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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). [ABSTRACT FROM AUTHOR]

Background: This study investigates the functional brain connectivity in patients with anterior knee pain (AKP). While biomechanical models are frequently employed to investigate AKP, it is important to recognize that pain can manifest even in the absence of structural abnormalities. Leveraging the capabilities of functional magnetic resonance imaging (fMRI), this research aims to investigate the brain mechanisms present in AKP patients. Methods: Forty-five female subjects (24 AKP patients, 21 controls) underwent resting-state fMRI and T1-weighted structural MRI. Functional brain connectivity patterns were analyzed, focusing on pain network areas, and the influence of catastrophizing thoughts was evaluated. Results: Comparing patients and controls, several findings emerged. First, patients with AKP exhibited increased correlation between the right supplementary motor area and cerebellum I, as well as decreased correlation between the right insula and the left rostral prefrontal cortex and superior frontal gyrus. Second, in AKP patients with catastrophizing thoughts, there was increased correlation of the left lateral parietal cortex with two regions of the right cerebellum (II and VII) and the right pallidum, and decreased correlation between the left medial frontal gyrus and the right thalamus. Furthermore, the correlation between these regions showed promising results for discriminating AKP patients from controls, achieving a cross-validation accuracy of 80.5%. Conclusions: Resting-state fMRI revealed correlation differences in AKP patients compared to controls and based on catastrophizing thoughts levels. These findings shed light on neural correlates of chronic pain in AKP, suggesting that functional brain connectivity alterations may be linked to pain experience in this population. Relevance statement: Etiopathogenesis of pain in anterior knee pain patients might not be limited to the knee, but also to underlying alterations in the central nervous system: cortical changes might lead a perpetuation of pain. Key points: • Anterior knee pain patients exhibit distinct functional brain connectivity compared to controls, and among catastrophizing subgroups. • Resting-state fMRI reveals potential for discriminating anterior knee pain patients with 80.5% accuracy. • Functional brain connectivity differences improve understanding of pain pathogenesis and objective anterior knee pain identification. [ABSTRACT FROM AUTHOR]

Objective: Automatic MR imaging segmentation of the prostate provides relevant clinical benefits for prostate cancer evaluation such as calculation of automated PSA density and other critical imaging biomarkers. Further, automated T2-weighted image segmentation of central-transition zone (CZ-TZ), peripheral zone (PZ), and seminal vesicle (SV) can help to evaluate clinically significant cancer following the PI-RADS v2.1 guidelines. Therefore, the main objective of this work was to develop a robust and reproducible CNN-based automatic prostate multi-regional segmentation model using an intercontinental cohort of prostate MRI. Methods: A heterogeneous database of 243 T2-weighted prostate studies from 7 countries and 10 machines of 3 different vendors, with the CZ-TZ, PZ, and SV regions manually delineated by two experienced radiologists (ground truth), was used to train (n = 123) and test (n = 120) a U-Net-based model with deep supervision using a cyclical learning rate. The performance of the model was evaluated by means of dice similarity coefficient (DSC), among others. Segmentation results with a DSC above 0.7 were considered accurate. Results: The proposed method obtained a DSC of 0.88 ± 0.01, 0.85 ± 0.02, 0.72 ± 0.02, and 0.72 ± 0.02 for the prostate gland, CZ-TZ, PZ, and SV respectively in the 120 studies of the test set when comparing the predicted segmentations with the ground truth. No statistically significant differences were found in the results obtained between manufacturers or continents. Conclusion: Prostate multi-regional T2-weighted MR images automatic segmentation can be accurately achieved by U-Net like CNN, generalizable in a highly variable clinical environment with different equipment, acquisition configurations, and population. Key Points: • Deep learning techniques allows the accurate segmentation of the prostate in three different regions on MR T2w images. • Multi-centric database proved the generalization of the CNN model on different institutions across different continents. • CNN models can be used to aid on the diagnosis and follow-up of patients with prostate cancer. [ABSTRACT FROM AUTHOR]

Background: Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19). Objectives: To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity. Methods: The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected. Results: A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user. Conclusion: We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans. [ABSTRACT FROM AUTHOR]

