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Abstract The aim of this pilot study was to assess the compliance of breast cancer (BC) patients with fitness tracker (FT) monitoring program during radiotherapy (RT) and to characterize radiation-induced fatigue (RIF) status through objective evaluation using FT-collected parameters. Thirty-six BC patients were invited to wear FT during their RT course for continuous monitoring of heart rate (HR) and step counts (STP). RIF assessment was performed weekly, according to CTCAE v5.0 and dichotomized into G0 vs. any-grade. A novel concept based on patient Repeated Activity Window (RAW) was introduced to evaluate HR and STP variations during RT. Several Machine Learning (ML) methods were trained to characterize RIF on the basis of HR and STP collected data. RIF of any grade was reported by 17 out of 36 patients (47%) included in the study. None of patient clinical variables were significantly correlated with RIF. All patients accepted the FT monitoring program, and for 32 patients FT collection efficiency was greater than 60%. For each patient, a distinct distribution of RAWs was identifiable over RT and across the entire patient cohort, with a total of 7950 RAWs processed. Six features related to RAWs, HR and STP were identified as associated with RIF. The best-performing classifier was the Bagged Trees model, showing a cross-validated ROC-AUC of 89% (95% CI 88–90%). This study confirms the feasibility of continuous biomedical monitoring of BC patients by FT. We successfully identify objective indicators of RIF through HR and STP variation measures within each patient’s RAW, thus providing a novel and practical approach to assess and manage RIF. This can significantly aid medical staff in evaluating RIF trajectories, potentially leading to better individualized care strategies and improved patient outcomes.

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 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.

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

Abstract Objectives Imaging guidelines could play an important role in the training of radiologists, but the extent of their adoption in residency programs is unclear. With this survey, the European Society of Urogenital Radiology (ESUR) Junior Network aimed to assess the dissemination of the ESUR guidelines on endometrial cancer MRI staging (EC-ESUR guidelines) among young radiologists. Methods An online questionnaire targeted to last year radiology residents and radiologists in the first year of their career was designed. It included 24 questions, structured in 4 sections (i.e., background, general, acquisition protocol, interpretation, and reporting). The survey was active between April and May 2022, accepting answers worldwide. Answers were solicited with a social media campaign and with the support of national scientific societies. Subgroup analysis was performed based on variables such as subspecialty of interest and number of EC-ESUR guidelines consultations using the Wilcoxon rank sum test. Results In total, 118 participants completed the questionnaire, of which 94 (80%) were from Europe and 46 (39%) with a special interest in urogenital radiology. Overall, 68 (58%) stated that the guidelines were not part of their residency teaching programs while 32 (27%) had never even consulted the guidelines. Interest in urogenital radiology as a subspecialty and EC-ESUR guidelines consultations were associated with greater confidence in supervising scan acquisition, interpreting, and reporting EC MRI staging exams. Conclusion Four years after publication, the adoption of EC-ESUR guidelines in residency programs is heterogeneously low. Despite a possible selection bias, our findings indicate that active promotion of EC-ESUR guidelines is required. Key points • The adoption of ESUR guidelines on endometrial cancer in radiology residency programs is heterogeneous. • Almost one third of respondents stated they had never even consulted the guidelines. • Confidence toward guidelines was higher in those who were exposed to more endometrial cancer MRI staging scans. • Reading the guidelines was associated with a greater confidence in protocol acquisition, interpretation, and reporting. • Active efforts to promote their dissemination are required.

In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology.

Abstract Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine clinical practice. The workflow of radiomics is complex due to several methodological steps and nuances, which often leads to inadequate reporting and evaluation, and poor reproducibility. Available reporting guidelines and checklists for artificial intelligence and predictive modeling include relevant good practices, but they are not tailored to radiomic research. There is a clear need for a complete radiomics checklist for study planning, manuscript writing, and evaluation during the review process to facilitate the repeatability and reproducibility of studies. We here present a documentation standard for radiomic research that can guide authors and reviewers. Our motivation is to improve the quality and reliability and, in turn, the reproducibility of radiomic research. We name the checklist CLEAR (CheckList for EvaluAtion of Radiomics research), to convey the idea of being more transparent. With its 58 items, the CLEAR checklist should be considered a standardization tool providing the minimum requirements for presenting clinical radiomics research. In addition to a dynamic online version of the checklist, a public repository has also been set up to allow the radiomics community to comment on the checklist items and adapt the checklist for future versions. Prepared and revised by an international group of experts using a modified Delphi method, we hope the CLEAR checklist will serve well as a single and complete scientific documentation tool for authors and reviewers to improve the radiomics literature.

