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[EN] PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours., Horizon 2020 project (RIA, topic SC1-DTH-07-2018)

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. 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. 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. Liver parenchyma can be accurately segmented with CNN in a vendor-neutral virtual approach, allowing to obtain reproducible automatic whole organ virtual biopsies. • 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.

The role of Radiology in SARS-CoV-2 infection has been relevant but marginal. Even though it offers important information on pulmonary and vascular involvement in these patients, its contributions should not be considered diagnostic for COVID-19. Although there are characteristic patterns of involvement, these same radiological findings can be observed in other entities. Mainly it should be considered that Radiology contributes to the disease severity gradation, extension, complications, follow-up and in the prognostic evaluation of these patients. In this review, these contributions will be discussed together with the role of imaging in clinical trials and in the evaluation of new epidemic situations through the creation of Medical Imaging Biobanks capable of using the estimating potential of Artificial Intelligence.

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.

Aim To compare pancreatic and hepatic steatosis quantified by proton density fat fraction (PDFF) on magnetic resonance imaging (MRI) in patients with chronic liver disease. Material and methods This cross-sectional study included 46 adult patients who underwent liver biopsy for chronic viral hepatitis (n=19) or other chronic non-alcoholic liver diseases (NALD) (n=27). Liver biopsy was used as the gold standard for diagnosing and grading hepatic steatosis. All patients underwent clinical evaluation and MRI with a multi-echo chemical shift-encoded (MECSE) gradient-echo sequence for liver and pancreas PDFF quantification. We used Spearman's correlation coefficient to determine the degree of association between hepatic PDFF and steatosis grade, and between pancreatic PDFF and steatosis grade and hepatic PDFF. To compare the chronic viral hepatitis group and the NALD group, we used t-tests for continuous or ordinal variables and chi-square tests for categorical variables. Results Hepatic PDFF measurements correlated with steatosis grades (RS=0.875, p Conclusion Pancreatic fat deposition correlates with hepatic steatosis in patients with chronic NALD, but not in those with chronic viral hepatitis.

The association of non-alcoholic fatty liver disease (NAFLD) with insulin resistance (IR) is well established, yet little is known of their possible relationship with intrahepatic iron and serum tumour necrosis factor (TNF)-α concentrations in adults without diabetes. Thus, this study looked at the relationship of intrahepatic iron and serum TNF-α with intrahepatic triglycerides and IR in non-diabetic adults.In this cross-sectional study of 104 healthy non-diabetic Caucasians, a quantitative magnetic resonance (MR) imaging T2 gradient-echo technique was used to measure hepatic iron, with 1H-MR spectroscopy used to measure hepatic triglycerides. HOMA-IR was calculated to determine IR.The prevalence of hepatic iron overload (HIOL) was 26.6% in individuals with NAFLD vs. 0% in those without. IR was present in 87.5% of subjects with both NAFLD and HIOL, in 45.4% of those with NAFLD but not HIOL, and in 4.5% of those with neither. HOMA-IR was positively correlated with hepatic triglycerides (r = 0.56, P 0.001) and hepatic iron (r = 0.52, P 0.001), whereas serum TNF-α concentrations correlated with intrahepatic triglyceride levels (r = 0.28, P 0.04), but not with intrahepatic iron. Hepatic triglycerides, serum TNF-α and age were the only significant determinants of IR in regression analyses.IR is closely associated with intrahepatic triglycerides and, to a lesser extent, intrahepatic iron, with some interplay between them. High serum TNF-α concentrations may contribute to the association between NAFLD and IR, while increased hepatic triglycerides appear to be a determinant of the development of HIOL in non-diabetic subjects without haemochromatosis.

Development of a fully automatic algorithm for the automatic localization and identification of vertebral bodies in computed tomography (CT). 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. 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. The algorithm provides a new method to detect and identify vertebral bodies from arbitrary field-of-view body CT scans.

Abstract 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.

[EN] 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, This study received funding from the Global Investigator Initiated Research Committee (GIIRC) research program by Bracco S.p.A (2015/0724). The funders had no role in study design, data collection and analysis and preparation of the manuscript.

Unlike other medical imaging domains such as pathology and dermatology, radiology data is inherently in 3D, even 4D and n-dimensional (nD) depending on the modality used for images acquisition. More and more evidences exist on the advantages of studying volume through advanced visualization techniques rather than exploring 2D data alone in different clinical scenarios of diagnosis, treatment planning, and treatment response evaluation (response criteria).

