Artificial intelligence‐based analysis of body composition in Marfan: skeletal muscle density and psoas muscle index predict aortic enlargement

Background: Patients with Marfan syndrome are at risk for aortic enlargement and are routinely monitored by computed tomography (CT) imaging. The purpose of this study is to analyse body composition using artificial intelligence (AI)-based tissue segmentation in patients with Marfan syndrome in order to identify possible predictors of progressive aortic enlargement. Methods: In this study, the body composition of 25 patients aged <= 50 years with Marfan syndrome and no prior aortic repair was analysed at the third lumbar vertebra (L3) level from a retrospective dataset using an AI-based software tool (Visage Imaging). All patients underwent electrocardiography-triggered CT of the aorta twice within 2 years for suspected progression of aortic disease, suspected dissection, and/or pre-operative evaluation. Progression of aortic enlargement was defined as an increase in diameter at the aortic sinus or the ascending aorta of at least 2 mm. Patients meeting this definition were assigned to the 'progressive aortic enlargement' group (proAE group) and patients with stable diameters to the 'stable aortic enlargement' group (staAE group). Statistical analysis was performed using the Mann-Whitney U test. Two possible body composition predictors of aortic enlargement-skeletal muscle density (SMD) and psoas muscle index (PMI)-were analysed further using multivariant logistic regression analysis. Aortic enlargement was defined as the dependent variant, whereas PMI, SMD, age, sex, body mass index (BMI), beta blocker medication, and time interval between CT scans were defined as independent variants. Results: There were 13 patients in the proAE group and 12 patients in the staAE group. AI-based automated analysis of body composition at L3 revealed a significantly increased SMD measured in Hounsfield units (HUs) in patients with aortic enlargement (proAE group: 50.0 +/- 8.6 HU vs. staAE group: 39.0 +/- 15.0 HU; P = 0.03). PMI also trended towards higher values in the proAE group (proAE group: 6.8 +/- 2.3 vs. staAE group: 5.6 +/- 1.3; P = 0.19). Multivariate logistic regression revealed significant prediction of aortic enlargement for SMD (P = 0.05) and PMI (P = 0.04). Conclusions: Artificial intelligence-based analysis of body composition at L3 in Marfan patients is feasible and easily available from CT angiography. Analysis of body composition at L3 revealed significantly higher SMD in patients with progressive aortic enlargement. PMI and SMD significantly predicted aortic enlargement in these patients. Using body composition as a predictor of progressive aortic enlargement may contribute information for risk stratification regarding follow-up intervals and the need for aortic repair

First PACS‐integrated artificial intelligence‐based software tool for rapid and fully automatic analysis of body composition from CT in clinical routine

Background: To externally evaluate the first picture archiving communications system (PACS)-integrated artificial intelligence (AI)-based workflow, trained to automatically detect a predefined computed tomography (CT) slice at the third lumbar vertebra (L3) and automatically perform complete image segmentation for analysis of CT body composition and to compare its performance with that of an established semi-automatic segmentation tool regarding speed and accuracy of tissue area calculation. Methods: For fully automatic analysis of body composition with L3 recognition, U-Nets were trained (Visage) and compared with a conventional image segmentation software (TomoVision). Tissue was differentiated into psoas muscle, skeletal muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Mid-L3 level images from randomly selected DICOM slice files of 20 CT scans acquired with various imaging protocols were segmented with both methods. Results: Success rate of AI-based L3 recognition was 100%. Compared with semi-automatic, fully automatic AI-based image segmentation yielded relative differences of 0.22% and 0.16% for skeletal muscle, 0.47% and 0.49% for psoas muscle, 0.42% and 0.42% for VAT and 0.18% and 0.18% for SAT. AI-based fully automatic segmentation was significantly faster than semi-automatic segmentation (3 ± 0 s vs. 170 ± 40 s, P < 0.001, for User 1 and 152 ± 40 s, P < 0.001, for User 2). Conclusion: Rapid fully automatic AI-based, PACS-integrated assessment of body composition yields identical results without transfer of critical patient data. Additional metabolic information can be inserted into the patient’s image report and offered to the referring clinicians

Effects of Artificial Intelligence-Derived Body Composition on Kidney Graft and Patient Survival in the Eurotransplant Senior Program

The Eurotransplant Senior Program allocates kidneys to elderly transplant patients. The aim of this retrospective study is to investigate the use of computed tomography (CT) body composition using artificial intelligence (AI)-based tissue segmentation to predict patient and kidney transplant survival. Body composition at the third lumbar vertebra level was analyzed in 42 kidney transplant recipients. Cox regression analysis of 1-year, 3-year and 5-year patient survival, 1-year, 3-year and 5-year censored kidney transplant survival, and 1-year, 3-year and 5-year uncensored kidney transplant survival was performed. First, the body mass index (BMI), psoas muscle index (PMI), skeletal muscle index (SMI), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) served as independent variates. Second, the cut-off values for sarcopenia and obesity served as independent variates. The 1-year uncensored and censored kidney transplant survival was influenced by reduced PMI (p = 0.02 and p = 0.03, respectively) and reduced SMI (p = 0.01 and p = 0.03, respectively); 3-year uncensored kidney transplant survival was influenced by increased VAT (p = 0.04); and 3-year censored kidney transplant survival was influenced by reduced SMI (p = 0.05). Additionally, sarcopenia influenced 1-year uncensored kidney transplant survival (p = 0.05), whereas obesity influenced 3-year and 5-year uncensored kidney transplant survival. In summary, AI-based body composition analysis may aid in predicting short- and long-term kidney transplant survival