Key points The quality of current radiomics studies on cholangiocarcinoma is insufficient, with a median radiomics quality score of 8–10, corresponding to 22–28% of the ideal quality score. None of the current studies conducted phantom assessment, imaging at multiple time points, prospective registration in a trial database, nor cost-effectiveness analysis. The inter-reader agreement of the radiomics quality score is good (ICC of 0.75; 95% CI 0.62–0.85) among readers with different levels of experience.

In the field of computer science, known as artificial intelligence, algorithms imitate reasoning tasks that are typically performed by humans. The techniques that allow machines to learn and get better at tasks such as recognition and prediction, which form the basis of clinical practice, are referred to as machine learning, which is a subfield of artificial intelligence. The number of artificial intelligence- and machine learnings-related publications in clinical journals has grown exponentially, driven by recent developments in computation and the accessibility of simple tools. However, clinicians are often not included in data science teams, which may limit the clinical relevance, explanability, workflow compatibility, and quality improvement of artificial intelligence solutions. Thus, this results in the language barrier between clinicians and artificial intelligence developers. Healthcare practitioners sometimes lack a basic understanding of artificial intelligence research because the approach is difficult for non-specialists to understand. Furthermore, many editors and reviewers of medical publications might not be familiar with the fundamental ideas behind these technologies, which may prevent journals from publishing high-quality artificial intelligence studies or, worse still, could allow for the publication of low-quality works. In this review, we aim to improve readers’ artificial intelligence literacy and critical thinking. As a result, we concentrated on what we consider the 10 most important qualities of artificial intelligence research: valid scientific purpose, high-quality data set, robust reference standard, robust input, no information leakage, optimal bias-variance tradeoff, proper model evaluation, proven clinical utility, transparent reporting, and open science. Before designing a study, one should have defined a sound scientific purpose. Then, it should be backed by a high-quality data set, robust input, and a solid reference standard. The artificial intelligence development pipeline should prevent information leakage. For the models, optimal bias-variance tradeoff should be achieved, and generalizability assessment must be adequately performed. The clinical value of the final models must also be established. After the study, thought should be given to transparency in publishing the process and results as well as open science for sharing data, code, and models. We hope this work may improve the artificial intelligence literacy and mindset of the readers.

Key Points The clinical role of PWI in HNSCC still has to be defined. Evidence-based recommendations are provided for the acquisition of PWI sequence in HNSCC. A modified-Delphi approach was used to reach a consensus among selected experts.

Abstract Background T1 mapping is an established cardiovascular magnetic resonance (CMR) technique that can characterize myocardial tissue. We aimed to determine the weighted mean native T1 values of Anderson-Fabry disease (AFD) patients and the standardized mean differences (SMD) as compared to healthy control subjects. Methods A comprehensive literature search of the PubMed, Scopus and Web of Science databases was conducted according to the PRISMA statement to retrieve original studies reporting myocardial native T1 values in AFD patients and healthy controls. A random effects model was used to calculate SMD, and meta-regression analysis was conducted to explore heterogeneity sources. Subgroup analysis was also performed according to scanner field strength and sequence type. Results From a total of 151 items, 14 articles were included in the final analysis accounting for a total population of 982 subjects. Overall, the weighted mean native T1 values was 984 ± 47 ms in AFD patients and 1016 ± 26 ms in controls (P

