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Abstract Over the last few years, radiation therapy (RT) techniques have evolved very rapidly, with the aim of conforming high-dose volume tightly to a target. Although to date CT is still considered the imaging modality for target delineation, it has some known limited capabilities in properly identifying pathologic processes occurring, for instance, in soft tissues. This limitation, along with other advantages such as dose reduction, can be overcome using magnetic resonance imaging (MRI), which is increasingly being recognized as a useful tool in RT clinical practice. This review has a two-fold aim of providing a basic introduction to the physics of MRI in a narrative way and illustrating the current knowledge on its application “from head to toe” (i.e., different body sites), in order to highlight the numerous advantages in using MRI to ensure the best therapeutic response. We provided a basic introduction for residents and non-radiologist on the physics of MR and reported evidence of the advantages and future improvements of MRI in planning a tailored radiotherapy treatment “from head to toe”. Critical relevance statement This review aims to help understand how MRI has become indispensable, not only to better characterize and evaluate lesions, but also to predict the evolution of the disease and, consequently, to ensure the best therapeutic response. Key Points MRI is increasingly gaining interest and applications in RT planning. MRI provides high soft tissue contrast resolution and accurate delineation of the target volume. MRI will increasingly become indispensable for characterizing and evaluating lesions, and to predict the evolution of disease. Graphical Abstract

Abstract Various healthcare domains have witnessed successful preliminary implementation of artificial intelligence (AI) solutions, including radiology, though limited generalizability hinders their widespread adoption. Currently, most research groups and industry have limited access to the data needed for external validation studies. The creation and accessibility of benchmark datasets to validate such solutions represents a critical step towards generalizability, for which an array of aspects ranging from preprocessing to regulatory issues and biostatistical principles come into play. In this article, the authors provide recommendations for the creation of benchmark datasets in radiology, explain current limitations in this realm, and explore potential new approaches. Clinical relevance statement Benchmark datasets, facilitating validation of AI software performance can contribute to the adoption of AI in clinical practice. Key Points Benchmark datasets are essential for the validation of AI software performance. Factors like image quality and representativeness of cases should be considered. Benchmark datasets can help adoption by increasing the trustworthiness and robustness of AI. Graphical Abstract

Abstract In order to assess the perceptions and expectations of the radiology staff about artificial intelligence (AI), we conducted an online survey among ESR members (January–March 2024). It was designed considering that conducted in 2018, updated according to recent advancements and emerging topics, consisting of seven questions regarding demographics and professional background and 28 AI questions. Of 28,000 members contacted, 572 (2%) completed the survey. AI impact was predominantly expected on breast and oncologic imaging, primarily involving CT, mammography, and MRI, and in the detection of abnormalities in asymptomatic subjects. About half of responders did not foresee an impact of AI on job opportunities. For 273/572 respondents (48%), AI-only reports would not be accepted by patients; and 242/572 respondents (42%) think that the use of AI systems will not change the relationship between the radiological team and the patient. According to 255/572 respondents (45%), radiologists will take responsibility for any AI output that may influence clinical decision-making. Of 572 respondents, 274 (48%) are currently using AI, 153 (27%) are not, and 145 (25%) are planning to do so. In conclusion, ESR members declare familiarity with AI technologies, as well as recognition of their potential benefits and challenges. Compared to the 2018 survey, the perception of AI's impact on job opportunities is in general slightly less optimistic (more positive from AI users/researchers), while the radiologist’s responsibility for AI outputs is confirmed. The use of large language models is declared not only limited to research, highlighting the need for education in AI and its regulations. Critical relevance statement This study critically evaluates the current impact of AI on radiology, revealing significant usage patterns and clinical implications, thereby guiding future integration strategies to enhance efficiency and patient care in clinical radiology. Key Points The survey examines ESR member's views about the impact of AI on radiology practice. AI use is relevant in CT and MRI, with varying impacts on job roles. AI tools enhance clinical efficiency but require radiologist oversight for patient acceptance. Graphical Abstract

Abstract Two-dimensional transition metal dichalcogenides, particularly MoS2, are interesting materials for many applications in aerospace research, radiation therapy and bioscience more in general. Since in many of these applications MoS2-based nanomaterials can be placed in an aqueous environment while exposed to ionizing radiation, both experimental and theoretical studies of their behaviour under these conditions is particularly interesting. Here, we study the effects of tiny imparted doses of 511 keV photons to MoS2 nanoflakes in water solution. To the best of our knowledge, this is the first study in which ionizing radiation on 2D-MoS2 occurs in water. Interestingly, we find that, in addition to the direct interaction between high-energy photons and nanoflakes, reactive chemical species, generated by γ-photons induced radiolysis of water, come into play a relevant role. A radiation transport Monte Carlo simulation allowed determining the elements driving the morphological and spectroscopical changes of 2D-MoS2, experimentally monitored by SEM microscopy, DLS, Raman and UV–vis spectroscopy, AFM, and X-ray photoelectron techniques. Our study demonstrates that radiolysis products affect the Molybdenum oxidation state, which is massively changed from the stable + 4 and + 6 states into the rarer and more unstable + 5. These findings will be relevant for radiation-based therapies and diagnostics in patients that are assuming drugs or contrast agents containing 2D-MoS2 and for aerospace biomedical applications of 2DMs investigating their actions into living organisms on space station or satellites.

