress perfusion CMR in consecutive symptomatic patients with known CKD. METHODS: Between 2008 and 2021, we conducted a retrospective dual center study with all consecutive symptomatic patients with known stage 3 CKD, defined by estimated glomerular filtration rate (eGFR) between 30 and 60 ml/min/1.73 m2, referred for vasodilator stress CMR. All patients with eGFR < 30 ml/min/1.73 m2 (n = 62) were excluded due the risk of nephrogenic systemic fibrosis. All patients were followed for the occurrence of major adverse cardiovascular events (MACE) defined as cardiac death or recurrent nonfatal myocardial infarction (MI). Cox regression analysis was used to determine the prognostic value of stress CMR parameters. RESULTS: Of 825 patients with known CKD (71.4 ± 8.8 years, 70% men), 769 (93%) completed the CMR protocol. Follow-up was available in 702 (91%) (median follow-up 6.4 (4.0-8.2) years). Stress CMR was well tolerated without occurrence of death or severe adverse event related to the injection of gadolinium or cases of nephrogenic systemic fibrosis. The presence of inducible ischemia was associated with the occurrence of MACE (hazard ratio [HR] 12.50; 95% confidence interval [CI] 7.50-20.8; p < 0.001). In multivariable analysis, ischemia and late gadolinium enhancement were independent predictors of MACE (HR 15.5; 95% CI 7.72 to 30.9; and HR 4.67 [95% CI 2.83-7.68]; respectively, both p < 0.001). After adjustment, stress CMR findings showed the best improvement in model discrimination and reclassification above traditional risk factors (C-statistic improvement: 0.13; NRI = 0.477; IDI = 0.049). CONCLUSIONS: In patients with known stage 3 CKD, stress CMR is safe and its findings have an incremental prognostic value to predict MACE over traditional risk factors.

Left atrioventricular coupling index assessed using cardiac CT as a prognostic marker of cardiovascular death.

PURPOSE: The purpose of this study was to investigate the prognostic value of left atrioventricular coupling index (LACI) assessed by cardiac computed tomography (CT), to predict cardiovascular death in consecutive patients referred for cardiac CT with coronary analysis. MATERIALS AND METHODS: Between 2010 and 2020, we conducted a single-centre study with all consecutive patients without known cardiovascular disease referred for cardiac CT. LACI was defined as the ratio of left atrial to left ventricle end-diastolic volumes. The primary outcome was cardiovascular death. Cox regressions were used to evaluate the association between LACI and primary outcome after adjustment for traditional risk factors and cardiac CT angiography findings. RESULTS: In 1,444 patients (mean age, 70 ± 12 [standard deviation] years; 43% men), 67 (4.3%) patients experienced cardiovascular death after a median follow-up of 6.8 (Q1, Q3: 5.9, 9.1) years. After adjustment, LACI was positively associated with the occurrence of cardiovascular death (adjusted hazard ratio [HR], 1.07 [95% CI: 1.05-1.09] per 1% increment; P < 0.001), and all-cause death (adjusted HR, 1.05 [95% CI: 1.03-1.07] per 1% increment; P <0.001). After adjustment, a LACI ≥ 25% showed the best improvement in model discrimination and reclassification for predicting cardiovascular death above traditional risk factors and cardiac CT findings (C-statistic improvement: 0.27; Nnet reclassification improvement = 0.826; Integrative discrimination index =0.209, all P < 0.001; likelihood-ratio-test, P < 0.001). CONCLUSION: LACI measured by cardiac CT is independently associated with cardiovascular death and all-cause death in patients without known cardiovascular disease referred for cardiac CT, with an incremental prognostic value over traditional risk factors and cardiac CT findings.

Application of artificial intelligence to imaging interpretations in the musculoskeletal area: Where are we? Where are we going?