Simple Summary There is growing interest in applying quantitative diffusion techniques to magnetic resonance imaging for cancer diagnosis and treatment. These measurements are used as a surrogate marker of tumor cellularity and aggressiveness, although there may be factors that introduce some bias to these approaches. Thus, we explored a novel methodology based on confidence habitats and voxel uncertainty to improve the power of the apparent diffusion coefficient to discriminate between benign and malignant neuroblastic tumor profiles in children. We were able to show this offered an improved sensitivity and negative predictive value relative to standard voxel-based methodologies. Background/Aim: In recent years, the apparent diffusion coefficient (ADC) has been used in many oncology applications as a surrogate marker of tumor cellularity and aggressiveness, although several factors may introduce bias when calculating this coefficient. The goal of this study was to develop a novel methodology (Fit-Cluster-Fit) based on confidence habitats that could be applied to quantitative diffusion-weighted magnetic resonance images (DWIs) to enhance the power of ADC values to discriminate between benign and malignant neuroblastic tumor profiles in children. Methods: Histogram analysis and clustering-based algorithms were applied to DWIs from 33 patients to perform tumor voxel discrimination into two classes. Voxel uncertainties were quantified and incorporated to obtain a more reproducible and meaningful estimate of ADC values within a tumor habitat. Computational experiments were performed by smearing the ADC values in order to obtain confidence maps that help identify and remove noise from low-quality voxels within high-signal clustered regions. The proposed Fit-Cluster-Fit methodology was compared with two other methods: conventional voxel-based and a cluster-based strategy. Results: The cluster-based and Fit-Cluster-Fit models successfully differentiated benign and malignant neuroblastic tumor profiles when using values from the lower ADC habitat. In particular, the best sensitivity (91%) and specificity (89%) of all the combinations and methods explored was achieved by removing uncertainties at a 70% confidence threshold, improving standard voxel-based sensitivity and negative predictive values by 4% and 10%, respectively. Conclusions: The Fit-Cluster-Fit method improves the performance of imaging biomarkers in classifying pediatric solid tumor cancers and it can probably be adapted to dynamic signal evaluation for any tumor.

Biomarkers of aging are urgently needed to identify individuals at high risk of developing age-associated disease or disability. Growing evidence from population-based studies points to whole-body magnetic resonance imaging's (MRI) enormous potential for quantifying subclinical disease burden and for assessing changes that occur with aging in all organ systems. The Aging Imageomics Study aims to identify biomarkers of human aging by analyzing imaging, biopsychosocial, cardiovascular, metabolomic, lipidomic, and microbiome variables. This study recruited 1030 participants aged >= 50 years (mean 67, range 50-96 years) that underwent structural and functional MRI to evaluate the brain, large blood vessels, heart, abdominal organs, fat, spine, musculoskeletal system and ultrasonography to assess carotid intima-media thickness and plaques. Patients were notified of incidental findings detected by a certified radiologist when necessary. Extensive data were also collected on anthropometrics, demographics, health history, neuropsychology, employment, income, family status, exposure to air pollution and cardiovascular status. In addition, several types of samples were gathered to allow for microbiome, metabolomic and lipidomic profiling. Using big data techniques to analyze all the data points from biological phenotyping together with health records and lifestyle measures, we aim to cultivate a deeper understanding about various biological factors (and combinations thereof) that underlie healthy and unhealthy aging.

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.

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

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/mm(2). 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 f(CER) and D*(CER) correlated with rCBF(CER). The best cutoffs for 6-month survival were f(CER)>9.86% and D*(CER)>21.712 x 10(-3)mm(2)/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); f(CER) combined with treatment predicted survival with 100% accuracy. Conclusions The IVIM-metrics f(CER) and D*(CER) are promising biomarkers of 6-month survival in newly diagnosed glioblastoma.

A higher degree of angiogenesis is associated with shortened survival in glioblastoma. Feasible morphometric parameters for analyzing vascular networks in brain tumors in clinical practice are lacking. We investigated whether the macrovascular network classified by the number of vessel-like structures (nVS) visible on three-dimensional T1-weighted contrast&ndash, enhanced (3D-T1CE) magnetic resonance imaging (MRI) could improve survival prediction models for newly diagnosed glioblastoma based on clinical and other imaging features. Ninety-seven consecutive patients (62 men, mean age, 58 &plusmn, 15 years) with histologically proven glioblastoma underwent 1.5T-MRI, including anatomical, diffusion-weighted, dynamic susceptibility contrast perfusion, and 3D-T1CE sequences after 0.1 mmol/kg gadobutrol. We assessed nVS related to the tumor on 1-mm isovoxel 3D-T1CE images, and relative cerebral blood volume, relative cerebral flow volume (rCBF), delay mean time, and apparent diffusion coefficient in volumes of interest for contrast-enhancing lesion (CEL), non-CEL, and contralateral normal-appearing white matter. We also assessed Visually Accessible Rembrandt Images scoring system features. We used ROC curves to determine the cutoff for nVS and univariate and multivariate cox proportional hazards regression for overall survival. Prognostic factors were evaluated by Kaplan-Meier survival and ROC analyses. Lesions with nVS &gt, 5 were classified as having highly developed macrovascular network, 58 (60.4%) tumors had highly developed macrovascular network. Patients with highly developed macrovascular network were older, had higher volumeCEL, increased rCBFCEL, and poor survival, nVS correlated negatively with survival (r = &minus, 0.286, p = 0.008). On multivariate analysis, standard treatment, age at diagnosis, and macrovascular network best predicted survival at 1 year (AUC 0.901, 83.3% sensitivity, 93.3% specificity, 96.2% PPV, 73.7% NPV). Contrast-enhanced MRI macrovascular network improves survival prediction in newly diagnosed glioblastoma.

