FDG-PET markers of heterogeneity and different risk of progression in amnestic MCI.

INTRODUCTION: Amnestic mild cognitive impairment (aMCI) is emerging as a heterogeneous condition. METHODS: We looked at a cohort of N = 207 aMCI subjects, with baseline fluorodeoxyglucose positron emission tomography (FDG-PET), T1 magnetic resonance imaging, cerebrospinal fluid (CSF), apolipoprotein E (APOE), and neuropsychological assessment. An algorithm based on FDG-PET hypometabolism classified each subject into subtypes, then compared biomarker measures and clinical progression. RESULTS: Three subtypes emerged: hippocampal sparing-cortical hypometabolism, associated with younger age and the highest level of Alzheimer's disease (AD)-CSF pathology; hippocampal/cortical hypometabolism, associated with a high percentage of APOE ε3/ε4 or ε4/ε4 carriers; medial-temporal hypometabolism, characterized by older age, the lowest AD-CSF pathology, the most severe hippocampal atrophy, and a benign course. Within the whole cohort, the severity of temporo-parietal hypometabolism, correlated with AD-CSF pathology and marked the rate of progression of cognitive decline. DISCUSSION: FDG-PET can distinguish clinically comparable aMCI at single-subject level with different risk of progression to AD dementia or stability. The obtained results can be useful for the optimization of pharmacological trials and automated-classification models. HIGHLIGHTS: Algorithm based on FDG-PET hypometabolism demonstrates distinct subtypes across aMCI; Three different subtypes show heterogeneous biological profiles and risk of progression; The cortical hypometabolism is associated with AD pathology and cognitive decline; MTL hypometabolism is associated with the lowest conversion rate and CSF-AD pathology.

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA.

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.

Italian, European, and international neuroinformatics efforts: An overview.

Neuroinformatics is a research field that focusses on software tools capable of identifying, analysing, modelling, organising and sharing multiscale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big Data phenomenon, characterised by the so-called 3Vs (volume, velocity and variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic and radiological technologies. This situation has led to a 'data deluge', as neuroscientists can routinely collect more study data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focussed neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS and GAAIN BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI and CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable and Reusable (FAIR) neuroscience worldwide.

MRI data quality assessment for the RIN - Neuroimaging Network using the ACR phantoms.

PURPOSE: Generating big-data is becoming imperative with the advent of machine learning. RIN-Neuroimaging Network addresses this need by developing harmonized protocols for multisite studies to identify quantitative MRI (qMRI) biomarkers for neurological diseases. In this context, image quality control (QC) is essential. Here, we present methods and results of how the RIN performs intra- and inter-site reproducibility of geometrical and image contrast parameters, demonstrating the relevance of such QC practice. METHODS: American College of Radiology (ACR) large and small phantoms were selected. Eighteen sites were equipped with a 3T scanner that differed by vendor, hardware/software versions, and receiver coils. The standard ACR protocol was optimized (in-plane voxel, post-processing filters, receiver bandwidth) and repeated monthly. Uniformity, ghosting, geometric accuracy, ellipse's ratio, slice thickness, and high-contrast detectability tests were performed using an automatic QC script. RESULTS: Measures were mostly within the ACR tolerance ranges for both T1- and T2-weighted acquisitions, for all scanners, regardless of vendor, coil, and signal transmission chain type. All measurements showed good reproducibility over time. Uniformity and slice thickness failed at some sites. Scanners that upgraded the signal transmission chain showed a decrease in geometric distortion along the slice encoding direction. Inter-vendor differences were observed in uniformity and geometric measurements along the slice encoding direction (i.e. ellipse's ratio). CONCLUSIONS: Use of the ACR phantoms highlighted issues that triggered interventions to correct performance at some sites and to improve the longitudinal stability of the scanners. This is relevant for establishing precision levels for future multisite studies of qMRI biomarkers.

Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN-Neuroimaging Network.

Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures.

Norms for Automatic Estimation of Hippocampal Atrophy and a Step Forward for Applicability to the Italian Population.