Simple Summary: Tumor segmentation is a key step in oncologic imaging processing. We have recently developed a model to detect and segment neuroblastic tumors on MR images based on deep learning architecture nnU-Net. In this work, we performed an independent validation of the automatic segmentation tool with a large heterogeneous dataset. We reviewed the automatic segmentations and manually edited them when necessary. We were able to show that the automatic network was able to locate and segment the primary tumor on the T2 weighted images in the majority of cases, with an extremely high agreement between the automatic tool and the manually edited masks. The time needed for manual adjustment was very low. Objectives. To externally validate and assess the accuracy of a previously trained fully automatic nnU-Net CNN algorithm to identify and segment primary neuroblastoma tumors in MR images in a large children cohort. Methods. An international multicenter, multivendor imaging repository of patients with neuroblastic tumors was used to validate the performance of a trained Machine Learning (ML) tool to identify and delineate primary neuroblastoma tumors. The dataset was heterogeneous and completely independent from the one used to train and tune the model, consisting of 300 children with neuroblastic tumors having 535 MR T2-weighted sequences (486 sequences at diagnosis and 49 after finalization of the first phase of chemotherapy). The automatic segmentation algorithm was based on a nnU-Net architecture developed within the PRIMAGE project. For comparison, the segmentation masks were manually edited by an expert radiologist, and the time for the manual editing was recorded. Different overlaps and spatial metrics were calculated to compare both masks. Results. The median Dice Similarity Coefficient (DSC) was high 0.997; 0.944–1.000 (median; Q1–Q3). In 18 MR sequences (6%), the net was not able neither to identify nor segment the tumor. No differences were found regarding the MR magnetic field, type of T2 sequence, or tumor location. No significant differences in the performance of the net were found in patients with an MR performed after chemotherapy. The time for visual inspection of the generated masks was 7.9 ± 7.5 (mean ± Standard Deviation (SD)) seconds. Those cases where manual editing was needed (136 masks) required 124 ± 120 s. Conclusions. The automatic CNN was able to locate and segment the primary tumor on the T2-weighted images in 94% of cases. There was an extremely high agreement between the automatic tool and the manually edited masks. This is the first study to validate an automatic segmentation model for neuroblastic tumor identification and segmentation with body MR images. The semi-automatic approach with minor manual editing of the deep learning segmentation increases the radiologist's confidence in the solution with a minor workload for the radiologist. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) with medical images to solve clinical problems is becoming increasingly common, and the development of new AI solutions is leading to more studies and publications using this computational technology. As a novel research area, the use of common standards to aid AI developers and reviewers as quality control criteria will improve the peer review process. Although some guidelines do exist, their heterogeneity and extension advocate that more explicit and simple schemes should be applied on the publication practice. Based on a review of existing AI guidelines, a proposal which collects, unifies, and simplifies the most relevant criteria was developed. The MAIC-10 (Must AI Criteria-10) checklist with 10 items was implemented as a guide to design studies and evaluate publications related to AI in the field of medical imaging. Articles published in Insights into Imaging in 2021 were selected to calculate their corresponding MAIC-10 quality score. The mean score was found to be 5.6 ± 1.6, with critical items present in most articles, such as "Clinical need", "Data annotation", "Robustness", and "Transparency" present in more than 80% of papers, while improvements in other areas were identified. MAIC-10 was also observed to achieve the highest intra-observer reproducibility when compared to other existing checklists, with an overall reduction in terms of checklist length and complexity. In summary, MAIC-10 represents a short and simple quality assessment tool which is objective, robust and widely applicable to AI studies in medical imaging. Key points: AI solutions have become an essential clinical tool in medical imaging. Standardised criteria are necessary to ensure quality of AI studies. Established criteria are often incomplete, too exhaustive, or not broadly applicable. A concise and reproducible quantitative checklist will help to ensure a minimum of acceptance. [ABSTRACT FROM AUTHOR]

Background: Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. Methods: A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2–4. Subsequent rounds were informed by responses of previous rounds. Results/conclusions: Items with ≥ 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60–74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with ≤ 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) imaging is the most sensitive clinical tool in the diagnosis and monitoring of multiple sclerosis (MS) alterations. Spinal cord evaluation has gained interest in this clinical scenario in recent years, but, unlike the brain, there is a more limited choice of algorithms to assist spinal cord segmentation. Our goal was to investigate and develop an automatic MR cervical cord segmentation method, enabling automated and seamless spinal cord atrophy assessment and setting the stage for the development of an aggregated algorithm for the extraction of lesion-related imaging biomarkers. The algorithm was developed using a real-world MR imaging dataset of 121 MS patients (96 cases used as a training dataset and 25 cases as a validation dataset). Transversal, 3D T1-weighted gradient echo MR images (TE/TR/FA = 1.7–2.7 ms/5.6–8.2 ms/12°) were acquired in a 3 T system (Signa HD, GEHC) as standard of care in our clinical practice. Experienced radiologists supervised the manual labelling, which was considered the ground-truth. The 2D convolutional neural network consisted of a hybrid residual attention-aware segmentation method trained to delineate the cervical spinal cord. The training was conducted using a focal loss function, based on the Tversky index to address label imbalance, and an automatic optimal learning rate finder. Our automated model provided an accurate segmentation, achieving a validation DICE coefficient of 0.904 ± 0.101 compared with the manual delineation. An automatic method for cervical spinal cord segmentation on T1-weighted MR images was successfully implemented. It will have direct implications serving as the first step for accelerating the process for MS staging and follow-up through imaging biomarkers. [ABSTRACT FROM AUTHOR]