Magnetic resonance (MR) imaging is the reference modality for brain tumor diagnosis, staging, and prognosis evaluation. Identifying tumor persistence and recurrence after surgery is still one of the open issues for the multidisciplinary teams managing these patients, including neurosurgeons, radiologists, pathologists, and oncologists. Several studies have evaluated the effectiveness of conventional MR examinations for brain tumor grading and follow-up. Nevertheless, the conventional approach mainly relies on morphological evidences (lesion, edema, and necrosis volumes) and on the disruption of the blood-brain barrier as evaluated by the relative enhancement after the administration of Gadolinium (Gd)-based contrast agents.

Background and Purpose— Physiological effects of stroke are best assessed over entire brain networks rather than just focally at the site of structural damage. Resting-state functional magnetic resonance imaging can map functional-anatomic networks by analyzing spontaneously correlated low-frequency activity fluctuations across the brain, but its potential usefulness in predicting functional outcome after acute stroke remains unknown. We assessed the ability of resting-state functional magnetic resonance imaging to predict functional outcome after acute stroke. Methods— We scanned 37 consecutive reperfused stroke patients (age, 69±14 years; 14 females; 3-day National Institutes of Health Stroke Scale score, 6±5) on day 3 after symptom onset. After imaging preprocessing, we used a whole-brain mask to calculate the correlation coefficient matrices for every paired region using the Harvard-Oxford probabilistic atlas. To evaluate functional outcome, we applied the modified Rankin Scale at 90 days. We used region of interest analyses to explore the functional connectivity between regions and graph-computation analysis to detect differences in functional connectivity between patients with good functional outcome (modified Rankin Scale score ≤2) and those with poor outcome (modified Rankin Scale score >2). Results— Patients with good outcome had greater functional connectivity than patients with poor outcome. Although 3-day National Institutes of Health Stroke Scale score was the most accurate independent predictor of 90-day modified Rankin Scale (84.2%), adding functional connectivity increased accuracy to 94.7%. Preserved bilateral interhemispheric connectivity between the anterior inferior temporal gyrus and superior frontal gyrus and decreased connectivity between the caudate and anterior inferior temporal gyrus in the left hemisphere had the greatest impact in favoring good prognosis. Conclusions— These data suggest that information about functional connectivity from resting-state functional magnetic resonance imaging may help predict 90-day stroke outcome.

[EN] Purpose: To determine if preoperative vascular heterogeneity of glioblastoma is predictive of overall survival of patients undergoing standard-of-care treatment by using an unsupervised multiparametric perfusion-based habitat-discovery algorithm. Materials and Methods: Preoperative magnetic resonance (MR) imaging including dynamic susceptibility-weighted contrast material-enhanced perfusion studies in 50 consecutive patients with glioblastoma were retrieved. Perfusion parameters of glioblastoma were analyzed and used to automatically draw four reproducible habitats that describe the tumor vascular heterogeneity: high-angiogenic and low-angiogenic regions of the enhancing tumor, potentially tumor-infiltrated peripheral edema, and vasogenic edema. Kaplan-Meier and Cox proportional hazard analyses were conducted to assess the prognostic potential of the hemodynamic tissue signature to predict patient survival. Results: Cox regression analysis yielded a significant correlation between patients' survival and maximum relative cerebral blood volume (rCBV(max)) and maximum relative cerebral blood flow (rCBF(max)) in high-angiogenic and low-angiogenic habitats (P < .01, false discovery rate-corrected P < .05). Moreover, rCBF(max) in the potentially tumor-infiltrated peripheral edema habitat was also significantly correlated (P < .05, false discovery rate-corrected P < .05). Kaplan-Meier analysis demonstrated significant differences between the observed survival of populations divided according to the median of the rCBV(max) or rCBF(max) at the high-angiogenic and low-angiogenic habitats (log-rank test P < .05, false discovery rate-corrected P < .05), with an average survival increase of 230 days. Conclusion: Preoperative perfusion heterogeneity contains relevant information about overall survival in patients who undergo standard-of-care treatment. The hemodynamic tissue signature method automatically describes this heterogeneity, providing a set of vascular habitats with high prognostic capabilities., Study supported by H2020 European Institute of Innovation and Technology (POC-2016.SPAIN-07) and Universitat Politecnica de Valencia (PAID-10-14). J.J.A., E.F.G., and J.M.G.G. supported by Secretaria de Estado de Investigacion, Desarrollo e Innovacion (DPI2016-80054-R, TIN2013-43457-R). E.F.G. supported by CaixaImpulse program from Fundacio Bancaria "la Caixa" (LCF/TR/CI16/10010016). E.F.G and A.A.B. supported by the Universitat Politecnica de Valencia Instituto Investigacion Sanitaria de La Fe (C05).

