Purpose: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. Methods: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. Result: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. Conclusion: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. Critical relevance statement: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. Key points: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score (https://metricsscore.github.io/metrics/METRICS.html) and a repository created to collect feedback from the radiomics community (https://github.com/metricsscore/metrics). Graphical

With the advent of large language models (LLMs), the artificial intelligence revolution in medicine and radiology is now more tangible than ever. Every day, an increasingly large number of articles are published that utilize LLMs in radiology. To adopt and safely implement this new technology in the field, radiologists should be familiar with its key concepts, understand at least the technical basics, and be aware of the potential risks and ethical considerations that come with it. In this review article, the authors provide an overview of the LLMs that might be relevant to the radiology community and include a brief discussion of their short history, technical basics, ChatGPT, prompt engineering, potential applications in medicine and radiology, advantages, disadvantages and risks, ethical and regulatory considerations, and future directions.

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects - Diagnostic and Interventional Radiology

Over the past two decades, the epidemiology of chronic liver disease has changed with an increase in the prevalence of nonalcoholic fatty liver disease in parallel to the advent of curative treatments for hepatitis C. Recent developments provided new tools for diagnosis and monitoring of liver diseases based on ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI), as applied for assessing steatosis, fibrosis, and focal lesions. This narrative review aims to discuss the emerging approaches for qualitative and quantitative liver imaging, focusing on those expected to become adopted in clinical practice in the next 5 to 10 years. While radiomics is an emerging tool for many of these applications, dedicated techniques have been investigated for US (controlled attenuation parameter, backscatter coefficient, elastography methods such as point shear wave elastography [pSWE] and transient elastography [TE], novel Doppler techniques, and three-dimensional contrast-enhanced ultrasound [3D-CEUS]), CT (dual-energy, spectral photon counting, extracellular volume fraction, perfusion, and surface nodularity), and MRI (proton density fat fraction [PDFF], elastography [MRE], contrast enhancement index, relative enhancement, T1 mapping on the hepatobiliary phase, perfusion). Concurrently, the advent of abbreviated MRI protocols will help fulfill an increasing number of examination requests in an era of healthcare resource constraints.

Epiploic appendagitis is a rare cause of acute abdominal pain, determined by a benign self-limiting inflammation of the epiploic appendages. It may manifest with heterogeneous clinical presentations, mimicking other more severe entities responsible of acute abdominal pain, such as acute diverticulitis or appendicitis. Given its importance as clinical mimicker, imaging plays a crucial role to avoid inaccurate diagnosis that may lead to unnecessary hospitalization, antibiotic therapy, and surgery. CT represents the gold standard technique for the evaluation of patients with indeterminate acute abdominal pain. Imaging findings include the presence of an oval lesion with fat-attenuation surrounded by a thin hyperdense rim on CT (“hyperattenuating ring sign”) abutting anteriorly the large bowel, usually associated with inflammation of the adjacent mesentery. A central high-attenuation focus within the fatty lesion (“central dot sign”) can sometimes be observed and is indicative of a central thrombosed vein within the inflamed epiploic appendage. Rarely, epiploic appendagitis may be located within a hernia sac or attached to the vermiform appendix. Chronically infarcted epiploic appendage may detach, appearing as an intraperitoneal loose calcified body in the abdominal cavity. In this review, we aim to provide an overview of the clinical presentation and key imaging features that may help the radiologist to make an accurate diagnosis and guide the clinical management of those patients.

The histopathological subtypes of hepatocellular carcinoma (HCC) are associated with distinct clinical features and prognoses. This study aims to report Liver Imaging Reporting and Data System (LI-RADS)-defined imaging features of different HCC subtypes in a cohort of resected tumours and to assess the influence of HCC subtypes on computed tomography (CT)/magnetic resonance imaging (MRI) LI-RADS categorisation in the subgroup of high-risk patients. This retrospective institutional review board-approved study included patients with resected HCCs and available histopathological classification. Three radiologists independently reviewed preoperative CT and MRI exams. The readers evaluated the presence of imaging features according to LI-RADS v2018 definitions and provided a LI-RADS category in patients at high risk of HCC. Differences in LI-RADS features and categorisations were assessed for not otherwise specified (NOS-HCC), steatohepatitic (SH-HCC), and macrotrabecular-massive (MTM-HCC) types of HCCs. Two hundred and seventy-seven patients (median age 64.0 years, 215 [77.6%] men) were analysed, which involved 295 HCCs. There were 197 (66.7%) NOS-HCCs, 62 (21.0%) SH-HCCs, 23 (7.8%) MTM-HCCs, and 13 (4.5%) other rare subtypes. The following features were more frequent in MTM-HCC: elevated α-foetoprotein serum levels (p <0.001), tumour-in-vein (p <0.001 on CT, p ≤0.052 on MRI), presence of at least 1 LR-M feature (p ≤0.010 on CT), infiltrative appearance (p ≤0.032 on CT), necrosis or severe ischaemia (p ≤0.038 on CT), and larger size (p ≤0.006 on CT, p ≤0.011 on MRI). SH-HCC was associated with fat in mass (p <0.001 on CT, p ≤0.002 on MRI). The distribution of the LI-RADS major features and categories in high-risk patients did not significantly differ among the 3 main HCC subtypes. The distribution of LI-RADS major features and categories is not different among the HCC subtypes. Nevertheless, careful analysis of tumour-in-vein, LR-M, and ancillary features as well as clinico-biological data can provide information for the non-invasive diagnosis of HCC subtypes. In high-risk patients, the overall distribution of LI-RADS major features and categories is not different among the histological subtypes of hepatocellular carcinoma, but tumour-in-vein, presence of LR-M features, and ancillary features can provide information for the non-invasive diagnosis of hepatocellular carcinoma subtypes.