The benefits of structured reporting (SR) in radiology are well-known and have been widely described. However, there are limitations that must be overcome. Radiologists may be reluctant to change the conventional way of reporting. Error rates could potentially increase if SR is used improperly. Interruption of the visual search pattern by keeping the eyes focused on the report rather than the images may increase reporting time. Templates that include unnecessary or irrelevant information may undermine the consistency of the report. Last, the lack of support for multiple languages may hamper the adaptation of the report to the target audience. This work aims to mitigate these limitations with a web-based structured reporting system based on templates. By including field validators and logical rules, the system avoids reporting mistakes and allows to automatically calculate values and radiological qualitative scores. The system can manage quantitative information from imaging biomarkers, combining this with qualitative radiological information usually present in the structured report. It manages SR templates as plugins (IHE MRRT compliant and compatible with RSNA's Radreport templates), ensures a seamless integration with PACS/RIS systems, and adapts the report to the target audience by means of natural language extracts generated in multiple languages. We describe a use case of SR template for prostate cancer including PI-RADS 2.1 scoring system and imaging biomarkers. For the time being, the system comprises 24 SR templates and provides service in 37 hospitals and healthcare institutions, endorsing the success of this contribution to mitigate some of the limitations of the SR. [ABSTRACT FROM AUTHOR]

Background: Indirect 1H-magnetic resonance (MR) imaging of 17O-labelled water allows imaging in vivo dynamic changes in water compartmentalisation. Our aim was to describe the feasibility of indirect 1H-MR methods to evaluate the effect of H217O on the MR relaxation rates by using conventional a 3-T equipment and voxel-wise relaxation rates. Methods: MR images were used to calculate the R1, R2, and R2* relaxation rates in phantoms (19 vials with different H217O concentrations, ranging from 0.039 to 5.5%). Afterwards, an experimental animal pilot study (8 rats) was designed to evaluate the in vivo relative R2 brain dynamic changes related to the intravenous administration of 17O-labelled water in rats. Results: There were no significant changes on the R1 and R2* values from phantoms. The R2 obtained with the turbo spin-echo T2-weighted sequence with 20-ms echo time interval had the higher statistical difference (0.67 s−1, interquartile range 0.34, p < 0.001) and Spearman correlation (rho 0.79). The R2 increase was adjusted to a linear fit between 0.25 and 5.5%, represented with equation R2 = 0.405 concentration + 0.3215. The highest significant differences were obtained for the higher concentrations (3.1–5.5%). The rat brain MR experiment showed a mean 10% change in the R2 value after the H217O injection with progressive normalisation. Conclusions: Indirect 1H-MR imaging method is able to measure H217O concentration by using R2 values and conventional 3-T MR equipment. Normalised R2 relative dynamic changes after the intravenous injection of a H217O saline solution provide a unique opportunity to map water pathophysiology in vivo, opening the analysis of aquaporins status and modifications by disease at clinically available 3-T proton MR scanners. [ABSTRACT FROM AUTHOR]

