Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis.

OBJECTIVES: To evaluate the methodological quality and diagnostic accuracy of MRI-based radiomic studies predicting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in gliomas. METHODS: PubMed Medline, EMBASE, and Web of Science were searched to identify MRI-based radiomic studies on MGMT methylation in gliomas published until December 31, 2022. Three raters evaluated the study methodological quality with Radiomics Quality Score (RQS, 16 components) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD, 22 items) scales. Risk of bias and applicability concerns were assessed with QUADAS-2 tool. A meta-analysis was performed to estimate the pooled area under the curve (AUC) and to assess inter-study heterogeneity. RESULTS: We included 26 studies, published from 2016. The median RQS total score was 8 out of 36 (22%, range 8-44%). Thirteen studies performed external validation. All studies reported AUC or accuracy, but only 4 (15%) performed calibration and decision curve analysis. No studies performed phantom analysis, cost-effectiveness analysis, and prospective validation. The overall TRIPOD adherence score was between 50% and 70% in 16 studies and below 50% in 10 studies. The pooled AUC was 0.78 (95% CI, 0.73-0.83, I2 = 94.1%) with a high inter-study heterogeneity. Studies with external validation and including only WHO-grade IV gliomas had significantly lower AUC values (0.65; 95% CI, 0.57-0.73, p < 0.01). CONCLUSIONS: Study RQS and adherence to TRIPOD guidelines was generally low. Radiomic prediction of MGMT methylation status showed great heterogeneity of results and lower performances in grade IV gliomas, which hinders its current implementation in clinical practice. CLINICAL RELEVANCE STATEMENT: MGMT promoter methylation status appears to be variably correlated with MRI radiomic features; radiomic models are not sufficiently robust to be integrated into clinical practice to accurately predict MGMT promoter methylation status in patients with glioma before surgery. KEY POINTS: • Adherence to the indications of TRIPOD guidelines was generally low, as was RQS total score. • MGMT promoter methylation status prediction with MRI radiomic features provided heterogeneous diagnostic accuracy results across studies. • Studies that included grade IV glioma only and performed external validation had significantly lower diagnostic accuracy than others.

Interval between contrast administration and T1-weighted MRI for cerebral adrenoleukodystrophy: a single-case observation.

In adrenoleukodystrophy (ALD), contrast enhancement (CE) is a disease activity marker, but there is uncertainty about the optimal delay, if any, between contrast injection and magnetic resonance imaging (MRI) acquisition to avoid false-negative results. We acquired axial two-dimensional (2D) and three-dimensional (3D) T1-weighted gradient-echo every 6 min from 0 to 36 min after contrast administration (gadobutrol 0.1 mmol/kg) in an ALD patient with enlarging white matter lesions and progressive neuropsychological symptoms, using a 3-T magnet. The image signal over time was qualitatively assessed and measured in two regions of interest. On 3D sequences, no definite CE was appreciated, whereas on 2D sequences, CE was noticed after 6 min and definitely evident after 12 min, when 73% of the maximum signal intensity was measured. In ALD subjects, contrast-enhanced 2D T1-weighted gradient-echo sequences acquired at least 10 min after contrast injection may be considered to reduce false negative results.Relevance statementOur report is the first attempt to find an optimal delay between contrast administration and T1-weighted acquisition in cALD patients in order to correctly detect disease activity and avoid false negative results.Key points• The optimal time between contrast injection and image acquisition for MRI of adrenoleukodystrophy is unknown.• Contrast enhancement predicts adrenoleukodystrophy progression and could help patient's selection for the therapy.• We acquired two post-contrast T1-GRE-2D/3D sequences several times to find the best injection-time.• T1-weighted 2D GRE resulted more sensitive than T1-weighted 3D GRE even after long intervals from injection.• A delay of about 10 min may minimize false negatives.

Deficits in naming pictures of objects are associated with glioma infiltration of the inferior longitudinal fasciculus: A study with diffusion MRI tractography, volumetric MRI, and neuropsychology.