BackgroundWhite matter hyperintensities (WMHs) of the brain are observed in normal aging, in various subtypes of dementia and in chronic pain, playing a crucial role in pain processing. The aim of the study has been to assess the WMHs in Burning Mouth Syndrome (BMS) patients by means of the Age-Related White Matter Changes scale (ARWMCs) and to analyze their predictors.MethodsOne hundred BMS patients were prospectively recruited and underwent magnetic resonance imaging (MRI) of the brain. Their ARWMCs scores were compared with those of an equal number of healthy subjects matched for age and sex. Intensity and quality of pain, psychological profile, and blood biomarkers of BMS patients were further investigated to find potential predictors of WMHs. Specifically, the Numeric Rating Scale (NRS), Short-Form McGill Pain Questionnaire (SF-MPQ), Hamilton rating scale for Depression and Anxiety (HAM-D and HAM-A), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS) were administered.ResultsThe BMS patients presented statistically significant higher scores on the ARWMCs compared to the controls, especially in the right frontal, left frontal, right parietal-occipital, left parietal-occipital, right temporal and left temporal lobes (p-values:

Abstract Background Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability. Results Out of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies. Conclusions The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.

Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes.

Magnetic resonance imaging (MRI) is the gold-standard technique for evaluating muscle fatty infiltration and muscle atrophy due to its high contrast resolution. It can differentiate muscular from adipose tissue accurately. MRI can also quantify the adipose content within muscle bellies with several sequences such as T1-mapping, T2-mapping, spectroscopy, Dixon, intra-voxel incoherent motion, and diffusion tensor imaging. The main fields of interest in musculoskeletal radiology for a quantitative MRI evaluation of muscular fatty infiltration include neuro-muscular disorders such as myopathies, and dystrophies. Sarcopenia is another important field in which the evaluation of the degree of muscular fat infiltration or muscular hypotrophy is required for a correct diagnosis. This review highlights several MRI techniques and sequences focusing on quantitative methods of assessing adipose tissue and muscle atrophy.

We report a case of a sixth-month-pregnant 37-year-old woman with abdominal pain with the presence of a dermoid cyst of the left ovary. The diagnostic work-up required a multi-modality imaging approach. In particular, US and MR examinations were initially performed but resulted with an inconclusive outcome of a final diagnosis. Hence, a CT scan was successively used to formulate lesion characterization. Thus, integrated imaging approach would be recommended. Keywords: Dermoid cyst, Ovarian teratoma, Pregnancy, Multimodality imaging

Abstract Purpose We aimed to assess the role of radiologists, cardiologists, and other medical and non-medical figures in cardiac magnetic resonance imaging (MRI) research in the last 34 years, focusing on first and last authorship, number of published studies, and journal impact factors (IF). Methods Articles in the field of cardiac MRI were considered in this systematic review and retrospective bibliometric analysis. For included studies, the first and last authors were categorized as cardiologists, radiologists/nuclear medicine physicians, medical doctors (MD) with specialties in both cardiology and radiology/nuclear medicine, and other MD and non-MD. Differences in the number of papers published overall and by year and institution location for the first and last author category were assessed. Mean IF differences between author categories were also investigated. Results A total of 2053 articles were included in the final analysis. For the first authors (n = 2011), 52% were cardiologists, 22% radiologists/nuclear medicine physicians, 16% other MD, 10% other non-MD, and 1% both cardiologists and radiologists/nuclear medicine physicians. Similarly, the last authors (n = 2029) resulted 54% cardiologists, 22% radiologists/nuclear medicine physicians, 15% other MD, 8% other non-MD, and 2% both cardiologists and radiologists/nuclear medicine physicians. No significant differences due to institution location in the first and last authorship proportions were found. Average journal IF was significantly higher for cardiologist first and last authors when compared to that of radiologists/nuclear medicine physicians (both p < 0.0001). Conclusion Over 50% of studies in the field of cardiac MRI published in the last 34 years are conducted by cardiologists.