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. • 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.

The application of imaging biomarkers in oncology is still in its infancy, but with the expansion of radiomics and radiogenomics a revolution is expected in this field. This may be of special interest in head and neck cancer, since it can promote precision medicine and personalization of treatment by overcoming several intrinsic obstacles in this pathology. Our goal is to provide the medical oncologist with the basis to approach these disciplines and appreciate their main uses in clinical research and clinical practice in the medium term. Aligned with this objective we analyzed the most relevant studies in the field, also highlighting novel opportunities and current challenges.

Contains fulltext : 209088.pdf (Publisher’s version ) (Open Access) 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.

AIM To test the diagnostic performance of a deep learning-based system for the detection of clinically significant pulmonary nodules/masses on chest radiographs. MATERIALS AND METHODS Using a retrospective study of 100 patients (47 with clinically significant pulmonary nodules/masses and 53 control subjects without pulmonary nodules), two radiologists verified clinically significantly pulmonary nodules/masses according to chest computed tomography (CT) findings. A computer-aided diagnosis (CAD) software using a deep-learning approach was used to detect pulmonary nodules/masses to determine the diagnostic performance in four algorithms (heat map, abnormal probability, nodule probability, and mass probability). RESULTS A total of 100 cases were included in the analysis. Among the four algorithms, mass algorithm could achieve a 76.6% sensitivity (36/47, 11 false negative) and 88.68% specificity (47/53, six false-positive) in the detection of pulmonary nodules/masses at the optimal probability score cut-off of 0.2884. Compared to the other three algorithms, mass probability algorithm had best predictive ability for pulmonary nodule/mass detection at the optimal probability score cut-off of 0.2884 (AUCMass: 0.916 versus AUCHeat map: 0.682, p CONCLUSION In conclusion, the deep-learning based computer-aided diagnosis system will likely play a vital role in the early detection and diagnosis of pulmonary nodules/masses on chest radiographs. In future applications, these algorithms could support triage workflow via double reading to improve sensitivity and specificity during the diagnostic process.

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.

Introduction: There is growing scientific interest in understanding the biological mechanisms affecting and/or underlying violent behaviors in order to develop effective treatment and prevention programs. In recent years, neuroscientific research has tried to demonstrate whether the intrinsic activity within the brain at rest in the absence of any external stimulation (resting-state functional connectivity; RSFC) could be employed as a reliable marker for several cognitive abilities and personality traits that are important in behavior regulation, particularly, proneness to violence. Aims: This review aims to highlight the association between the RSFC among specific brain structures and the predisposition to experiencing anger and/or responding to stressful and distressing situations with anger in several populations. Methods: The scientific literature was reviewed following the PRISMA quality criteria for reviews, using the following digital databases: PubMed, PsycINFO, Psicodoc, and Dialnet. Results: The identification of 181 abstracts and retrieval of 34 full texts led to the inclusion of 17 papers. The results described in our study offer a better understanding of the brain networks that might explain the tendency to experience anger. The majority of the studies highlighted that diminished RSFC between the prefrontal cortex and the amygdala might make people prone to reactive violence, but that it is also necessary to contemplate additional cortical (i.e. insula, gyrus [angular, supramarginal, temporal, fusiform, superior, and middle frontal], anterior and posterior cingulated cortex) and subcortical brain structures (i.e. hippocampus, cerebellum, ventral striatum, and nucleus centralis superior) in order to explain a phenomenon as complex as violence. Moreover, we also described the neural pathways that might underlie proactive violence and feelings of revenge, highlighting the RSFC between the OFC, ventral striatal, angular gyrus, mid-occipital cortex, and cerebellum. Conclusions. The results from this synthesis and critical analysis of RSFC findings in several populations offer guidelines for future research and for developing a more accurate model of proneness to violence, in order to create effective treatment and prevention programs.