The interest of researchers, clinicians and radiologists, in artificial intelligence (AI) continues to grow. Deep learning is a subset of machine learning, in which the computer algorithm itself can determine the optimal imaging features to answer a clinical question. Convolutional neural networks are the most common architecture for performing deep learning on medical images. The various musculoskeletal applications of deep learning are the detection of abnormalities on X-rays or cross-sectional images (CT, MRI), for example the detection of fractures, meniscal tears, anterior cruciate ligament tears, degenerative lesions of the spine, bone metastases, classification of e.g., dural sac stenosis, degeneration of intervertebral discs, assessment of skeletal age, and segmentation, for example of cartilage. Software developments are already impacting the daily practice of orthopedic imaging by automatically detecting fractures on radiographs. Improving image acquisition protocols, improving the quality of low-dose CT images, reducing acquisition times in MRI, or improving MR image resolution is possible through deep learning. Deep learning offers an automated way to offload time-consuming manual processes and improve practitioner performance. This article reviews the current state of AI in musculoskeletal imaging.

Safety, feasibility, and prognostic value of stress perfusion CMR in patients with MR-conditional pacemaker.

AIMS: To assess the safety, feasibility, and prognostic value of stress cardiovascular magnetic resonance (CMR) in patients with pacemaker (PM). METHODS AND RESULTS: Between 2008 and 2021, we conducted a bi-centre longitudinal study with all consecutive patients with MR-conditional PM referred for vasodilator stress CMR at 1.5 T in the Institut Cardiovasculaire Paris Sud and Lariboisiere University Hospital. They were followed for the occurrence of major adverse cardiovascular events (MACE) defined as cardiac death or non-fatal myocardial infarction. Cox regression analyses were performed to determine the prognostic value of CMR parameters. The quality of CMR was rated by two observers blinded to clinical details. Of 304 patients who completed the CMR protocol, 273 patients (70% male, mean age 71 ± 9 years) completed the follow-up (median [interquartile range], 7.1 [5.4-7.5] years). Among those, 32 experienced a MACE (11.7%). Stress CMR was well tolerated with no significant change in lead thresholds or pacing parameters. Overall, the image quality was rated good or excellent in 84.9% of segments. Ischaemia and late gadolinium enhancement (LGE) were significantly associated with the occurrence of MACE (hazard ratio, HR: 11.71 [95% CI: 4.60-28.2]; and HR: 5.62 [95% CI: 2.02-16.21], both P < 0.001). After adjustment for traditional risk factors, ischaemia and LGE were independent predictors of MACE (HR: 5.08 [95% CI: 2.58-14.0]; and HR: 2.28 [95% CI: 2.05-3.76]; both P < 0.001). CONCLUSION: Stress CMR is safe, feasible and has a good discriminative prognostic value in consecutive patients with PM.

Machine-Learning Score Using Stress CMR for Death Prediction in Patients With Suspected or Known CAD.

BACKGROUND: In patients with suspected or known coronary artery disease, traditional prognostic risk assessment is based on a limited selection of clinical and imaging findings. Machine learning (ML) methods can take into account a greater number and complexity of variables. OBJECTIVES: This study sought to investigate the feasibility and accuracy of ML using stress cardiac magnetic resonance (CMR) and clinical data to predict 10-year all-cause mortality in patients with suspected or known coronary artery disease, and compared its performance with existing clinical or CMR scores. METHODS: Between 2008 and 2018, a retrospective cohort study with a median follow-up of 6.0 (IQR: 5.0-8.0) years included all consecutive patients referred for stress CMR. Twenty-three clinical and 11 stress CMR parameters were evaluated. ML involved automated feature selection by random survival forest, model building with a multiple fractional polynomial algorithm, and 5 repetitions of 10-fold stratified cross-validation. The primary outcome was all-cause death based on the electronic National Death Registry. The external validation cohort of the ML score was performed in another center. RESULTS: Of 31,752 consecutive patients (mean age: 63.7 ± 12.1 years, and 65.7% male), 2,679 (8.4%) died with 206,453 patient-years of follow-up. The ML score (ranging from 0 to 10 points) exhibited a higher area under the curve compared with Clinical and Stress Cardiac Magnetic Resonance score, European Systematic Coronary Risk Estimation score, QRISK3 score, Framingham Risk Score, and stress CMR data alone for prediction of 10-year all-cause mortality (ML score: 0.76 vs Clinical and Stress Cardiac Magnetic Resonance score: 0.68, European Systematic Coronary Risk Estimation score: 0.66, QRISK3 score: 0.64, Framingham Risk Score: 0.63, extent of inducible ischemia: 0.66, extent of late gadolinium enhancement: 0.65; all P < 0.001). The ML score also exhibited a good area under the curve in the external cohort (0.75). CONCLUSIONS: The ML score including clinical and stress CMR data exhibited a higher prognostic value to predict 10-year death compared with all traditional clinical or CMR scores.

