0000000000367315
AUTHOR
Francesco Sardanelli
showing 4 related works from this author
CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools
2022
[EN] The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data. Subse…
Multicenter, double-blind, randomized, intraindividual crossover comparison of gadobenate dimeglumine and gadopentetate dimeglumine for Breast MR ima…
2011
To intraindividually compare 0.1 mmol/kg doses of gadobenate dimeglumine and gadopentetate dimeglumine for contrast material-enhanced breast magnetic resonance (MR) imaging by using a prospective, multicenter double-blind, randomized protocol.Institutional review board approval and patient informed consent were obtained. One hundred sixty-two women (mean age, 52.8 years ± 12.3 [standard deviation]) enrolled at 17 sites in Europe and China between July 2007 and May 2009 underwent at least one breast MR imaging examination at 1.5 T by using three-dimensional spoiled gradient-echo sequences. Of these, 151 women received both contrast agents in randomized order in otherwise identical examinatio…
Clinical indications for the use of cardiac MRI. By the SIRM Study Group on Cardiac Imaging
2013
Cardiac magnetic resonance (CMR) is considered an useful method in the evaluation of many cardiac disorders. Based on our experience and available literature, we wrote a document as a guiding tool in the clinical use of CMR. Synthetically we describe different cardiac disorders and express for each one a classification, I to IV, depending on the significance of diagnostic information expected.
Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features
2021
Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann–Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset spli…