Summary: Background: Atypical cartilaginous tumour (ACT) and grade II chondrosarcoma (CS2) of long bones are respectively managed with watchful waiting or curettage and wide resection. Preoperatively, imaging diagnosis can be challenging due to interobserver variability and biopsy suffers from sample errors. The aim of this study is to determine diagnostic performance of MRI radiomics-based machine learning in differentiating ACT from CS2 of long bones. Methods: One-hundred-fifty-eight patients with surgically treated and histology-proven cartilaginous bone tumours were retrospectively included at two tertiary bone tumour centres. The training cohort consisted of 93 MRI scans from centre 1 (n=74 ACT; n=19 CS2). The external test cohort consisted of 65 MRI scans from centre 2 (n=45 ACT; n=20 CS2). Bidimensional segmentation was performed on T1-weighted MRI. Radiomic features were extracted. After dimensionality reduction and class balancing in centre 1, a machine-learning classifier (Extra Trees Classifier) was tuned on the training cohort using 10-fold cross-validation and tested on the external test cohort. In centre 2, its performance was compared with an experienced musculoskeletal oncology radiologist using McNemar's test. Findings: After tuning on the training cohort (AUC=0.88), the machine-learning classifier had 92% accuracy (60/65, AUC=0.94) in identifying the lesions in the external test cohort. Its accuracies in correctly classifying ACT and CS2 were 98% (44/45) and 80% (16/20), respectively. The radiologist had 98% accuracy (64/65) with no difference compared to the classifier (p=0.134). Interpretation: Machine learning showed high accuracy in classifying ACT and CS2 of long bones based on MRI radiomic features. Funding: ESSR Young Researchers Grant.

Background: Due to its common association with chronic pain experience, cognitive impairment (CI) has never been evaluated in patients with burning mouth syndrome (BMS). The purpose of this study is to assess the prevalence of CI in patients with BMS and to evaluate its relationship with potential predictors such as pain, mood disorders, blood biomarkers, and white matter changes (WMCs).Methods: A case-control study was conducted by enrolling 40 patients with BMS and an equal number of healthy controls matched for age, gender, and education. Neurocognitive assessment [Mini Mental State Examination (MMSE), Digit Cancellation Test (DCT), the Forward and Backward Digit Span task (FDS and BDS), Corsi Block-Tapping Test (CB-TT), Rey Auditory Verbal Learning Test (RAVLT), Copying Geometric Drawings (CGD), Frontal Assessment Battery (FAB), and Trail Making A and B (TMT-A and TMT-B)], psychological assessment [Hamilton Rating Scale for Depression and Anxiety (HAM-D and HAM-A), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and 36-Item Short Form Health Survey (SF-36)], and pain assessment [Visual Analogic Scale (VAS), Total Pain Rating index (T-PRI), Brief Pain Inventory (BPI), and Pain DETECT Questionnaire (PD-Q)] were performed. In addition, blood biomarkers and MRI of the brain were recorded for the detection of Age-Related WMCs (ARWMCs). Descriptive statistics, the Mann-Whitney U-test, the Pearson Chi-Squared test and Spearman's correlation analysis were used.Results: Patients with BMS had impairments in most cognitive domains compared with controls (p < 0.001**) except in RAVLT and CGD. The HAM-D, HAM-A, PSQI, ESS, SF-36, VAS, T-PRI, BPI and PD-Q scores were statistically different between BMS patients and controls (p < 0.001**) the WMCs frequency and ARWMC scores in the right temporal (RT) and left temporal (LT) lobe were higher in patients with BMS (p = 0.023*).Conclusions: Meanwhile, BMS is associated with a higher decline in cognitive functions, particularly attention, working memory, and executive functions, but other functions such as praxis-constructive skills and verbal memory are preserved. The early identification of CI and associated factors may help clinicians to identify patients at risk of developing time-based neurodegenerative disorders, such as Alzheimer's disease (AD) and vascular dementia (VD), for planning the early, comprehensive, and multidisciplinary assessment and treatment.