High-resolution ab initio three-dimensional X-ray diffraction microscopy

Coherent X-ray diffraction microscopy is a method of imaging non-periodic isolated objects at resolutions only limited, in principle, by the largest scattering angles recorded. We demonstrate X-ray diffraction imaging with high resolution in all three dimensions, as determined by a quantitative analysis of the reconstructed volume images. These images are retrieved from the 3D diffraction data using no a priori knowledge about the shape or composition of the object, which has never before been demonstrated on a non-periodic object. We also construct 2D images of thick objects with infinite depth of focus (without loss of transverse spatial resolution). These methods can be used to image biological and materials science samples at high resolution using X-ray undulator radiation, and establishes the techniques to be used in atomic-resolution ultrafast imaging at X-ray free-electron laser sources.Comment: 22 pages, 11 figures, submitte

Biallelic ADAM22 pathogenic variants cause progressive encephalopathy and infantile-onset refractory epilepsy

Pathogenic variants in A Disintegrin And Metalloproteinase (ADAM) 22, the postsynaptic cell membrane receptor for the glycoprotein leucine-rich repeat glioma-inactivated protein 1 (LGI1), have been recently associated with recessive developmental and epileptic encephalopathy. However, so far, only two affected individuals have been described and many features of this disorder are unknown. We refine the phenotype and report 19 additional individuals harboring compound heterozygous or homozygous inactivating ADAM22 variants, of whom 18 had clinical data available. Additionally, we provide follow-up data from two previously reported cases. All affected individuals exhibited infantile-onset, treatment-resistant epilepsy. Additional clinical features included moderate to profound global developmental delay/intellectual disability (20/20), hypotonia (12/20), delayed motor development (19/20). Brain MRI findings included cerebral atrophy (13/20), supported by post-mortem histological examination in patient-derived brain tissue, cerebellar vermis atrophy (5/20), and callosal hypoplasia (4/20). Functional studies in transfected cell lines confirmed the deleteriousness of all identified variants and indicated at least three distinct pathological mechanisms: defective cell membrane expression (1), impaired LGI1-binding (2), and/or impaired interaction with the postsynaptic density protein PSD-95 (3). We reveal novel clinical and molecular hallmarks of ADAM22 deficiency and provide knowledge that might inform clinical management and early diagnostics

Biallelic ADAM22 pathogenic variants cause progressive encephalopathy and infantile-onset refractory epilepsy

Pathogenic variants in A Disintegrin And Metalloproteinase (ADAM) 22, the postsynaptic cell membrane receptor for the glycoprotein leucine-rich repeat glioma-inactivated protein 1 (LGI1), have been recently associated with recessive developmental and epileptic encephalopathy. However, so far, only two affected individuals have been described and many features of this disorder are unknown. We refine the phenotype and report 19 additional individuals harbouring compound heterozygous or homozygous inactivating ADAM22 variants, of whom 18 had clinical data available. Additionally, we provide follow-up data from two previously reported cases. All affected individuals exhibited infantile-onset, treatment-resistant epilepsy. Additional clinical features included moderate to profound global developmental delay/intellectual disability (20/20), hypotonia (12/20) and delayed motor development (19/20). Brain MRI findings included cerebral atrophy (13/20), supported by post-mortem histological examination in patient-derived brain tissue, cerebellar vermis atrophy (5/20), and callosal hypoplasia (4/20). Functional studies in transfected cell lines confirmed the deleteriousness of all identified variants and indicated at least three distinct pathological mechanisms: (i) defective cell membrane expression; (ii) impaired LGI1-binding; and/or (iii) impaired interaction with the postsynaptic density protein PSD-95. We reveal novel clinical and molecular hallmarks of ADAM22 deficiency and provide knowledge that might inform clinical management and early diagnostics. Van der Knoop et al. describe the clinical features of 21 individuals with biallelic pathogenic variants in ADAM22 and confirm the deleteriousness of the variants with functional studies. Clinical hallmarks of this rare disorder comprise progressive encephalopathy and infantile-onset refractory epilepsy.Peer reviewe

Biallelic variants in PCDHGC4 cause a novel neurodevelopmental syndrome with progressive microcephaly, seizures, and joint anomalies.

PURPOSE: We aimed to define a novel autosomal recessive neurodevelopmental disorder, characterize its clinical features, and identify the underlying genetic cause for this condition. METHODS: We performed a detailed clinical characterization of 19 individuals from nine unrelated, consanguineous families with a neurodevelopmental disorder. We used genome/exome sequencing approaches, linkage and cosegregation analyses to identify disease-causing variants, and we performed three-dimensional molecular in silico analysis to predict causality of variants where applicable. RESULTS: In all affected individuals who presented with a neurodevelopmental syndrome with progressive microcephaly, seizures, and intellectual disability we identified biallelic disease-causing variants in Protocadherin-gamma-C4 (PCDHGC4). Five variants were predicted to induce premature protein truncation leading to a loss of PCDHGC4 function. The three detected missense variants were located in extracellular cadherin (EC) domains EC5 and EC6 of PCDHGC4, and in silico analysis of the affected residues showed that two of these substitutions were predicted to influence the Ca2+-binding affinity, which is essential for multimerization of the protein, whereas the third missense variant directly influenced the cis-dimerization interface of PCDHGC4. CONCLUSION: We show that biallelic variants in PCDHGC4 are causing a novel autosomal recessive neurodevelopmental disorder and link PCDHGC4 as a member of the clustered PCDH family to a Mendelian disorder in humans