1,721,042 research outputs found
Patients' findings after COVID-19 infection and vaccinations: what to expect from [18F]FDG PET/CT
No full textFDG-PET in Cardiac Infections.
No full textCardiac infections include a group of conditions involving the heart muscle, the pericardium, or the endocardial surface of the heart. Infections can extend to prosthetic material or the leads in case of the implantation of devices. Despite their relative low incidence, these conditions that are associated with high morbidity and mortality involve a relevant burden of diagnostic workup. Early diagnosis is crucial for adequate management of patient, as early treatment improves the prognosis; unfortunately, the clinical manifestations are often nonspecific. Accurate and timely diagnosis typically requires the correlation of imaging findings with laboratory data. (18)F-FDG-PET is a well-established imaging modality for the diagnosis and management of malignancies, and evidence is also increasing regarding its value for assessing infectious and inflammatory diseases. This article summarizes published evidence on the usefulness of (18)F-FDG-PET for the diagnosis of cardiac infections, mainly focused on endocarditis and cardiovascular device infections. Nevertheless, the diagnostic potential of (18)F-FDG-PET in patients with pericarditis and myocarditis is also briefly reviewed, considering the most likely future advances and new perspectives that the use of PET/magnetic resonance would open in the diagnosis of such conditions
Quantitative imaging biomarkers in nuclear medicine: from SUV to image mining studies. Highlights from annals of nuclear medicine 2018
No full textObjective: Quantification in medical imaging is one of the main goals in research and clinical practice since it allows immediate understanding, objective communication, and comparison. Our aim was to summarize relevant investigations on quantification in nuclear medicine studies published in the volume 32 of Annals of Nuclear Medicine. Methods: In this article, we summarized the data of 14 selected papers from international research groups that were published between January and December 2018. This is a descriptive review with an inherently subjective selection of articles. Results: We discussed the role of parameters ranging from standardized uptake value to ratios, to flow within a region of interest, to volumetric parameters and to texture indices in different clinical scenarios in oncology, cardiology, and neurology. Conclusions: In all the medical disciplines in which nuclear medicine examinations play a role, quantification is essential both in research and in clinical practice. Standardization and high-quality protocols are crucial for the success and reliability of imaging biomarkers
Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
Full text linkPurpose The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process. Methods Among the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies. Results Overall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the develop- ment of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials. Conclusions The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools
Lymphoscintigraphy for the Differential Diagnosis of Peripheral Edema and Intracavitary Lymph Effusion
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