Abstract Intra-cranial and spinal foreign body reactions represent potential complications of medical procedures. Their diagnosis may be challenging as they frequently show an insidious clinical presentation and can mimic other life-threatening conditions. Their pathophysiological mechanism is represented by a local inflammatory response due to retained or migrated surgical elements. Cranial interventions may be responsible for the presence of retained foreign objects represented by surgical materials (such as sponges, bone wax, and Teflon). Spinal diagnostic and therapeutic procedures, including myelography, chordotomy, vertebroplasty, and device implantation, are another potential source of foreign bodies. These reactions can also follow material migration or embolization, for example in the case of Lipiodol, Teflon, and cement vertebroplasty. Imaging exams, especially CT and MRI, have a central role in the differential diagnosis of these conditions together with patient history. Neuroradiological findings are dependent on the type of material that has been left in or migrated from the surgical area. Knowledge of these entities is relevant for clinical practice as the correct identification of foreign bodies and related inflammatory reactions, material embolisms, or migrations can be difficult. This pictorial review reports neuroradiological semeiotics and differential diagnosis of foreign body-related imaging abnormalities in the brain and spine.

Abstract With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic resonance imaging (MRI). After defining the difference between ML and classical rule-based algorithms and the distinction among supervised, unsupervised and reinforcement learning, we explain the characteristic of deep learning (DL), a particular new type of ML, including its structure mimicking human neural networks and its ‘black box’ nature. Differences in the pipeline for applying ML and DL to prostate MRI are highlighted. The following potential clinical applications in different settings are outlined, many of them based only on MRI-unenhanced sequences: gland segmentation; assessment of lesion aggressiveness to distinguish between clinically significant and indolent cancers, allowing for active surveillance; cancer detection/diagnosis and localisation (transition versus peripheral zone, use of prostate imaging reporting and data system (PI-RADS) version 2), reading reproducibility, differentiation of cancers from prostatitis benign hyperplasia; local staging and pre-treatment assessment (detection of extraprostatic disease extension, planning of radiation therapy); and prediction of biochemical recurrence. Results are promising, but clinical applicability still requires more robust validation across scanner vendors, field strengths and institutions.

Dual-energy computed tomography (DECT) represents an emerging imaging technique which consists of the acquisition of two separate datasets utilizing two different X-ray spectra energies. Several cardiac DECT applications have been assessed, such as virtual monoenergetic images, virtual non-contrast reconstructions, and iodine myocardial perfusion maps, which are demonstrated to improve diagnostic accuracy and image quality while reducing both radiation and contrast media administration. This review will summarize the technical basis of DECT and review the principal cardiac applications currently adopted in clinical practice, exploring possible future applications.

Background: Clinical management ranges from surveillance or curettage to wide resection for atypical to higher-grade cartilaginous tumours, respectively. Our aim was to investigate the performance of computed tomography (CT) radiomics-based machine learning for classification of atypical cartilaginous tumours and higher-grade chondrosarcomas of long bones. Methods: One-hundred-twenty patients with histology-proven lesions were retrospectively included. The training cohort consisted of 84 CT scans from centre 1 (n=55 G1 or atypical cartilaginous tumours; n=29 G2-G4 chondrosarcomas). The external test cohort consisted of the CT component of 36 positron emission tomography-CT scans from centre 2 (n=16 G1 or atypical cartilaginous tumours; n=20 G2-G4 chondrosarcomas). Bidimensional segmentation was performed on preoperative CT. Radiomic features were extracted. After dimensionality reduction and class balancing in centre 1, the performance of a machine-learning classifier (LogitBoost) was assessed on the training cohort using 10-fold cross-validation and on the external test cohort. In centre 2, its performance was compared with preoperative biopsy and an experienced radiologist using McNemar's test. Findings: The classifier had 81% (AUC=0.89) and 75% (AUC=0.78) accuracy in identifying the lesions in the training and external test cohorts, respectively. Specifically, its accuracy in classifying atypical cartilaginous tumours and higher-grade chondrosarcomas was 84% and 78% in the training cohort, and 81% and 70% in the external test cohort, respectively. Preoperative biopsy had 64% (AUC=0.66) accuracy (p=0.29). The radiologist had 81% accuracy (p=0.75). Interpretation: Machine learning showed good accuracy in classifying atypical and higher-grade cartilaginous tumours of long bones based on preoperative CT radiomic features. Funding: ESSR Young Researchers Grant.