Cumulative Radiation Exposure in Covid-19 Patients Admitted to the Intensive Care Unit.

Medical imaging plays a major role in coronavirus disease-2019 (COVID-19) patient diagnosis and management. However, the radiation dose received from medical procedures by these patients has been poorly investigated. We aimed to estimate the cumulative effective dose (CED) related to medical exposure in COVID-19 patients admitted to the intensive care unit (ICU) in comparison to the usual critically ill patients. We designed a descriptive cohort study including 90 successive ICU COVID-19 patients admitted between March and May 2020 and 90 successive non-COVID-19 patients admitted between March and May 2019. In this study, the CED resulting from all radiological examinations was calculated and clinical characteristics predictive of higher exposure risk identified. The number of radiological examinations was 12.0 (5.0-26.0) [median (interquartile range) in COVID-19 vs. 4.0 (2.0-8.0) in non-COVID-19 patient (P < 0.001)]. The CED during a four-month period was 4.2 mSv (1.9-11.2) in the COVID-19 vs. 1.2 mSv (0.13-6.19) in the non-COVID-19 patients (P < 0.001). In the survivors, the CED in COVID-19 vs. non-COVID-19 patients was ≥100 mSv in 3% vs. 0%, 10-100 mSv in 23% vs. 15%, 1-10 mSv in 56% vs. 30% and <1 mSv in 18% vs. 55%. The CED (P < 0.001) and CED per ICU hospitalization day (P = 0.004) were significantly higher in COVID-19 than non-COVID-19 patients. The CED correlated significantly with the hospitalization duration (r = 0.45, P < 0.001) and the number of conventional radiological examinations (r = 0.8, P < 0.001). To conclude, more radiological examinations were performed in critically ill COVID-19 patients than non-COVID-19 patients resulting in higher CED. In COVID-19 patients, contribution of strategies to limit CED should be investigated in the future.

The shrinking toe sign in gout.

OBJECTIVE: To describe the frequency, clinical presentation and understand the pathophysiology of toe shortenings during urate-lowering treatment (ULT) of gout, a feature we called the shrinking toe sign. METHODS: Sequential foot photographs and radiographs of 1141 consecutive gouty patients followed-up for at least 6 months under ULT were retrospectively scrutinized. Features from patients with toe shortenings were extracted from anonymized files. Tophi adjacent to the shortening sites were semi quantified on foot photographs and toe shortenings were measured on radiographs with the Corel draw software (Corel corporation, Canada). Measurement concordance was assessed by concordance correlation coefficients (CCC) and correlation between tophus scores and toe shortenings was analyzed by using linear model with a patient random effect. 97 patients who did not develop toe shortening during ULT were analyzed as controls. RESULTS: Shrinking toes were observed in 10 patients (0.9%) with tophaceous gout at joints with baseline destructive arthropathy. The first and second toes and metatarsophalangeal joints were predominantly involved. The sign was observed after serum urate had been lowered below the 300 and 360 µmol/l targets, in 8 and 2 patients, respectively. Measured shortenings (CCC: 0.99) correlated (p < 10-4) with decreases in tophus score (CCC: 0.91). Sequential radiograph analysis revealed that toe shortening was mainly due to lytic bone collapse during articular tophus dissolution. Comparison with controls showed that the sign developed in severe gout and in joints with more severe erosion score at baseline. CONCLUSION: The shrinking toe appears as rare feature of severe tophaceous gout, triggered by dissolution of bone-replacing tophi. Our findings reinforce the need to treat gout early, before destruction of bone scaffold by extensive tophi, as MSU crystal dissolution by ULT may further weaken these areas and induce their collapse.

Calcium pyrophosphate crystal deposition in a cohort of 57 patients with Gitelman syndrome.