INTRODUCTION: Hippocampal volume is one of the main biomarkers of Alzheimer's Dementia (AD). Over the years, advanced tools that performed automatic segmentation of Magnetic Resonance Imaging (MRI) T13D scans have been developed, such as FreeSurfer (FS) and ACM-Adaboost (AA). Hippocampal volume is considered abnormal when it is below the 5th percentile of the normative population. The aim of this study was to set norms, established from the Alzheimer's Disease Neuroimaging Initiative (ADNI) population, for hippocampal volume measured with FS v.6.0 and AA tools in the neuGRID platform (www.neugrid2.eu) and demonstrate their applicability for the Italian population. METHODS: Norms were set from a large group of 545 healthy controls belonging to ADNI. For each pipeline, subjects with segmentation errors were discarded, resulting in 532 valid segmentations for FS and 421 for AA (age range 56-90 years). The comparability of ADNI and the Italian Brain Normative Archive (IBNA), representative of the Italian general population, was assessed testing clinical variables, neuropsychological scores and normalized hippocampal volumes. Finally, percentiles were validated using the Italian Alzheimer's disease Repository Without Borders (ARWiBo) as external independent data set to evaluate FS and AA generalizability. RESULTS: Hippocampal percentiles were checked with the chi-square goodness of fit test. P-values were not significant, showing that FS and AA algorithm distributions fitted the data well. Clinical, neuropsychological and volumetric features were similar in ADNI and IBNA (p > 0.01). Hippocampal volumes measured with both FS and AA were associated with age (p < 0.001). The 5th percentile thresholds, indicating left/right hippocampal atrophy were respectively: (i) below 3,223/3,456 mm3 at 56 years and 2,506/2,415 mm3 at 90 years for FS; (ii) below 4,583/4,873 mm3 at 56 years and 3,831/3,870 mm3 at 90 years for AA. The average volumes computed on 100 cognitively intact healthy controls (CN) selected from ARWiBo were close to the 50th percentiles, while those for 100 AD patients were close to the abnormal percentiles. DISCUSSION: Norms generated from ADNI through the automatic FS and AA segmentation tools may be used as normative references for Italian patients with suspected AD.

Test-Retest Reliability of Pain Measures in Institutionalized Older Adults: Number of Painful Body Sites, Pain Intensity, and Pain Extent.

OBJECTIVE: The reliability of pain assessment in frail and older adults has seldom been assessed. This study aims to assess the test-retest reliability of (1) the number of painful body sites, (2) pain intensity, and (3) pain extent in institutionalized older adults. METHODS: Seventy-four older adults who were institutionalized were assessed in 2 separate sessions, 2 days to 1 week apart, for pain intensity, number of painful body sites, and pain extent (in pixels) using a vertical pain numeric scale (0 to 10), a body chart divided into 50 body regions, and ImageJ, respectively. Intraclass correlation coefficients (ICCs), standard error of measurement (SEM), and minimal detectable differences (MDDs) were calculated. RESULTS: In session 1, the mean values (± standard deviation) were 5.54 ± 2.12 points for pain intensity, 4.47 ± 3.27 for number of painful body sites, and 2,726.00 ± 2,322.09 for pain extent. ICCs were 0.82 (95% confidence interval [CI] = 0.72 to 0.89) for pain intensity, 0.89 (95% CI = 0.83 to 0.93) for number of painful body sites, and 0.74 (95% CI = -0.07 to 0.91) for pain area. The MDDs were 2.46 for pain intensity, 3.14 for number of painful body sites, and 4,997.60 for pain extent. CONCLUSIONS: The vertical pain rating scale and the body chart seem reliable to assess pain intensity and number of pain sites, respectively. The wide CI for the ICC found for pain area and the high measurement error compromise its potential clinical relevance.

Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts.

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify-CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from "slight" to "significant" in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology.

Simulation of Image-Guided Intervention in Medical Imaging Education.