Objective: To automate the segmentation of whole liver parenchyma on multi-echo chemical shift encoded (MECSE) MR examinations using convolutional neural networks (CNNs) to seamlessly quantify precise organ-related imaging biomarkers such as the fat fraction and iron load. Methods: A retrospective multicenter collection of 183 MECSE liver MR examinations was conducted. An encoder-decoder CNN was trained (107 studies) following a 5-fold cross-validation strategy to improve the model performance and ensure lack of overfitting. Proton density fat fraction (PDFF) and R2* were quantified on both manual and CNN segmentation masks. Different metrics were used to evaluate the CNN performance over both unseen internal (46 studies) and external (29 studies) validation datasets to analyze reproducibility. Results: The internal test showed excellent results for the automatic segmentation with a dice coefficient (DC) of 0.93 ± 0.03 and high correlation between the quantification done with the predicted mask and the manual segmentation (rPDFF = 1 and rR2* = 1; p values < 0.001). The external validation was also excellent with a different vendor but the same magnetic field strength, proving the generalization of the model to other manufacturers with DC of 0.94 ± 0.02. Results were lower for the 1.5-T MR same vendor scanner with DC of 0.87 ± 0.06. Both external validations showed high correlation in the quantification (rPDFF = 1 and rR2* = 1; p values < 0.001). In both internal and external validation datasets, the relative error for the PDFF and R2* quantification was below 4% and 1% respectively. Conclusion: Liver parenchyma can be accurately segmented with CNN in a vendor-neutral virtual approach, allowing to obtain reproducible automatic whole organ virtual biopsies. Key points: • Whole liver parenchyma can be automatically segmented using convolutional neural networks. • Deep learning allows the creation of automatic pipelines for the precise quantification of liver-related imaging biomarkers such as PDFF and R2*. • MR "virtual biopsy" can become a fast and automatic procedure for the assessment of chronic diffuse liver diseases in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Estimation of the depth of myometrial invasion (MI) in endometrial cancer is pivotal in the preoperatively staging. Magnetic resonance (MR) reports suffer from human subjectivity. Multiparametric MR imaging radiomics and parameters may improve the diagnostic accuracy. Purpose: To discriminate between patients with MI ≥ 50% using a machine learning‐based model combining texture features and descriptors from preoperatively MR images. Study Type: Retrospective. Population: One hundred forty‐three women with endometrial cancer were included. The series was split into training (n = 107, 46 with MI ≥ 50%) and test (n = 36, 16 with MI ≥ 50%) cohorts. Field Strength/Sequences: Fast spin echo T2‐weighted (T2W), diffusion‐weighted (DW), and T1‐weighted gradient echo dynamic contrast‐enhanced (DCE) sequences were obtained at 1.5 or 3 T magnets. Assessment: Tumors were manually segmented slice‐by‐slice. Texture metrics were calculated from T2W and ADC map images. Also, the apparent diffusion coefficient (ADC), wash‐in slope, wash‐out slope, initial area under the curve at 60 sec and at 90 sec, initial slope, time to peak and peak amplitude maps from DCE sequences were obtained as parameters. MR diagnostic models using single‐sequence features and a combination of features and parameters from the three sequences were built to estimate MI using Adaboost methods. The pathological depth of MI was used as gold standard. Statistical Test: Area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, precision and recall were computed to assess the Adaboost models performance. Results: The diagnostic model based on the features and parameters combination showed the best performance to depict patient with MI ≥ 50% in the test cohort (accuracy = 86.1% and AUROC = 87.1%). The rest of diagnostic models showed a worse accuracy (accuracy = 41.67%–63.89% and AUROC = 41.43%–63.13%). Data Conclusion: The model combining the texture features from T2W and ADC map images with the semi‐quantitative parameters from DW and DCE series allow the preoperative estimation of myometrial invasion. Evidence Level: 4 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory. [ABSTRACT FROM AUTHOR]

Summary: Background: Histological evaluation of metabolic‐associated fatty liver disease (MAFLD) biopsies is subjective, descriptive and with interobserver variability. Aims: To examine the relationship between different histological features (fibrosis, steatosis, inflammation and iron) measured with automated whole‐slide quantitative digital pathology and corresponding semiquantitative scoring systems, and the distribution of digital pathology measurements across Fatty Liver Inhibition of Progression (FLIP) algorithm and Steatosis, Activity and Fibrosis (SAF) scoring system Methods: We prospectively included 136 consecutive patients who underwent liver biopsy for MAFLD at three Spanish centres (January 2017‐January 2020). Biopsies were scored by two blinded pathologists according to the Non‐alcoholic Steatohepatitis (NASH) Clinical Research Network system for fibrosis staging, the FLIP/SAF classification for steatosis and inflammation grading and Deugnier score for iron grading. Proportionate areas of collagen, fat, inflammatory cells and iron deposits were measured with computer‐assisted digital image analysis. A test‐retest experiment was performed for precision repeatability evaluation. Results: Digital pathology showed strong correlation with fibrosis (r = 0.79; P < 0.001), steatosis (r = 0.85; P < 0.001) and iron (r = 0.70; P < 0.001). Performance was lower when assessing the degree of inflammation (r = 0.35; P < 0.001). NASH cases had a higher proportion of collagen and fat compared to non‐NASH cases (P < 0.005), whereas inflammation and iron quantification did not show significant differences between categories. Repeatability evaluation showed that all the coefficients of variation were ≤1.1% and all intraclass correlation coefficient values were ≥0.99, except those of collagen. Conclusion: Digital pathology allows an automated, precise, objective and quantitative assessment of MAFLD histological features. Digital analysis measurements show good concordance with pathologists´ scores. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) identification and grading of subjects with liver fibrosis and inflammation represents a clinical challenge. MR elastography plays a well-defined role in fibrosis estimation, but its use is not widely available in clinical settings. Given that liver MR is becoming the reference standard for fat and iron quantitation, there is a need to clarify whether there is any role for MR imaging in the concomitant evaluation of fibrosis and inflammation in this setting. This review summarizes the diagnostic estimations of different MR imaging parameters obtained from conventional non-contrast-enhanced multiple b values diffusion-weighted acquisitions, variable flip angles T1 relaxation maps and STIR images. Although some derived parameters have shown a significant correlation to histological scores, a small magnitude of effect with wide overlap across severity grades is the rule. Contrary to fat and iron quantification, the low precision and reproducibility of MR imaging metrics limits its clinical relevance in fibrosis and inflammation assessment. In a sequential clinical approach combining different methodologies, MR imaging has no applicability for ruling-out and low accuracy for ruling-in advanced fibrosis. Thereby, MR elastography remains as the only image method with high diagnostic accuracy for the detection of advanced fibrosis. Until date, inflammation remains in a gray zone where biopsy cannot be replaced, and further investigations are needed. The present review offers an in-depth discuss of the MR imaging diagnostic performance for the evaluation of liver fibrosis and inflammation, highlighting the need for scientific improvements. [ABSTRACT FROM AUTHOR]