OBJECTIVE: Gitelman syndrome (GS) is the most frequent salt-wasting genetic tubulopathy and a source of hypokalaemia and hypomagnesemia. Chondrocalcinosis (CC) is a frequent feature of GS. The aim of our study was to determine the prevalence, distribution patterns, clinical phenotypes and risk factors for CC in GS. METHODS: This prospective study of a cohort of 57 patients with GS included a systematic screening for CC by peripheral joint radiography, cervical spine CT and joint US. The prevalence of cervical C1-C2 CC by CT was compared between 33 GS patients and sex- and age-matched controls. Clinical and biochemical features were analysed to identify factors associated with CC. RESULTS: Mean (s.d.) age of patients was 46.5 (12.4) years, 66.7% were women and 93.0% carried SLC12A3 mutations. Mean serum magnesium level was 0.60 (0.30) mmol/l. CC was observed in 79% of patients, with the highest prevalence at the cervical spine (81.8%) followed by the knee (52.6%), wrist (50.9%), ankle (38.6%), TM joint (36.4%), shoulder (33.3%), hip (22.8%), elbow (14.0%) and sclerochoroid (12.1%). Prevalence of CC at the C1-C2 level was higher in the GS cohort than control group (72.7% vs 9.1%) (adjusted odds ratio 21.0, 95% CI 2.8, 156.1, P = 0.003). Independent factors associated with CC were low serum magnesium level and age. CONCLUSION: GS was associated with widespread CC, favoured by aging and hypomagnesemia. The C1-C2 level was the most affected site. Follow-up of this unique cohort will help understanding the clinical consequences of CC, especially the precise characterization of pyrophosphate arthropathy.

Distribution of bony erosions in feet and performance of two bone erosion scores: A dual-energy computed tomography study of 61 patients with gout.

OBJECTIVES: To assess the distribution of bone erosions and two erosion scores in the feet of patients with gout and analyze the association between erosion scores and monosodium urate (MSU) crystal deposition using dual-energy computed tomography (DECT). MATERIALS AND METHODS: We included all patients who underwent DECT of both feet between 2016 and 2019 in our radiology department, with positive detection of MSU deposits. Data on sex, age, treatment, serum urate, and DECT urate volumes were obtained. CT images were analyzed to score bone erosions in 31 sites per foot by using the semi-quantitative method based on the Rheumatoid Arthritis MRI Scoring (RAMRIS) system and the Dalbeth-simplified score. Reproducibility for the two scores was calculated with intraclass correlation coefficients (ICCs). Correlations between clinical features, erosion scores and urate crystal volume were analyzed by the Spearman correlation coefficient (r). RESULTS: We studied 61 patients (mean age 62.0 years); 3,751 bones were scored. The first metatarsophalangeal joint and the midfoot were the most involved in terms of frequency and severity of bone erosions. The distribution of bone erosions was not asymmetrical. The intra- and inter-observer reproducibility was similar for the RAMRIS and Dalbeth-simplified scores (ICC 0.93 vs 0.94 and 0.96 vs 0.90). DECT urate volume was significantly correlated with each of the two erosion scores (r = 0.58-0.63, p < 0.001). There was a high correlation between the two scores (r = 0.96, p < 0.001). CONCLUSIONS: DECT demonstrates that foot erosions are not asymmetric in distribution and predominate at the first ray and midfoot. The two erosion scores are significantly correlated with DECT urate volume. An almost perfect correlation between the RAMRIS and Dalbeth-simplified scores is observed.

Vertebroplasty for osteoporotic vertebral fracture.

Appropriate care of patients with a recent painful osteoporotic vertebral fracture (VF) requires immobilisation, analgesics and spinal orthoses. Some VFs are however responsible for disabling pain and prolonged bed rest. In this context, vertebroplasty techniques have been proposed with a large benefit in case series and open-label randomised studies, but lack efficacy in three among four double-blind randomised studies. The objectives of the treatment of a recent painful VF are to relieve pain and to preserve mechanical conditions. With this in mind, we report an experts' opinion paper on the indications for vertebroplasty and research agenda for clinical studies.

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.

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia.

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.