INTRODUCTION/BACKGROUND: Medical imaging education programs across Europe, despite their variability, include clinical practice as a guarantee of quality because learning in a clinical context is more effective and allows for constructive qualification of students. Learning in a clinical simulation context is a strategy to promote simulated clinical experience and assure patient safety. In this work, a learning experience, consisting of simulating a pacemaker implantation, implemented over 3 years with students pursuing the Medical Imaging and Radiotherapy degree at the University of Aveiro was evaluated. METHODS: A pacemaker implantation simulation with fluoroscopic support was performed with students (third year) pursuing the Medical Imaging and Radiotherapy degree at the Simulation Centre of our institution over 3 years (2016, 2017-2018), addressing all the simulation phases. An operating theater, video recording system, high-fidelity full-body manikin with remote control and monitoring, anesthesia, and fluoroscopic C-arm imaging equipment were used for the simulation. After the simulation activity, students completed a questionnaire evaluated the importance of this learning experience for their educational process. RESULTS: Overall, the mean score results were consistent, even considering the three independent groups (one for each academic year). Students considered this experience valuable for their education, strongly agreeing that the simulation environment helped their learning process and allowed them to acquire, consolidate, and deepen knowledge. Furthermore, they considered that it impressed on them the necessity to continue to improve their learning, and that they would like to participate in other simulation scenarios. DISCUSSION/CONCLUSION: This simulation activity was a valuable experience for the learning process of the students because it facilitated the acquisition and consolidation of knowledge. It also allowed the students to be aware of the importance of being engaged in their own education. The results were highly consistent over the 3 years, reinforcing the positive feedback from this experience.

A randomized controlled trial of respiratory physiotherapy in lower respiratory tract infections.

INTRODUCTION: Physiotherapy may play a role in the recovery of signs, symptoms and function of patients with lower respiratory tract infections (LRTI) but its effectiveness is still controversial. OBJECTIVES: To assess the effects of respiratory physiotherapy compared with standard pharmacological care on symptoms and function in outpatients with LRTI. DESIGN: Single-blind, randomised controlled trial. SETTING: Outpatients were recruited from the casualties of a central hospital. PARTICIPANTS: Outpatients with LRTI were recruited and randomly allocated to the control (pharmacological) or experimental (pharmacological and respiratory physiotherapy) group. INTERVENTION: The intervention consisted of conventional pharmacological treatment and conventional pharmacological treatment plus respiratory physiotherapy. Respiratory physiotherapy included breathing and airway clearance techniques, exercise training and education during 3-weeks, 3 times per week. MAIN OUTCOME MEASURE: Primary outcome measures - occupation rate of wheezes Wh%; Secondary outcome measures - number of crackles, peripheral oxygen saturation (SpO2) modified Borg scale (mBorg), modified Medical Research Council scale (mMRC), 6-min walk test (6MWT), forced expiratory volume in 1 s and forced vital capacity, and volume and density of the lung and bronchial tree volume. RESULTS: Ninety-seven patients (53 controls and 44 experimental) completed the intervention. After the intervention, both groups improved significantly in all variables (0.0001 < p < 0.04; 0.001<ƞ2<0.092), with the exception of the mBorg. The magnitude of improvement of the experimental group exceeded the control group in the number of crackles, SpO2 levels, mMRC and 6MWT (0.002 < p < 0.032; 0.002<ƞ2<0.092). CONCLUSION: Adding respiratory physiotherapy to the pharmacological treatment of outpatients with LRTI results in greater recovery of symptoms and function parameters. TRIAL REGISTRATION: NCT02053870.

Improving access to community-based pulmonary rehabilitation: 3R protocol for real-world settings with cost-benefit analysis.

