Initiation of enzyme replacement therapy (ERT) early in the Fabry disease course may facilitate better outcomes than in patients with advanced disease. Early diagnosis is often hindered by the heterogeneous nature of signs and symptoms, and by the presentation of atypical phenotypes. The Sophisticated Assessment of Disease Burden in Patients with Fabry Disease study (SOPHIA; ClinicalTrials.gov, NCT01210196) evaluated clinical and diagnostic assessments for early detection of Fabry-related organ pathology in ERT-naïve patients with mild FD symptoms. Assessments included cardiac magnetic resonance imaging with late gadolinium enhancement (LGE-CMR), echocardiography, 24-h Holter electrocardiography, and biomarkers of FD and fibrosis. 35 patients with mean (SD) baseline age of 45.0 (10.2) years were included and assessed at baseline, 12 months, and (optionally) at 24 months. At baseline, LGE-CMR and elevated procollagen III N-terminal propeptide, sphingosine-1-phosphate, and globotriaosylsphingosine were the most prevalent indicators of early Fabry-related pathology. LGE was already present in 58.8% of patients with normal left ventricular mass index. 15.2% of patients showed grade 1 diastolic dysfunction. QRS duration increased from baseline to last observation, particularly in patients with severe baseline fibrosis. Fibrosis progressed from baseline to last observation, especially in patients with baseline LGE ≥ 2.50 mL (3.65 [1.14] mL vs 6.74 [1.10] mL). Statistically significant correlations were found between LGE volume and high-sensitivity troponin T, and between LGE volume and fragments of urinary collagen alpha-1 (I), (III), and (VII), and collagen alpha-3 (V). Fibrosis may become apparent before left ventricular hypertrophy occurs. LGE-CMR imaging is superior to conventional echocardiography for detecting early cardiomyopathy in FD and, in conjunction with biomarker tests, may help detect early organ involvement in mild FD.

Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.

Orbital masses include a broad spectrum of benign and malignant entities. Often these masses are asymptomatic or show a slow growth rate, so that emergence of clinical symptoms is prolonged. In this context, cross-sectional imaging plays an elementary role in the characterization of these lesions. Aside from the characterization of the underlying entity, an evaluation of the involved compartments is possible by sufficient imaging, which also facilitates optimal treatment and surgery planning. The purpose of this review is to explore different benign and malignant orbital tumors and their typical appearance in imaging together with histopathologic findings.

Mantle cell lymphoma (MCL) is a rare lymphoid malignancy with a poor prognosis characterised by frequent relapse and short durations of treatment response. Most patients present with aggressive disease, but there exist indolent subtypes without the need for immediate intervention. The very heterogeneous behaviour of MCL is genetically characterised by the translocation t(11;14)(q13;q32), leading to Cyclin D1 overexpression with distinct clinical and biological characteristics and outcomes. There is still an unfulfilled need for precise MCL prognostication in real-time. Machine learning and deep learning neural networks are rapidly advancing technologies with promising results in numerous fields of application. This study develops and compares the performance of deep learning (DL) algorithms and radiomics-based machine learning (ML) models to predict MCL relapse on baseline CT scans. Five classification algorithms were used, including three deep learning models (3D SEResNet50, 3D DenseNet, and an optimised 3D CNN) and two machine learning models based on K-nearest Neighbor (KNN) and Random Forest (RF). The best performing method, our optimised 3D CNN, predicted MCL relapse with a 70% accuracy, better than the 3D SEResNet50 (62%) and the 3D DenseNet (59%). The second-best performing method was the KNN-based machine learning model (64%) after principal component analysis for improved accuracy. Our optimised CNN developed by ourselves correctly predicted MCL relapse in 70% of the patients on baseline CT imaging. Once prospectively tested in clinical trials with a larger sample size, our proposed 3D deep learning model could facilitate clinical management by precision imaging in MCL.

Background: Chronic non-bacterial osteomyelitis (CNO) is an autoinflammatory bone disorder affecting children and adolescents. Previously classified as a rare disease, recent studies suggest a higher incidence of the disease. CNO may develop into the clinical presentation of chronic recurrent osteomyelitis (CRMO) with high relapse rate and multifocality.Main body: Diagnosis of CNO/CRMO is often delayed, with implications for disease severity and relapse rate. This can be significantly improved by knowledge of the disease entity and its characteristics. Imaging plays a key role in diagnosis, differential diagnosis and therapy monitoring. Magnetic resonance imaging (MRI) has several advantages compared to other imaging methods and is increasingly applied in clinical studies. Recent studies show that a whole-body (WB) coverage (WB-MRI) without contrast agent administration is a rational approach. This educational review is based on a systematic analysis of international peer-reviewed articles and presents our own clinical experiences. It provides an overview of disease entity, incidence and clinical diagnosis. The role of imaging, especially of whole-body MRI, is discussed in detail. Finally, practical advice for imaging, including flowcharts explaining when and how to apply imaging, is provided.Conclusion: Knowing the specifics of CNO/CRMO and the importance of MRI/whole-body MRI allows rapid and efficient diagnosis as well as therapy support and helps to avoid irreversible secondary damage.