The aim of the present study was to evaluate the safety and efficacy of radionuclide therapy with [177Lu]Lu-DOTA-TATE according to our single center experience at the University of Naples Federico II. For the present analysis, we considered 21 patients with progressive, advanced, well-differentiated G1 and G2 in patients with gastro-entero-pancreatic (GEP) neuroendocrine tumors (NETs) treated with [177Lu]Lu-DOTA-TATE according to the decisions of a multidisciplinary team. All patients underwent four cycles of 7–8 GBq of [177Lu]Lu-DOTA-TATE every 8 weeks. A whole-body scan (WBS) was performed 4, 48, and 168 h after each treatment. The dosimetry towards the organ at risk and target lesions was calculated. For each patient, renal and bone marrow parameters were evaluated before, during, and 3 months after the end of the treatment. Follow-up data were obtained and RECIST criteria were considered as the endpoint. Among 21 patients enrolled (mean age 65 ± 9 years); 17 (81%) were men and the small intestine was the most frequent location of disease (n = 12). A mild albeit significant variation (p < 0.05) in both platelets and white blood cell counts among all time points was observed, despite it disappearing 3 months after the end of the therapy. According to the RECIST criteria, 11 (55%) patients had a partial response to therapy and 8 (40%) had stable disease. Only one (5%) patient had disease progression 4 months after treatment. Our data confirm that [177Lu]Lu-DOTA is safe and effective in controlling the burden disease of G1/G2 GEP-NETs patients.

Abstract Starting from Picasso’s quote (“Computers are useless. They can only give you answers”), we discuss the introduction of generative artificial intelligence (AI), including generative adversarial networks (GANs) and transformer-based architectures such as large language models (LLMs) in radiology, where their potential in reporting, image synthesis, and analysis is notable. However, the need for improvements, evaluations, and regulations prior to clinical use is also clear. Integration of LLMs into clinical workflow needs cautiousness, to avoid or at least mitigate risks associated with false diagnostic suggestions. We highlight challenges in synthetic image generation, inherent biases in AI models, and privacy concerns, stressing the importance of diverse training datasets and robust data privacy measures. We examine the regulatory landscape, including the 2023 Executive Order on AI in the United States and the 2024 AI Act in the European Union, which set standards for AI applications in healthcare. This manuscript contributes to the field by emphasizing the necessity of maintaining the human element in medical procedures while leveraging generative AI, advocating for a “machines-in-the-loop” approach.

Background: Differentiated thyroid cancer (DTC) patients have an outstanding overall long-term survival rate, and certain subsets of DTC patients have a very high likelihood of disease recurrence. Radioactive iodine (RAI) therapy is a cornerstone in DTC management, but cancer cells can eventually develop resistance to RAI. Radioactive iodine-refractory DTC (RAIR-DTC) is a condition defined by ATA 2015 guidelines when DTC cannot concentrate RAI ab initio or loses RAI uptake ability after the initial therapy. The RAIR condition implies that RAI cannot reveal new met-astatic foci, so RAIR-DTC metabolic imaging needs new tracers. 18F-FDG PET/CT has been widely used and has demonstrated prognostic value, but 18F-FDG DTC avidity may remain low. Fibroblast activation protein inhibitors (FA-Pi)s, prostatic-specific membrane antigen (PSMA), and somatostatin receptor (SSTR) tracers have been proposed as theragnostic agents in experimental settings and Arg-Gly-Asp (RGD) peptides in the diagnostic trial field. Multi-targeted tyrosine kinase inhibitors are relatively new drugs approved in RAIR-DTC therapy. Despite the promising targeted setting, they relate to frequent adverse-event onset. Sorafenib and trametinib have been included in re-differentiation protocols aimed at re-inducing RAI accumulation in DTC cells. Results appear promising, but not excellent. Conclusions: RAIR-DTC remains a challenging nosological entity. There are still controversies on RAIR-DTC definition and post-RAI therapy evaluation, with post-therapy whole-body scan (PT-WBS) the only validated criterion of response. The recent introduction of multiple diagnostic and therapeutic agents obliges physicians to pursue a multidisciplinary approach aiming to correct drug introduction and timing choice.