The aim of this investigation was to compare the diagnostic performance of radiographers and deep learning algorithms in pulmonary nodule/mass detection on chest radiograph.A test set of 100 chest radiographs containing 53 cases with no pathology (normal) and 47 abnormal cases (pulmonary nodules/masses) independently interpreted by 6 trained radiographers and deep learning algorithems in a random order. The diagnostic performances of both deep learning algorithms and trained radiographers for pulmonary nodules/masses detection were compared.QUIBIM Chest X-ray Classifier, a deep learning through mass algorithm that performs superiorly to practicing radiographers in the detection of pulmonary nodules/masses (AUCMass: 0.916 vs AUCTrained radiographer: 0.778, P

TPS9586 Background: Intratumoral immunotherapies are gaining interest in oncology, particularly in melanoma. These therapies, however, have faced some issues. For instance, standard response criteria do not accurately describe tumor burden, and responses may differ for injected/non injected lesions. Besides, target lesions may become non evaluable. Biomarkers provide interesting information for these therapies. In addition, some radiomic signatures have been associated with CD-8 infiltration. BO-112 is a double stranded synthetic RNA formulated with polyethyleneimine (PEI) that mimics a viral infection, mobilizing the immune system and changing tumor microenvironment. Clinical data are available from a first-in-human study, which showed ORR of 11% and DCR of 46% in patients who had developed progressive disease on immunotherapy. In patients with melanoma, this ORR was 20%. A phase 2 clinical study of BO-112 with pembrolizumab in patients with liver metastases from digestive tumors is ongoing. Both studies brought up data regarding how some biomarkers are increased after a single dose of BO-112 and correlated with responses. In this phase II study in patients with pretreated melanoma (NCT04570332), we will prospectively assess CD-8 and PD-L1 by immunohistochemistry, which will be compared with multi-parametric radiologic findings and correlated with clinical benefit. In addition, retrospective DNA sequencing will be performed. This kind of exploratory analysis in intratumoral immunotherapies might be key to identify predictive and prognostic factors. Methods: Phase 2, single arm, open label study of BO-112 with pembrolizumab in patients with advanced melanoma. BO-112 is administered once weekly (QW) in 1 to 8 tumor lesions, total dose 1-2 mg (depending on the number of injected lesions), for the first 7 weeks and then once every three weeks (Q3W); pembrolizumab 200 mg will be administered Q3W. Key eligibility criteria: advanced cutaneous or mucosal melanoma; patients must have progressed on or after treatment with an antiPD-1/L1 mAb; at least one measurable lesion amenable for weekly IT injection. Primary efficacy variable is ORR by RECIST 1.1, assessed by independent central radiologist (QUIBIM Precision platform). A 1-sided alpha of 4.19% and power of 81.8% are used. If less than 8 patients out of 40 have ORR, the study will not meet the statistical bar. Secondary endpoints include clinical activity by RECIST1.1 and iRECIST, overall survival, safety and PKs. Exploratory objectives include itRECIST and evaluation of CD-8 and PD-L1 expression by immunohistochemistry (Pangaea laboratory), which will be correlated with radiomic signatures (first order and second order) from standard-of-care computed tomography (CT) images. Enrollment is open and 1 of planned 40 patients has been enrolled. Nineteen sites are planned to participate. Clinical trial information: NCT04570332.

AIM: To compare the performance of multi-echo chemical-shift-encoded (MECSE) magnetic resonance imaging (MRI) proton density fat fraction (PDFF) estimation, considering three different fat frequency peak combinations, for the quantification of steatosis in patients with non-alcoholic fatty liver disease (NAFLD). MATERIALS AND METHODS: The present study was a prospective cross-sectional research of 121 patients with metabolic syndrome and evidence of hepatic steatosis on ultrasound, who underwent a 3 T MRI examination. All patients were studied with a multifrequency MECSE sequence. The PDFF was calculated using six peaks (MECSEp123456), three peaks (MECSEp456), and a single peak (MECSEp5) model. The two simpler fat peak models were compared to the six peaks model, which was considered the reference standard. Linearity was evaluated using linear regression while agreement was described using Bland-Altman analysis. RESULTS: The mean age was 47 (similar to 9) years and BMI was 29.9 (similar to 2.9) kg/m(2). Steatosis distribution was 15%/31%/54% (S1/S2/S3, respectively). Compared to MECSEp123456, both models provided linear PDFF measurements (R-2 = 0.99 and 0.97, MECSEp456 and MECSEp5 respectively). Regression slope (0.92; p

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.We studied 24 consecutive obese subjects (13 women; age, 49·8 ± 8·1 years; body mass index [BMI], 43·9 ± 4·54 kg/m(2) ) and 20 healthy volunteers (10 women; age, 48·8 ± 9·5 years; BMI, 24·3 ± 3·54 kg/m(2) ) 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.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 caudate601·832 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; R(2) = 0·103). Bayesian information criterion demonstrated that osteocalcin had the predominant role in predicting IGT score.Lower serum osteocalcin concentrations are associated with brain microstructural changes and worse cognitive performance.