BACKGROUND: Pulmonary rehabilitation (PR) has demonstrated patients' physiological and psychosocial improvements, symptoms reduction and health-economic benefits whilst enhances the ability of the whole family to adjust to illness. However, PR remains highly inaccessible due to lack of awareness of its benefits, poor referral and availability mostly in hospitals. Novel models of PR delivery are needed to enhance its implementation while maintaining cost-efficiency. We aim to implement an innovative community-based PR programme and assess its cost-benefit. METHODS: A 12-week community-based PR will be implemented in primary healthcare centres where programmes are not available. Healthcare professionals will be trained. 73 patients with CRD and their caregivers (dyads patient-caregivers) will compose the experimental group. The control group will include dyads age- and disease-matched willing to collaborate in data collection but not in PR. Patients/family-centred outcomes will be dyspnoea (modified Medical Research Council Questionnaire), fatigue (Checklist of individual strength and Functional assessment of chronic illness therapy - fatigue), cough and sputum (Leicester cough questionnaire and Cough and sputum assessment questionnaire), impact of the disease (COPD Assessment Test), emotional state (The Hospital Anxiety and Depression Scale), number of exacerbations, healthcare utilisation, health-related quality of life and family adaptability/cohesion (Family Adaptation and Cohesion Scale). Other clinical outcomes will be peripheral (biceps and quadriceps-hand held dynamometer, 1 or 10 repetition-maximum) and respiratory (maximal inspiratory and expiratory pressures) muscle strength, muscle thickness and cross sectional area (biceps brachialis, rectus femoris and diaphragm-ultrasound imaging), exercise capacity (six-minute walk test and one-minute sit to stand test), balance (brief-balance evaluation systems test) and physical activity (accelerometer). Data will be collected at baseline, at 12 weeks, at 3- and 6-months post-PR. Changes in the outcome measures will be compared between groups, after multivariate adjustment for possible confounders, and effect sizes will be calculated. A cost-benefit analysis will be conducted. DISCUSSION: This study will enhance patients access to PR, by training healthcare professionals in the local primary healthcare centres to conduct such programmes and actively involving caregivers. The cost-benefit analysis of this intervention will provide an evidence-based insight into the economic benefit of community-based PR in chronic respiratory diseases. TRIAL REGISTRATION: The trial was registered in the ClinicalTrials.gov U.S. National Library of Medicine, on 10th January, 2019 (registration number: NCT03799666 ).

Artifact level produced by different femoral head prostheses in CT imaging: diamond coated silicon nitride as total hip replacement material.

Commercial femoral head prostheses (cobalt-chromium alloy, yttria partially stabilized zirconia (Y-PSZ) and alumina) and new silicon nitride ceramic ones (nanocrystalline diamond coated and uncoated) were compared in terms of artifact level production by computed tomography (CT). Pelvis examination by CT allows the correct diagnosis of some pathologies (e.g. prostate and colon cancer) and the evaluation of the prosthesis-bone interface in post-operative joint surgery. Artifact quantification is rarely seen in literature despite having a great potential to grade biomaterials according to their imaging properties. Materials' characteristics (density and effective atomic number), size and geometry of the prostheses can cause more or less artifact. A quantification procedure based on the calculation of four statistical parameters for the Hounsfield pixel values (mean, standard deviation, mean squared error and worst case error) is presented. CT sequential and helical scanning modes were performed. Results prove the artifact reproducibility and indicate that the cobalt-chromium and Y-PSZ are the most artifact-inducing materials, while alumina and silicon nitride (diamond coated and uncoated) ceramic ones present a low level of artifact. Considering the excellent biocompatibility and biotribological behaviour reported in earlier works, combined with the high medical imaging quality here assessed, diamond coated silicon nitride ceramics are arising as new materials for joint replacement.

Psoriasis pustulosa en un niño tratado con acitetrin / Psoriasis pustular in one child treaty with acitetrin

Se presenta el caso de un niño de 21 meses de edad con psoriasis pustulosa generalizada que tras presentar una grave afección de su estado general, mejoró notablemente con el empleo de acitetrin. Los autores sugieren la rápida supresión del acitetrin una vez lograda la remisión de los síntomas, con el fin de evitar la exagerada descamación que la medicación produce en los niños con el uso continuado y los efectos adversos indeseables comunes en los retinoides

Psoriasis pustulosa en un niño tratado con acitetrin / Psoriasis pustular in one child treaty with acitetrin

Se presenta el caso de un niño de 21 meses de edad con psoriasis pustulosa generalizada que tras presentar una grave afección de su estado general, mejoró notablemente con el empleo de acitetrin. Los autores sugieren la rápida supresión del acitetrin una vez lograda la remisión de los síntomas, con el fin de evitar la exagerada descamación que la medicación produce en los niños con el uso continuado y los efectos adversos indeseables comunes en los retinoides (AU)