Introduction: Bone mineral density (BMD) is reduced in patients with human immunodeficiency virus (HIV) infection. Trabecular bone score (TBS) is an additional feature calculated by dual-energy X ray absorption (DXA) that measures texture inhomogeneity at lumbar spine level, providing an index of bone microarchitecture. However, its clinical value still needs to be fully addressed. Aims of the study were to assess BMD and TBS in a cohort of patients with HIV compared to a population of healthy subjects and to investigate the prognostic value of TBS in HIV infected patients. Method: Bone health was assessed by DXA in 165 patients with HIV infection (120 men, mean age 40 ± 7 years) and in 164 healthy subjects (53 male, mean age 37 ± 10 years). BMD was measured at level of lumbar spine (L1-L4), femoral neck and total hip. TBS was computed from the images of lumbar spine using machine proprietary software. Results: BMD at femoral neck level was similar in HIV infected patients and healthy subjects (p = 0.57), whereas BMD measured in total femur was lower in HIV infected patients compared to healthy subjects (p

Abstract 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, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR’s usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics−Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item’s explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts. Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research. Key points • As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts. • Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3. • The resulting explanation and elaboration document with examples can be accessed at https://radiomic.github.io/CLEAR-E3/ . Graphical Abstract

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.

Abstract Objective To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020. Methods A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed. Results Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies. Conclusions Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage. Critical relevance statement An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage. Key points • 2021–2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation. Graphical Abstract

Executive functions (EFs) are neurocognitive processes planning and regulating daily life actions. Performance of two simultaneous tasks, requiring the same cognitive resources, lead to a cognitive fatigue. Several studies investigated cognitive-motor task and the interference during walking, highlighting an increasing risk of falls especially in elderly and people with neurological diseases. A few studies instrumentally explored relationship between activation-no-activation of two EFs (working memory and inhibition) and spatial-temporal gait parameters. Aim of our study was to detect activation of inhibition and working memory during progressive difficulty levels of cognitive tasks and spontaneous walking using, respectively, wireless electroencephalography (EEG) and 3D-gait analysis. Thirteen healthy subjects were recruited. Two cognitive tasks were performed, activating inhibition (Go-NoGo) and working memory (N-back). EEG features (absolute and relative power in different bands) and kinematic parameters (7 spatial-temporal ones and Gait Variable Score for 9 range of motion of lower limbs) were analyzed. A significant decrease of stride length and an increase of external-rotation of foot progression were found during dual task with Go-NoGo. Moreover, a significant correlation was found between the relative power in the delta band at channels Fz, C4 and progressive difficulty levels of Go-NoGo (activating inhibition) during walking, whereas working memory showed no correlation. This study reinforces the hypothesis of the prevalent involvement of inhibition with respect to working memory during dual task walking and reveals specific kinematic adaptations. The foundations for EEG-based monitoring of cognitive processes involved in gait are laid. Clinical and Translational Impact Statement: Clinical and instrumental evaluation and training of executive functions (as inhibition), during cognitive-motor task, could be useful for rehabilitation treatment of gait disorder in elderly and people with neurological disease.

Abstract 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 Abstract

Whole-body positron emission tomography (PET)–computed tomography (CT) imaging performed for oncological purposes may provide additional parameters such as the coronary artery calcium (CAC) and epicardial adipose tissue (EAT) volume with cost-effective prognostic information in asymptomatic people beyond traditional cardiovascular risk factors. We evaluated the feasibility of measuring the CAC score and EAT volume in cancer patients without known coronary artery disease (CAD) referred to whole-body 18F-FDG PET–CT imaging, regardless of the main clinical problem. We also investigated the potential relationships between traditional cardiovascular risk factors and CAC with EAT volume. A total of 109 oncological patients without overt CAD underwent whole-body PET–CT imaging with 18F-fluorodeoxyglucose (FDG). Unenhanced CT images were retrospectively viewed for CAC and EAT measurements on a dedicated platform. Overall, the mean EAT volume was 99 ± 49 cm3. Patients with a CAC score ≥ 1 were older than those with a CAC = 0 (p < 0.001) and the prevalence of hypertension was higher in patients with detectable CAC as compared to those without (p < 0.005). The EAT volume was higher in patients with CAC than in those without (p < 0.001). For univariable age, body mass index (BMI), hypertension, and CAC were associated with increasing EAT values (all p < 0.005). However, the correlation between the CAC score and EAT volume was weak, and in multivariable analysis only age and BMI were independently associated with increased EAT (both p < 0.001), suggesting that potential prognostic information on CAC and EAT is not redundant. This study demonstrates the feasibility of a cost-effective assessment of CAC scores and EAT volumes in oncological patients undergoing whole-body 18F-FDG PET–CT imaging, enabling staging cancer disease and atherosclerotic burden by a single test already included in the diagnostic work program, with optimization of the radiation dose and without additional costs.