Multiple myeloma is a common hematological malignancy arising in the bone marrow. Bone lesions were initially depicted with conventional radiography, although recently F-FDG PET/CT and MRI are recognized as having a clear role in the initial workup and in the evaluation of therapy response.Tumor development produces osteolysis and expansive lesions. Although tumor burden and extent are key prognostic factors, different cancer hallmarks can also be evaluated in vivo through noninvasive imaging.This imaging-based virtual biopsy approach might be useful to define several relevant prognostic markers, such as angiogenesis, cellularity, metabolic trapping and bone morphology and elasticity, both before and during treatment, to predict tumor behavior and the early effect of therapy.

[EN] Multivariate Curve Resolution (MCR) has been applied on prostate Diffusion Weighted-Magnetic Resonance Images (DW-MRI). Different physiological-based modeling approaches of the diffusion process have been submitted to validation by sequentially incorporating prior knowledge on the MCR constraints. Results validate the biexponential diffusion modeling approach and show the capability of the MCR models to find, characterize and locate the behaviors related to the presence of an early prostate tumor., The authors want to thank prof. Anna de Juan for her comments and help in using the software for this study. This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI 2011-28112-004-02.

Several pathological conditions carry high risk of bone fracture which is associated to high economic and healthy costs. In order to improve understanding the bone microarchitecture, this work aims to evaluate the accuracy of the bone analysis methodology and compare it to the reference standard under different imaging modalities. The results indicate that there is a good relation between the analysis of the bone analysis methodology obtained from the MDCT and MR scans and these modalities can be used for bone quality assessment in clinical practice.

Osteoporosis, characterised by a decrease in bone mineral density, is responsible for millions of fractures. The reference test for this disease is bone densitometry. The diagnosis, based on statistical parameters obtained from an Xray image, evaluates the patient's risk of fracture. Their results are not accurate enough, since about 40% of patients with a low fracture risk diagnosis end up suffering from an osteoporotic fracture. Therefore, it seems reasonable to look for a technique that allows diagnoses of greater accuracy. The authors have developed an analysis technique, called the Cartesian Grid Finite Element Method (cgFEM), based on the use of Cartesian meshes, that allows the generation of Finite Element (FE) models directly from medical images. This method has been adapted to obtain an indication of the stress distribution on the femur that enriches the information available to evaluate the risk of osteoporotic failure. The multi-disciplinary collaboration of this work has allowed analysing densitometries from a large set of patients. The preliminary results obtained show that it will be able to improve the evaluation of the risk of fracture in femurs due to osteoporosis.

Aim: To investigate iron loading within the liver, pancreas, spleen, and bone marrow using magnetic resonance imaging (MRI) transverse relaxation rate (R2*), in patients with diffuse liver diseases; to evaluate the relationships between iron accumulation in these tissue compartments; and to assess the association between tissue iron overload and the pattern of hepatic cellular iron distribution (hepatocytes versus Kupffer cells). Material and methods: Fifty-six patients with diffuse liver diseases had MRI-derived R2* values, using a multi-echo chemical-shift encoded MRI sequence, of the liver, pancreas, spleen, and vertebral bone marrow. All patients had liver biopsy samples scored for hepatic iron grading (0–4) and iron cellular distribution (within hepatocytes only or within both hepatocytes and Kupffer cells). Results: Liver R2* increased with histological iron grade (RS=0.58, p

Context Microbiota perturbations seem to exert modulatory effects on emotional behavior, stress-, and pain-modulation systems in adult animals; however, limited information is available in humans. Objective To study potential relationships among the gut metagenome, brain microstructure, and cognitive performance in middle-aged, apparently healthy, obese and nonobese subjects after weight changes. Design This is a longitudinal study over a 2-year period. Setting A tertiary public hospital. Patients or other participants Thirty-five (18 obese) apparently healthy subjects. Intervention(s) Diet counseling was provided to all subjects. Obese subjects were followed every 6 months. Main outcome measure(s) Brain relaxometry (using magnetic resonance R2*), cognitive performance (by means of cognitive tests), and gut microbiome composition (shotgun). Results R2* increased in both obese and nonobese subjects, independent of weight variations. Changes in waist circumference, but not in body mass index, were associated with brain iron deposition (R2*) in the striatum, amygdala, and hippocampus in parallel to visual-spatial constructional ability and circulating beta amyloid Aβ42 levels. These changes were linked to shifts in gut microbiome in which the relative abundance of bacteria belonging to Caldiserica and Thermodesulfobacteria phyla were reciprocally associated with raised R2* in different brain nuclei. Of note, the increase in bacteria belonging to Tenericutes phylum was parallel to decreased R2* gain in the striatum, serum Aβ42 levels, and spared visual-spatial constructional ability. Interestingly, metagenome functions associated with circulating and brain iron stores are involved in bacterial generation of siderophores. Conclusions Changes in the gut metagenome are associated longitudinally with cognitive function and brain iron deposition.