The Prostate Imaging Reporting and Data System (PI-RADS) classification is based on a scale of values from 1 to 5. The value is assigned according to the probability that a finding is a malignant tumor (prostate carcinoma) and is calculated by evaluating the signal behavior in morphological, diffusion, and post-contrastographic sequences. A PI-RADS score of 3 is recognized as the equivocal likelihood of clinically significant prostate cancer, making its diagnosis very challenging. While PI-RADS values of 4 and 5 make biopsy necessary, it is very hard to establish whether to perform a biopsy or not in patients with a PI-RADS score 3. In recent years, machine learning algorithms have been proposed for a wide range of applications in medical fields, thanks to their ability to extract hidden information and to learn from a set of data without previous specific programming. In this paper, we evaluate machine learning approaches in detecting prostate cancer in patients with PI-RADS score 3 lesions via considering clinical-radiological characteristics. A total of 109 patients were included in this study. We collected data on body mass index (BMI), location of suspicious PI-RADS 3 lesions, serum prostate-specific antigen (PSA) level, prostate volume, PSA density, and histopathology results. The implemented classifiers exploit a patient’s clinical and radiological information to generate a probability of malignancy that could help the physicians in diagnostic decisions, including the need for a biopsy.

Photon-counting computed tomography (CT) is a technology that has attracted increasing interest in recent years since, thanks to new-generation detectors, it holds the promise to radically change the clinical use of CT imaging. Photon-counting detectors overcome the major limitations of conventional CT detectors by providing very high spatial resolution without electronic noise, providing a higher contrast-to-noise ratio, and optimizing spectral images. Additionally, photon-counting CT can lead to reduced radiation exposure, reconstruction of higher spatial resolution images, reduction of image artifacts, optimization of the use of contrast agents, and create new opportunities for quantitative imaging. The aim of this review is to briefly explain the technical principles of photon-counting CT and, more extensively, the potential clinical applications of this technology.

Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman’s correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis.

In this study, we aimed to systematically review the current literature on radiomics applied to cross-sectional adrenal imaging and assess its methodological quality. Scopus, PubMed and Web of Science were searched to identify original research articles investigating radiomics applications on cross-sectional adrenal imaging (search end date February 2021). For qualitative synthesis, details regarding study design, aim, sample size and imaging modality were recorded as well as those regarding the radiomics pipeline (e.g., segmentation and feature extraction strategy). The methodological quality of each study was evaluated using the radiomics quality score (RQS). After duplicate removal and selection criteria application, 25 full-text articles were included and evaluated. All were retrospective studies, mostly based on CT images (17/25, 68%), with manual (19/25, 76%) and two-dimensional segmentation (13/25, 52%) being preferred. Machine learning was paired to radiomics in about half of the studies (12/25, 48%). The median total and percentage RQS scores were 2 (interquartile range, IQR = −5–8) and 6% (IQR = 0–22%), respectively. The highest and lowest scores registered were 12/36 (33%) and −5/36 (0%). The most critical issues were the absence of proper feature selection, the lack of appropriate model validation and poor data openness. The methodological quality of radiomics studies on adrenal cross-sectional imaging is heterogeneous and lower than desirable. Efforts toward building higher quality evidence are essential to facilitate the future translation into clinical practice.

Abstract Lytic lesions of the skull include a wide range of diseases, ranging from benign conditions such as arachnoid granulations or vascular lacunae, to aggressive malignant lesions such as lymphomas or metastases. An early and correct characterisation of the nature of the lesion is, therefore, crucial, in order to achieve a fast and appropriate treatment option. In this review, we present the radiological appearance of the most frequent lytic lesions of the skull, describing findings from different imaging modalities (plain X-rays, CT and MRI), with particular attention to diagnostic clues and differential diagnoses. Teaching Points • Osteolytic skull lesions may be challenging to diagnose. • Association of different imaging techniques may aid image interpretation. • Clinical information and extensive knowledge of possible differential diagnoses is essential. • Some osteolytic tumours, although benign, may present as locally aggressive lesions. • Malignant lesions require accurate staging, followed by variable treatment approaches.

