A5036026172- Radiomics and Machine Learning in Medical Imaging
- Glioma Diagnosis and Treatment
- Meningioma and schwannoma management
- Artificial Intelligence in Healthcare and Education
- MRI in cancer diagnosis
- AI in cancer detection
- Pituitary Gland Disorders and Treatments
- Advanced X-ray and CT Imaging
- Neurofibromatosis and Schwannoma Cases
- Head and Neck Surgical Oncology
- Neurological disorders and treatments
- Salivary Gland Tumors Diagnosis and Treatment
- Spinal Fractures and Fixation Techniques
- Medical Imaging Techniques and Applications
- Lysosomal Storage Disorders Research
- Sarcoma Diagnosis and Treatment
- Head and Neck Cancer Studies
- Spine and Intervertebral Disc Pathology
- Cerebrospinal fluid and hydrocephalus
- Fetal and Pediatric Neurological Disorders
- Genetic Neurodegenerative Diseases
- Moyamoya disease diagnosis and treatment
- Multiple Sclerosis Research Studies
- Pancreatic and Hepatic Oncology Research
- Protein Tyrosine Phosphatases
Federico II University Hospital
2016-2025
University of Naples Federico II
2016-2025
Berlin Institute of Health at Charité - Universitätsmedizin Berlin
2024
Centre Hospitalier Universitaire de Reims
2024
Technologies pour la Santé
2024
Hôpital Maison Blanche
2024
Humboldt-Universität zu Berlin
2024
Freie Universität Berlin
2024
Foundation for Research and Technology Hellas
2024
Sağlık Bilimleri Üniversitesi
2024
Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine practice. The workflow of complex due several methodological steps and nuances, which often leads inadequate reporting evaluation, poor reproducibility. Available guidelines checklists artificial intelligence predictive modeling include relevant good practices, but they are not tailored radiomic research. There a clear...
Abstract Purpose To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research of radiomics studies. Methods We conducted an online modified Delphi study with group international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members identify the items be voted; Stage#3, four rounds exercise by panelists determine eligible for METRICS their weights. The...
adiology is one of the most technology-driven medical specialties and has always been closely linked to computer science.In particular, ever since picture archiving communication system (PACS) revolution, there have many examples emerging new technology that shaped reshaped day-to-day practice radiologists. 1 More recently, scientific community witnessed remarkable progress artificial intelligence (AI), advances in image-recognition tasks are likely herald another significant leap forward...
To investigate whether a radiomic machine learning (ML) approach employing texture-analysis (TA) features extracted from primary tumor lesions (PTLs) is able to predict grade (TG) and nodal status (NS) in patients with oropharyngeal (OP) oral cavity (OC) squamous-cell carcinoma (SCC).Contrast-enhanced CT images of 40 OP OC SCC were post-processed extract TA PTLs. A feature selection method different ML algorithms applied find the most accurate subset TG NS.For prediction TG, best accuracy...
To investigate the intra- and inter-rater reliability of total radiomics quality score (RQS) reproducibility individual RQS items' in a large multireader study.
Abstract Purpose Pituitary macroadenoma consistency can influence the ease of lesion removal during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not assessable on standard qualitative MRI. Radiomic texture analysis could help in extracting mineable quantitative tissue characteristics. We aimed to assess accuracy combined with machine learning preoperative evaluation pituitary patients undergoing endoscopic endonasal surgery. Methods Data 89 (68 soft and 21...
Background Experience with the endovascular treatment of cerebral aneurysms using p64 Flow Modulation Device is still limited. This study discusses results and complications this new flow diverter device. Methods 40 patients (30 women, 10 men) 50 treated in six Italian neurointerventional centers between April 2013 September 2015 were retrospectively reviewed. Results Complete occlusion was obtained 44/50 (88%) partial 3 (6%). In other three (6%), two cases asymptomatic in-stent thrombosis...
Abstract Background Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value such framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status. Methods Three hundred thirty-three 6 tertiary centres, diagnosed...
To systematically review current research applications of radiomics in patients with cholangiocarcinoma and to assess the quality CT MRI studies.A systematic search was conducted on PubMed/Medline, Web Science, Scopus databases identify original studies assessing and/or MRI. Three readers different experience levels independently assessed using score (RQS). Subgroup analyses were performed according journal type, year publication, quartile impact factor (from Journal Citation Report...