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 S = 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 S = 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. This work was partially funded by a research grant from the Teaching and Research Department of Centro Hospitalar do Porto (DEFI:309/12(213-DEFI/251-CES)) and from a Spanish Ministry of Health and Carlos III Health Institute funding grant (PI12/01262). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Objectives: The aim of this study was to evaluate the use of phase-contrast MR imaging to diagnose normal pressure hydrocephalus (NPH) and differentiate it from other neurological disorders with similar clinical symptoms. Methods: The study included 108 subjects, of whom 61 were healthy controls and 47, patients; in the patient group, 19 had cerebrovascular disease (CVD) and 28 had NPH. All patients underwent a phase-contrast MRI study and several CSF flow and velocity parameters were measured at the aqueduct of Sylvius. Discriminant analyses were performed to evaluate the classification capacity of both individual parameters and the combination of different parameters. Results: Maximum diastolic velocity, mean flow, and stroke volume showed statistically significant differences that could be used to distinguish between NPH and CVD patients (P

Objective We used an animal model to analyze the reproducibility and accuracy of certain biomarkers of bone image quality in comparison to a gold standard of computed microtomography (μCT). Materials and methods We used magnetic resonance (MR) imaging and μCT to study the metaphyses of 5 sheep tibiae. The MR images (3 T) were acquired with a T1-weighted gradient echo sequence and an isotropic spatial resolution of 180 μm. The μCT images were acquired using a scanner with a spatial resolution of 7.5 μm isotropic voxels. In the preparation of the images, we applied equalization, interpolation, and thresholding algorithms. In the quantitative analysis, we calculated the percentage of bone volume (BV/TV), the trabecular thickness (Tb.Th), the trabecular separation (Tb.Sp), the trabecular index (Tb.N), the 2D fractal dimension (D 2D ), the 3D fractal dimension (D 3D ), and the elastic module in the three spatial directions ( Ex , Ey and Ez ). Results The morphometric and mechanical quantification of trabecular bone by MR was very reproducible, with percentages of variation below 9% for all the parameters. Its accuracy compared to the gold standard (μCT) was high, with errors less than 15% for BV/TV, D 2D , D 3D , and E app x , E app y and E app z . Conclusions Our experimental results in animals confirm that the parameters of BV/TV, D 2D , D 3D , and E app x , E app y and E app z obtained by MR have excellent reproducibility and accuracy and can be used as imaging biomarkers for the quality of trabecular bone.

Objectives: To evaluate the effect of various medial patellofemoral ligament (MPFL) fixation techniques on patellar pressure compared with the native knee. Methods: A finite element model of the patellofemoral joint consisting of approximately 30,700 nodes and 22,200 elements was created from computed tomography scans of 24 knees with chronic lateral patellar instability. Patellar contact pressures and maximum MPFL graft stress at five positions of flexion (0&deg, 30&deg, 60&deg, 90&deg, and 120&deg, ) were analyzed in three types of MPFL reconstruction (MPFLr): (1) static/anatomic, (2) dynamic, using the adductor magnus tendon (AMT) as the femoral fixation, and (3) dynamic, using the quadriceps tendon as the attachment (medial quadriceps tendon-femoral ligament (MQTFL) reconstruction). Results: In the static/anatomic technique, the patellar contact pressures at 0&deg, and 30&deg, were greater than in the native knee. As in a native knee, the contact pressures at 60&deg, were very low. The maximum MPFL graft stress at 0&deg, was greater than in a native knee. However, the MPFL graft was loose at 60&deg, meaning it had no tension. In the dynamic MPFLr using the AMT as a pulley, the patellar contact pressures were like those of a native knee throughout the entire range of motion. However, the maximum stress of the MPFL graft at 0&deg, was less than that of a native ligament. Yet, the maximum MPFL graft stress was greater at 30&deg, than in a native ligament. After 30&deg, of flexion, the MPFL graft loosened, similarly to a native knee. In the dynamic MQTFL reconstruction, the maximum patellar contact pressure was slightly greater than in a normal knee. The maximum stress of the MPFL graft was much greater at 0&deg, than that of a native MPFL. After 30&deg, of flexion, the MQPFL graft loosened just as in the native knee. Conclusions: The patellar contact pressures after the dynamic MPFLr were like those of the native knee, whereas a static reconstruction resulted in greater pressures, potentially increasing the risk of patellofemoral osteoarthritis in the long term. Therefore, the dynamic MPFLr might be a safer option than a static reconstruction from a biomechanical perspective.