Abstract Background In Neurofibromatosis type 1, cerebral Unidentified Bright Objects are a well-known benign entity that has been extensively reported in the literature. In our case series, we wish to focus on a further possible location of such lesions, the spinal cord, which we have defined as medullary Unidentified Bright Objects. These have been, to our knowledge, scarcely described in previous works. Case presentation We report the cases of 7 patients with medullary Unidentified Bright Objects in Neurofibromatosis type 1 that we have followed for up to 9 years in our Regional Referral Center for Neurofibromatosis. In all of our patients, these lesions were completely asymptomatic and reported on Magnetic Resonance exams the patients underwent for other clinical indications. Conclusions The aim of our work is to increase awareness of the possibility of medullary Unidentified Bright Objects in Neurofibromatosis type 1 patients, which can simulate neoplastic lesions, suggesting a more conservative approach in these cases.

An original maxillofacial fracture detection system (MFDS), based on convolutional neural networks and transfer learning, is proposed to detect traumatic fractures in patients. A convolutional neural network pre-trained on non-medical images was re-trained and fine-tuned using computed tomography (CT) scans to produce a model for the classification of future CTs as either “fracture” or “noFracture”. The model was trained on a total of 148 CTs (120 patients labeled with “fracture” and 28 patients labeled with “noFracture”). The validation dataset, used for statistical analysis, was characterized by 30 patients (5 with “noFracture” and 25 with “fracture”). An additional 30 CT scans, comprising 25 “fracture” and 5 “noFracture” images, were used as the test dataset for final testing. Tests were carried out both by considering the single slices and by grouping the slices for patients. A patient was categorized as fractured if two consecutive slices were classified with a fracture probability higher than 0.99. The patients’ results show that the model accuracy in classifying the maxillofacial fractures is 80%. Even if the MFDS model cannot replace the radiologist’s work, it can provide valuable assistive support, reducing the risk of human error, preventing patient harm by minimizing diagnostic delays, and reducing the incongruous burden of hospitalization.

Background: Following the EuroAIM initiative to assess the quality of medical imaging guidelines by using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument, we aimed to evaluate the quality of the current imaging guidelines in patients with gliomas.Methods: A literature search was conducted to identify eligible imaging guidelines considered in the management of adult patients with gliomas. The selected guidelines were evaluated using the AGREE II instrument by four independent appraisers. The agreement among the four appraisers was estimated using the intraclass correlation coefficient (ICC) analysis.Results: Seven guidelines were selected for the appraisal. Six out of the seven guidelines showed an average level of quality with only one showing a low quality. The highest scores were found in Domain 1 “Scope and purpose” (mean score = 81.2%) and Domain 4 “Clarity of presentation” (mean score = 77.6%). The remaining domains showed a low level of quality and, in particular, Domain 5 “Applicability” was the most critical with a mean score of 41.7%, mainly related to a minor attention to barriers and facilitators as well as costs and resources implications of applying the guidelines. The ICC analysis showed a very good agreement among the four appraisers with ICC values ranging from 0.907 to 0.993.Conclusions: The available guidelines on glioma imaging emerged as of average quality according to the AGREE II tool analysis. Based on these results, further efforts should be made in order to involve different professional bodies and stakeholders and increase patient and public involvement in any future guideline drafting as well as to improve the applicability of these guidelines into the clinical practice.

The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.

The assessment of coronary flow reserve (CFR) may be useful for the functional evaluation of coronary artery disease (CAD). Invasive techniques, such as intracoronary Doppler ultrasound and pressure-derived method, directly assess CFR velocity and fractional flow reserve. Positron emission tomography (PET) has emerged as an accurate noninvasive technique to quantify CFR. Nevertheless, this approach has not been applied to routine studies because of its high cost and complexity. Recently, attempts to estimate CFR with single-photon emission computed tomography (SPECT) tracers have been made in order to obtain, with noninvasive methods, data for quantitative functional assessment of CAD. This review analyzes the relative merit and limitations of CFR measurements by cardiac imaging techniques and describes the potential clinical applications.