Medical imaging capable of generating imaging biomarkers, specifically radiology and nuclear medicine image acquisition and analysis processes, differs from frequently used comparators like blood or urine biomarkers. This difference arises from the sample acquisition methodology. While different analysis methodologies and equipment provide slightly different results in any analytical domain, unlike blood or urine analysis where the samples are obtained by simple extraction or excretion, in radiology the acquisition of the sample is heterogeneous by design, since complex equipment from different vendors is used. Therefore, with this additional degree of freedom in medical imaging, there is still risk of persistent heterogeneity of image quality through time, due to different technological implementations across vendors and protocols used in different centres. Quantitative imaging biomarkers have yet to demonstrate an impact on clinical practice due to this lack of comprehensive standardisation in terms of technical aspects of image acquisition, analysis algorithms, processes and clinical validation. The aim is establishing a standard methodology based on metrology for the validation of image acquisition and analysis methods used in the extraction of biomarkers and radiomics data. The appropriate implementation of the guidelines herein proposed by radiology departments, research institutes and industry will allow for a significant reduction in inter-vendor & inter-centre variability in imaging biomarkers and determine the measurement error obtained, enabling them to be used in imaging-based criteria for diagnosis, prognosis or treatment response, ultimately improving clinical workflows and patient care. The validation of developed analytical methods must be based on a technical performance validation and clinical validation. [ABSTRACT FROM AUTHOR]

Purpose: Development of a fully automatic algorithm for the automatic localization and identification of vertebral bodies in computed tomography (CT). Materials and methods: This algorithm was developed using a dataset based on real-world data of 232 thoraco-abdominopelvic CT scans retrospectively collected. In order to achieve an accurate solution, a two-stage automated method was developed: decision forests for a rough prediction of vertebral bodies position, and morphological image processing techniques to refine the previous detection by locating the position of the spinal canal. Results: The mean distance error between the predicted vertebrae centroid position and truth was 13.7 mm. The identification rate was 79.6% on the thoracic region and of 74.8% on the lumbar segment. Conclusion: The algorithm provides a new method to detect and identify vertebral bodies from arbitrary field-of-view body CT scans. [ABSTRACT FROM AUTHOR]

Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions. [ABSTRACT FROM AUTHOR]

Aims: The aim of this study was to compare magnetic resonance imaging (MRI) parameters in patients with mild incontinence after radical prostatectomy, who had undergone treatment with a suburethral sling. The objective was to compare patients who had been successfully treated with unsuccessful patients. Methods: This observational cohort study at a single institution evaluated consecutive patients treated with an AdVance XP sling. MRI was performed using a 1.5 Tesla system. Preoperative urodynamic assessment and flexible cystoscopy were performed. The qualitative analysis was based on sling indentation (complete vs incomplete). The quantitative analysis comprised the following three parameters: the sling‐pubis distance, the thickness of the proximal urethral bulb, and the sling backward distance (SBD), defined as the distance between the prolongation of a line through the major axis of the pubis (the line‐segment joining the vertices of the pubis) and the sling indentation. The primary outcome was pad count at 3 months; cure as zero pads. A logistic univariate regression model was employed to assess the potential predictors of successful outcomes. An adjusted multivariate logistic regression model using the significant univariate factors was developed. Results: Of the 83 patients enrolled, the univariate analysis revealed a relationship between failure and adverse urodynamics and between success and sling indentation, thickness of the proximal bulb and SBD. Only the association with SBD persisted in the multivariate analysis. Conclusions: MRI revealed a strong relationship between proper positioning of the sling (small SBD) and continence outcome. These data have important implications for a second surgery following initial sling failure. [ABSTRACT FROM AUTHOR]

Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions. [ABSTRACT FROM AUTHOR]

This paper proposes a discrete particle model based on the random-walk theory for simulating cement infiltration within open-cell structures to prevent osteoporotic proximal femur fractures. Model parameters consider the cement viscosity (high and low) and the desired direction of injection (vertical and diagonal). In vitro and in silico characterizations of augmented open-cell structures validated the computational model and quantified the improved mechanical properties (Young’s modulus) of the augmented specimens. The cement injection pattern was successfully predicted in all the simulated cases. All the augmented specimens exhibited enhanced mechanical properties computationally and experimentally (maximum improvements of 237.95 ± 12.91% and 246.85 ± 35.57%, respectively). The open-cell structures with high porosity fraction showed a considerable increase in mechanical properties. Cement augmentation in low porosity fraction specimens resulted in a lesser increase in mechanical properties. The results suggest that the proposed discrete particle model is adequate for use as a femoroplasty planning framework. [ABSTRACT FROM AUTHOR]