Imaging biomarkers describe objective characteristics that are related to normal biological processes, diseases, or the response to treatment. They enable radiologists to incorporate into their reports data about structure, function, and tissue components. With the aim of taking maximum advantage of the quantification of medical images, we present a procedure to integrate imaging biomarkers into radiological reports, bringing the new paradigm of personal medicine closer to radiological workflow. In this manner, the results of quantification can complement traditional radiological diagnosis, improving accuracy and the evaluation of the efficacy of treatments. A more personalized, standardized, structured radiological report should include quantitative analyses to complement conventional qualitative reporting in selected cases.

With the elderly population increase, osteoporosis and its consequences have become not just a health issue but also a serious economic burden. The trabecular bone structure plays a very important role for the bone quality and mechanical competence of the scaffold. Currently, it is claimed that the trabecular microarchitecture understanding can improve the fracture risk prediction above 65%. Several parameters seem to be correlated providing structural details of the trabecular bone network. However, the tortuosity of the trabeculae has not yet been systematically taken into account and its contribution has not been fully investigated and understood. In this paper, we discuss the relationship between the trabecular tortuosity, connectivity, volume fraction, and elasticity, and provide a unified parameter to estimate the mechanical competence of the structure. It is shown that the trabecular network tortuosity presents high linear correlation with the other parameters and that the trabeculae tend to get aligned in the direction where the structure is mostly submitted to stress, corresponding to higher stiffness orientation. This new parameter will help to integrate the relevant information of bone microarchitecture quality and assess more directly the real trabecular fragility in osteoporotic patients.

BACKGROUND/ OBJECTIVES: Growing evidence implicates neuroinflammation in the pathogenesis of diet-induced obesity and cognitive dysfunction in rodent models. Obesity is associated with reduced white matter integrity and cognitive decline. Circulating lipopolysaccharide binding protein (LBP) concentration is known to be increased in patients with obesity. Here, we aimed to evaluate whether circulating LBP is associated longitudinally with white matter structure and cognitive performance according to obesity status. SUBJECTS/METHODS: This longitudinal study analyzed circulating LBP (ELISA), DTI-metrics (axial diffusivity (L1), fractional anisotropy (FA) and radial diffusivity (RD)) in specific regions of the white matter of 24 consecutive middle-aged obese subjects (13 women) and 20 healthy volunteers (10 women) at baseline and two years later. Digit Span Test (DST) was used as a measure of working memory/short-term verbal memory. RESULTS: Circulating LBP concentration was associated with FA and L1 values of several white matter regions both at baseline and follow-up. The associations remained significant after controlling for age, BMI, fat mass and plasma high sensitivity C-reactive protein. Importantly, the increase in LBP over time impacted negatively on FA and L1 values and on DST performance. CONCLUSIONS: Circulating LBP associates with brain white matter integrity and working memory/short-term verbal memory in both obese and non-obese subjects.

Diffuse liver diseases result from a wide spectrum of etiologies, and, since they can lead to cirrhosis, end-stage liver disease, and hepatocellular carcinoma, they represent an important cause of morbidity, mortality, and healthcare costs worldwide. Regardless of the etiology, liver fat, iron, and combined overload are common pathological features of different diffuse liver diseases. Fat and iron frequently coexist and they act as cofactors in disease progression. Because liver biopsy has several limitations, quantitative imaging biomarkers have been developed for liver fat and iron quantification.

Medical imaging has a key role in current workflows for the assessment of clinical decisions in many disease scenarios. Concretely, imaging biomarkers are transforming the way radiology has taken part in the healthcare cycle, from conventional workflows based on qualitative criteria and the experience of the radiologist toward having a powerful measurement tool in each hospital, allowing for the extraction of quantitative indicators of tissue and organ characteristics by the application of image processing methods and algorithms to medical images from modalities like X-ray (XR), magnetic resonance (MR) imaging, computed tomography (CT), ultrasound (US), positron emission tomography (PET), single-photon emission computed tomography (SPECT), among others.

William Morton wrote the first radiology report in 1896. It was written in narrative mode and with no consensual content (Fig. 11.1). In 1923 Charles D. Enfield emphasized that the radiologist provided detailed descriptions, without expressing his opinion on the clinical relevance of such descriptions, which could benefit the patient [1]. However, the methodology for reporting medical studies has not changed with the same speed as technology, work environment or medical discoveries. Imaging studies are often an integral part of a patient’s evaluation, and the associated radiology report serves as the primary method of communication between healthcare providers and, increasingly, patients.

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. 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. 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. 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.