Structured reporting of rectal cancer staging and restaging: A consensus proposal
Background: Structured reporting (SR) in oncologic imaging is becoming necessary and has recently been recognized by major scientific societies. The aim of this study was to build MRI-based structured reports for rectal cancer (RC) staging and restaging in order to provide clinicians all critical tumor information. Materials and Methods: A panel of radiologist experts in abdominal imaging, called the members of the Italian Society of Medical and Interventional Radiology, was established. The modified Delphi process was used to build the SR and to assess the level of agreement in all sections. The Cronbach’s alpha (Cα) correlation coefficient was used to assess the internal consistency of ea…
Diagnostic accuracy of computed tomographic colonography for the detection of advanced neoplasia in individuals at increased risk of colorectal cancer.
CONTEXT: Computed tomographic (CT) colonography has been recognized as an alternative for colorectal cancer (CRC) screening in average-risk individuals, but less information is available on its performance in individuals at increased risk of CRC. OBJECTIVE: To assess the accuracy of CT colonography in detecting advanced colorectal neoplasia in asymptomatic individuals at increased risk of CRC using unblinded colonoscopy as the reference standard. DESIGN, SETTING, AND PARTICIPANTS: This was a multicenter, cross-sectional study. Individuals at increased risk of CRC due to either family history of advanced neoplasia in first-degree relatives, personal history of colorectal adenomas, or positiv…
CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools
[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…
A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia
Abstract Background The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model. Methods LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived…
ESGAR consensus statement on liver MR imaging and clinical use of liver-specific contrast agents
Objectives: To develop a consensus and provide updated recommendations on liver MR imaging and the clinical use of liver-specific contrast agents. Methods: The European Society of Gastrointestinal and Abdominal Radiology (ESGAR) formed a multinational European panel of experts, selected on the basis of a literature review and their leadership in the field of liver MR imaging. A modified Delphi process was adopted to draft a list of statements. Descriptive and Cronbach’s statistics were used to rate levels of agreement and internal reliability of the consensus. Results: Three Delphi rounds were conducted and 76 statements composed on MR technique (n = 17), clinical application of liver-speci…