Several image processing algorithms have emerged to cover unmet clinical needs but their application to radiological routine with a clear clinical impact is still not straightforward. Moving from local to big infrastructures, such as Medical Imaging Biobanks (millions of studies), or even more, Federations of Medical Imaging Biobanks (in some cases totaling to hundreds of millions of studies) require the integration of automated pipelines for fast analysis of pooled data to extract clinically relevant conclusions, not uniquely linked to medical imaging, but in combination to other information such as genetic profiling. A general strategy for the development of imaging biomarkers and their integration in the cloud for the quantitative management and exploitation in large databases is herein presented. The proposed platform has been successfully launched and is being validated nowadays among the early adopters' community of radiologists, clinicians, and medical imaging researchers. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the diagnostic performances of 3 Tesla multi-echo chemical shift-encoded gradient echo magnetic resonance (MECSE-MR) imaging to simultaneously quantify liver steatosis and iron overload in a wide spectrum of diffuse liver diseases having biopsy as reference standard. Methods: MECSE-MR-acquired images were used to calculate fat fraction and iron content in a single breath-hold in 109 adult patients. Proton density fat fraction (PDFF) was prospectively estimated using complex-based data reconstruction with multipeak fat modeling. Water R2* was used to estimate iron content. Biopsy was obtained in all cases, grading liver steatosis, siderosis, inflammation, and fibrosis. Differences in PDFF and R2* values across histopathological grades were analyzed, and ROC curves analyses evaluated the MR diagnostic performance. Results: Calculated fat fraction measurements showed significant differences ( p < 0.001) among steatosis grades, being unaffected by the presence of inflammation or fibrosis ( p ≥ 0.05). A strong correlation was found between fat fraction and steatosis grade ( R = 0.718, p < 0.001). Iron deposits did not affect fat fraction quantitation ( p ≥ 0.05), except in cases with severe iron overload (grade 4). A strong positive correlation was also observed between R2* measurements and iron grades ( R = 0.704, p < 0.001). Calculated R2* values were not different across grades of steatosis, inflammation, and fibrosis ( p ≥ 0.05). Conclusion: A MECSE-MR sequence simultaneously quantifies liver steatosis and siderosis, regardless coexisting liver inflammation or fibrosis, with high accuracy in a wide spectrum of diffuse liver disorders. This sequence can be acquired within a single breath-hold and can be implemented in the routine MR evaluation of the liver. [ABSTRACT FROM AUTHOR]

Purpose: Despite improved acute treatment and new tools to facilitate recovery, most patients have motor deficits after stroke, often causing disability. However, motor impairment varies considerably among patients, and recovery in the acute/subacute phase is difficult to predict using clinical measures alone, particularly in severely impaired patients. Accurate early prediction of recovery would help rationalize rehabilitation goals and improve the design of trials testing strategies to facilitate recovery. Methods: We review the role of diffusion tensor imaging (DTI) in predicting motor recovery after stroke, in monitoring treatment response, and in evaluating white matter remodeling. We critically appraise DTI studies and discuss their limitations, and we explore directions for future study. Results: Growing evidence suggests that combining clinical scores with information about corticospinal tract (CST) integrity can improve predictions about motor outcome. The extent of CST damage on DTI and/or the overlap between the CST and a lesion are key prognostic factor that determines motor performance and outcome. Three main strategies to quantify stroke-related CST damage have been proposed: (i) measuring FA distal to the stroke area, (ii) measuring the number of fibers that go through the stroke with tractography, and (iii) measuring the overlap between the stroke and a CST map derived from healthy age- and gender-matched controls. Conclusion: Recovery of motor function probably involves remodeling of the CST proper and/or a greater reliance on alternative motor tracts through spontaneous and treatment-induced plasticity. DTI-metrics represent promising clinical biomarkers to predict motor recovery and to monitor and predict the response to neurorehabilitative interventions. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study was to evaluate the role of intravoxel incoherent motion (IVIM) diffusion model for the assessment of liver fibrosis and inflammation in diffuse liver disorders, also considering the presence of liver steatosis and iron deposits. Methods: Seventy-four patients were included, with liver biopsy and a 3 Tesla abdominal magnetic resonance imaging examination, with an IVIM diffusion-weighted sequence (single-shot spin-echo echo-planar sequence, with gradient reversal fat suppression; 6 b-values: 0, 50, 200, 400, 600, and 800 s/mm). Histological evaluation comprised the Ishak modified scale, for grading inflammation and fibrosis, plus steatosis and iron loading classification. The liver apparent diffusion coefficient (ADC) and IVIM parameters ( D, D*, f) were calculated from the IVIM images. The relationship between IVIM parameters and histopathological scores were evaluated by ANOVA and Spearman correlation tests. A test-retest experiment assessed reproducibility and repeatability in 10 healthy volunteers and 10 randomly selected patient studies. Results: ADC and f values were lower with higher fibrosis stages ( p = 0.009, p = 0.006, respectively) and also with higher necro-inflammatory activity grades ( p = 0.02, p = 0.017, respectively). Considered together, only fibrosis presented a significant effect on ADC and f measurements ( p < 0.05), whereas inflammation had no significant effect ( p > 0.05). A mild correlation was found between ADC and f with fibrosis ( R = −0.32 and R = −0.38; p < 0.05) and inflammation ( R = −0.31 and R = −0.32, p < 0.05; respectively). The AUROC for ADC and f measurements with the different dichotomizations between fibrosis or inflammation grades were only fair (0.670 to 0.749, p < 0.05). Neither D nor D* values were significantly different between liver fibrosis or inflammation grades. D measurements were significantly different across histologic grades of steatosis ( p < 0.001) and iron overload ( p < 0.001), whereas f measurements showed significant differences across histologic steatosis grades ( p = 0.005). There was an excellent agreement between the different readers for ADC, f, and D. Conclusions: Although fibrosis presented a significant effect on ADC and f, IVIM measurements are not accurate enough to stage liver fibrosis or necro-inflammatory activity in diffuse liver diseases. D values were influenced by steatosis and iron overload. [ABSTRACT FROM AUTHOR]

