A5007322283- Radiomics and Machine Learning in Medical Imaging
- Prostate Cancer Diagnosis and Treatment
- MRI in cancer diagnosis
- Prostate Cancer Treatment and Research
- Advanced X-ray and CT Imaging
- Artificial Intelligence in Healthcare and Education
- AI in cancer detection
- Urologic and reproductive health conditions
- Cardiac Imaging and Diagnostics
- Adrenal and Paraganglionic Tumors
- Renal cell carcinoma treatment
- Endometrial and Cervical Cancer Treatments
- Radiology practices and education
- Radiation Dose and Imaging
- Medical Imaging Techniques and Applications
- Maternal and fetal healthcare
- Pancreatic and Hepatic Oncology Research
- Advanced MRI Techniques and Applications
- Uterine Myomas and Treatments
- Ultrasound in Clinical Applications
- Sarcoma Diagnosis and Treatment
- Health Systems, Economic Evaluations, Quality of Life
- Clinical practice guidelines implementation
- Meta-analysis and systematic reviews
- Urological Disorders and Treatments
University of Naples Federico II
2018-2025
Addenbrooke's Hospital
2023
University of Cambridge
2023
Federico II University Hospital
2016-2021
University College London Hospitals NHS Foundation Trust
2019
University College London
2019
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...
Background Prostate volume, as determined by magnetic resonance imaging (MRI), is a useful biomarker both for distinguishing between benign and malignant pathology can be used either alone or combined with other parameters such prostate‐specific antigen. Purpose This study compared different deep learning methods whole‐gland zonal prostate segmentation. Study Type Retrospective. Population A total of 204 patients (train/test = 99/105) from the PROSTATEx public dataset. Field...
Abstract Objectives We aimed to assess the performance of radiomics and machine learning (ML) for classification non-cystic benign malignant breast lesions on ultrasound images, compare ML’s accuracy with that a radiologist, verify if radiologist’s is improved by using ML. Methods Our retrospective study included patients from two institutions. A total 135 Institution 1 were used train test ML model cross-validation. Radiomic features extracted manually annotated images underwent multistep...
To investigate the intra- and inter-rater reliability of total radiomics quality score (RQS) reproducibility individual RQS items' in a large multireader study.
Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, can be accomplished with systematic use guidelines. The CheckList for EvaluAtion Radiomics (CLEAR) was previously developed assist authors their radiomic reviewers evaluation. To take full advantage CLEAR, further explanation elaboration each item, well literature examples, may useful. main goal this work,...
Abstract Objectives To build a machine learning (ML) model to detect extraprostatic extension (EPE) of prostate cancer (PCa), based on radiomics features extracted from MRI index lesions. Methods Consecutive exams patients undergoing radical prostatectomy for PCa were retrospectively collected three institutions. Axial T2-weighted and apparent diffusion coefficient map images annotated obtain lesion volumes interest feature extraction. Data one institution was used training, selection (using...