Applications of machine learning algorithms using texture analysis-derived features extracted from computed tomography and magnetic resonance images. - Valeria Romeo (2018)

Could Blockchain Technology Empower Patients, Improve Education, and Boost Research in Radiology Departments? An Open Question for Future Applications. - Francesco Verde; Arnaldo Stanzione; Valeria Romeo; Renato Cuocolo; Simone Maurea; Arturo Brunetti (J. Digit. Imaging, 2019)

MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study. - Arnaldo Stanzione; Carlo Ricciardi; Renato Cuocolo; Valeria Romeo; Jessica Petrone; Michela Sarnataro; Pier Paolo Mainenti; Giovanni Improta; Filippo De Rosa; Luigi Insabato; Arturo Brunetti; Simone Maurea (J. Digit. Imaging, 2020)

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status. - Carole H. Sudre; Jasmina Panovska; Eser Sanverdi; Sebastian Brandner; Vasileios K. Katsaros; George Stranjalis; Francesca B. Pizzini; Claudio Ghimenton; Katarina Surlan-Popovic; Jernej Avsenik; Maria Vittoria Spampinato; Mario Nigro; et al. (BMC Medical Informatics Decis. Mak., 2020)

Identifying Phenotypic Concepts Discriminating Molecular Breast Cancer Sub-Types. - Christoph Fürböck; Matthias Perkonigg; Thomas Helbich; Katja Pinker; Valeria Romeo; Georg Langs (MICCAI (8), 2022)

A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer. - Valentina Brancato; Nadia Brancati; Giusy Esposito; Massimo La Rosa; Carlo Cavaliere; Ciro Allarà; Valeria Romeo; Giuseppe De Pietro; Marco Salvatore; Marco Aiello 003; Mara Sangiovanni (Sensors, 2023)