Normal and degenerated cartilages have different magnetic resonance (MR) capillary permeability (K(trans)) and interstitial interchangeable volume (v(e)). Our hypothesis was that glucosamine sulfate treatment modifies these neovascularity abnormalities in osteoarthritis. Sixteen patients with patella degeneration, randomly distributed into glucosamine or control groups, underwent two 1.5-Tesla dynamic contrast-enhanced MR imaging studies (treatment initiation and after 6 months). The pain visual analog scale (VAS) and American Knee Society (AKS) score were used. A two-compartment pharmacokinetic model was used. Percentages of variations (postreatment-pretreatment/pretreatment) were compared (t-test for independent data). In the glucosamine group, pain and functional outcomes statistically improved (VAS: 7.3 +/- 1.1 to 3.6 +/- 1.3, p < 0.001; AKS: 18.6 +/- 6.9 to 42.9 +/- 2.7, p < 0.01). Glucosamine significantly increased K(trans) at 6 months (-54.4 +/- 21.2% vs 126.7 +/- 56.9%, p < 0.001, control vs glucosamine). In conclusion, glucosamine sulfate decreases pain while improving functional outcome in patients with cartilage degeneration. Glucosamine sulfate increases K(trans), allowing its proposal as a surrogate imaging biomarker after 6 months of treatment. [ABSTRACT FROM AUTHOR]

Objective: Intravoxel incoherent motion (IVIM) is an MRI technique with potential applications in measuring brain tumor perfusion, but its clinical impact remains to be determined. We assessed the usefulness of IVIM-metrics in predicting survival in newly diagnosed glioblastoma. Methods: Fifteen patients with glioblastoma underwent MRI including spin-echo echo-planar DWI using 13 b-values ranging from 0 to 1000 s/mm2. Parametric maps for diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were generated for contrast-enhancing regions (CER) and non-enhancing regions (NCER). Regions of interest were manually drawn in regions of maximum f and on the corresponding dynamic susceptibility contrast images. Prognostic factors were evaluated by Kaplan-Meier survival and Cox proportional hazards analyses. Results: We found that fCER and D*CER correlated with rCBFCER. The best cutoffs for 6-month survival were fCER>9.86% and D*CER>21.712 x10−3mm2/s (100% sensitivity, 71.4% specificity, 100% and 80% positive predictive values, and 80% and 100% negative predictive values; AUC:0.893 and 0.857, respectively). Treatment yielded the highest hazard ratio (5.484; 95% CI: 1.162–25.88; AUC: 0.723; P = 0.031); fCER combined with treatment predicted survival with 100% accuracy. Conclusions: The IVIM-metrics fCER and D*CER are promising biomarkers of 6-month survival in newly diagnosed glioblastoma. [ABSTRACT FROM AUTHOR]

Objective Rodent models have found that osteocalcin crosses the blood-brain barrier and regulates behaviour. No data are available on osteocalcin's effects on brain microstructure and cognitive performance in humans. We evaluated the association between serum osteocalcin concentrations and (i) brain microstructural changes on magnetic resonance imaging ( MRI) and (ii) neuropsychological performance. Design, Patients and Measurements We studied 24 consecutive obese subjects (13 women; age, 49·8 ± 8·1 years; body mass index [ BMI], 43·9 ± 4·54 kg/m2) and 20 healthy volunteers (10 women; age, 48·8 ± 9·5 years; BMI, 24·3 ± 3·54 kg/m2) in a cross-sectional study within the multicentre FLORINASH Project. FLAIR signal intensity and DTI-metrics (primary (λ1), secondary (λ2) and tertiary (λ3) eigenvalues; fractional anisotropy ( FA); and mean diffusivity) in the caudate, hypothalamus, thalamus and putamen, and in subcortical white matter were assessed. Cognitive performance evaluated by neuropsychological test battery. Results Lower osteocalcin concentrations were associated with BMI, higher λ1, λ2 and λ3 values at the caudate and lower FLAIR signal intensity at the caudate and putamen. Obese patients with lower osteocalcin concentrations had higher FA at putamen and thalamus. Lower osteocalcin concentrations were associated with higher Iowa Gambling Task ( IGT) scores. FLAIR signal intensity at the caudate <601·832 yielded 85·7% sensitivity, 64·3% specificity, 70·6% negative predictive value and 81·8% positive predictive value for IGT score. Lower osteocalcin was an independent predictor of worse cognitive performance on multivariate analysis ( F = 3·551, P = 0·01343; R2 = 0·103). Bayesian information criterion demonstrated that osteocalcin had the predominant role in predicting IGT score. Conclusions Lower serum osteocalcin concentrations are associated with brain microstructural changes and worse cognitive performance. [ABSTRACT FROM AUTHOR]

Introduction: The objective of the study was to determine whether tumor-associated neovascularization on high-resolution gadofosveset-enhanced magnetic resonance angiography (MRA) is a useful biomarker for predicting survival in patients with newly diagnosed glioblastomas. Methods: Before treatment, 35 patients (25 men; mean age, 64 ± 14 years) with glioblastoma underwent MRI including first-pass dynamic susceptibility contrast (DSC) perfusion and post-contrast T1WI sequences with gadobutrol (0.1 mmol/kg) and, 48 h later, high-resolution MRA with gadofosveset (0.03 mmol/kg). Volumes of interest for contrast-enhancing lesion (CEL), non-CEL, and contralateral normal-appearing white matter were obtained, and DSC perfusion and DWI parameters were evaluated. Prognostic factors were assessed by Kaplan-Meier survival and Cox proportional hazards model. Results: Eighteen (51.42 %) glioblastomas were hypervascular on high-resolution MRA. Hypervascular glioblastomas were associated with higher CEL volume and lower Karnofsky score. Median survival rates for patients with hypovascular and hypervascular glioblastomas treated with surgery, radiotherapy, and chemotherapy were 15 and 9.75 months, respectively ( P < 0.001). Tumor-associated neovascularization was the best predictor of survival at 5.25 months (AUC = 0.794, 81.2 % sensitivity, 77.8 % specificity, 76.5 % positive predictive value, 82.4 % negative predictive value) and yielded the highest hazard ratio ( P < 0.001). Conclusions: Tumor-associated neovascularization detected on high-resolution blood-pool-contrast-enhanced MRA of newly diagnosed glioblastoma seems to be a useful biomarker that correlates with worse survival. [ABSTRACT FROM AUTHOR]

The article highlights the Optimization of the management of hereditary hemochromatosis by magnetic resonance imaging R2 (MRI-R2) to predict and monitor patient's body iron content. Topics discussed include risk of severe tissue damage due to iron toxicity and mortality can be effectively reduced by regular venesection; diagnosis of the disease with the help of several medical techniques; and Challenges faced by the patient in the treatment.

Background: To introduce a segmentation method to calculate an automatic arterial input function (AIF) based on principal component analysis (PCA) of dynamic contrast enhanced MR (DCE-MR) imaging and compare it with individual manually selected and population-averaged AIFs using calculated pharmacokinetic parameters. Methods: The study included 65 individuals with prostate examinations (27 tumors and 38 controls). Manual AIFs were individually extracted and also averaged to obtain a population AIF. Automatic AIFs were individually obtained by applying PCA to volumetric DCE-MR imaging data and finding the highest correlation of the PCs with a reference AIF. Variability was assessed using coefficients of variation and repeated measures tests. The different AIFs were used as inputs to the pharmacokinetic model and correlation coefficients, Bland-Altman plots and analysis of variance tests were obtained to compare the results. Results: Automatic PCA-based AIFs were successfully extracted in all cases. The manual and PCA-based AIFs showed good correlation (r between pharmacokinetic parameters ranging from 0.74 to 0.95), with differences below the manual individual variability (RMSCV up to 27.3%). The population-averaged AIF showed larger differences (r from 0.30 to 0.61). Conclusion: The automatic PCA-based approach minimizes the variability associated to obtaining individual volume-based AIFs in DCE-MR studies of the prostate. [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published: https://doi.org/10.1007/s00330-021-07721-3 [ABSTRACT FROM AUTHOR]