It has been suggested that the inferior longitudinal fasciculus (ILF) may play an important role in several aspects of language processing such as visual object recognition, visual memory, lexical retrieval, reading, and specifically, in naming visual stimuli. In particular, the ILF appears to convey visual information from the occipital lobe to the anterior temporal lobe (ATL). However, direct evidence proving the essential role of the ILF in language and semantics remains limited and controversial. The first aim of this study was to prove that patients with a brain glioma damaging the left ILF would be selectively impaired in picture naming of objects; the second aim was to prove that patients with glioma infiltrating the ATL would not be impaired due to functional reorganization of the lexical retrieval network elicited by the tumor. We evaluated 48 right-handed patients with neuropsychological testing and magnetic resonance imaging (MRI) before and after surgery for resection of a glioma infiltrating aspects of the left temporal, occipital, and/or parietal lobes; diffusion tensor imaging (DTI) was acquired preoperatively in all patients. Damage to the ILF, inferior frontal occipital fasciculus (IFOF), uncinate fasciculus (UF), arcuate fasciculus (AF), and associated cortical regions was assessed by means of preoperative tractography and pre-/pos-toperative MRI volumetry. The association of fascicles damage with patients' performance in picture naming and three additional cognitive tasks, namely, verbal fluency (two verbal non-visual tasks) and the Trail Making Test (a visual attentional task), was evaluated. Nine patients were impaired in the naming test before surgery. ILF damage was demonstrated with tractography in six (67%) of these patients. The odds of having an ILF damage was 6.35 (95% CI: 1.27-34.92) times higher among patients with naming deficit than among those without it. The ILF was the only fascicle to be significantly associated with naming deficit when all the fascicles were considered together, achieving an adjusted odds ratio of 15.73 (95% CI: 2.30-178.16, p = .010). Tumor infiltration of temporal and occipital cortices did not contribute to increase the odd of having a naming deficit. ILF damage was found to be selectively associated with picture naming deficit and not with lexical retrieval assessed by means of verbal fluency. Early after surgery, 29 patients were impaired in naming objects. The association of naming deficit with percentage of ILF resection (assessed by 3D-MRI) was confirmed (beta = -56.78 ± 20.34, p = .008) through a robust multiple linear regression model; no significant association was found with damage of IFOF, UF or AF. Crucially, postoperative neuropsychological evaluation showed that naming scores of patients with tumor infiltration of the anterior temporal cortex were not significantly associated with the percentage of ILF damage (rho = .180, p > .999), while such association was significant in patients without ATL infiltration (rho = -.556, p = .004). The ILF is selectively involved in picture naming of objects; however, the naming deficits are less severe in patients with glioma infiltration of the ATL probably due to release of an alternative route that may involve the posterior segment of the AF. The left ILF, connecting the extrastriatal visual cortex to the anterior region of the temporal lobe, is crucial for lexical retrieval on visual stimulus, such as in picture naming. However, when the ATL is also damaged, an alternative route is released and the performance improves.

Development of Predictive Models for the Response of Vestibular Schwannoma Treated with Cyberknife®: A Feasibility Study Based on Radiomics and Machine Learning.

PURPOSE: to predict vestibular schwannoma (VS) response to radiosurgery by applying machine learning (ML) algorithms on radiomic features extracted from pre-treatment magnetic resonance (MR) images. METHODS: patients with VS treated with radiosurgery in two Centers from 2004 to 2016 were retrospectively evaluated. Brain T1-weighted contrast-enhanced MR images were acquired before and at 24 and 36 months after treatment. Clinical and treatment data were collected contextually. Treatment responses were assessed considering the VS volume variation based on pre- and post-radiosurgery MR images at both time points. Tumors were semi-automatically segmented and radiomic features were extracted. Four ML algorithms (Random Forest, Support Vector Machine, Neural Network, and extreme Gradient Boosting) were trained and tested for treatment response (i.e., increased or non-increased tumor volume) using nested cross-validation. For training, feature selection was performed using the Least Absolute Shrinkage and Selection Operator, and the selected features were used as input to separately build the four ML classification algorithms. To overcome class imbalance during training, Synthetic Minority Oversampling Technique was used. Finally, trained models were tested on the corresponding held out set of patients to evaluate balanced accuracy, sensitivity, and specificity. RESULTS: 108 patients treated with Cyberknife® were retrieved; an increased tumor volume was observed at 24 months in 12 patients, and at 36 months in another group of 12 patients. The Neural Network was the best predictive algorithm for response at 24 (balanced accuracy 73% ± 18%, specificity 85% ± 12%, sensitivity 60% ± 42%) and 36 months (balanced accuracy 65% ± 12%, specificity 83% ± 9%, sensitivity 47% ± 27%). CONCLUSIONS: radiomics may predict VS response to radiosurgery avoiding long-term follow-up as well as unnecessary treatment.

A discriminative event-based model for subtype diagnosis of sporadic Creutzfeldt-Jakob disease using brain MRI.

INTRODUCTION: Sporadic Creutzfeldt-Jakob disease (sCJD) comprises multiple subtypes (MM1, MM2, MV1, MV2C, MV2K, VV1, and VV2) with distinct disease durations and spatiotemporal cascades of brain lesions. Our goal was to establish the ante mortem diagnosis of sCJD subtype, based on patient-specific estimates of the spatiotemporal cascade of lesions detected by diffusion-weighted magnetic resonance imaging (DWI). METHODS: We included 488 patients with autopsy-confirmed diagnosis of sCJD subtype and 50 patients with exclusion of prion disease. We applied a discriminative event-based model (DEBM) to infer the spatiotemporal cascades of lesions, derived from the DWI scores of 12 brain regions assigned by three neuroradiologists. Based on the DEBM cascades and the prion protein genotype at codon 129, we developed and validated a novel algorithm for the diagnosis of the sCJD subtype. RESULTS: Cascades of MM1, MM2, MV1, MV2C, and VV1 originated in the parietal cortex and, following subtype-specific orderings of propagation, went toward the striatum, thalamus, and cerebellum; conversely, VV2 and MV2K cascades showed a striatum-to-cortex propagation. The proposed algorithm achieved 76.5% balanced accuracy for the sCJD subtype diagnosis, with low rater dependency (differences in accuracy of ± 1% among neuroradiologists). DISCUSSION: Ante mortem diagnosis of sCJD subtype is feasible with this novel data-driven approach, and it may be valuable for patient prognostication, stratification in targeted clinical trials, and future therapeutics. HIGHLIGHTS: Subtype diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD) is achievable with diffusion MRI. Cascades of diffusion MRI abnormalities in the brain are subtype-specific in sCJD. We proposed a diagnostic algorithm based on cascades of diffusion MRI abnormalities and demonstrated that it is accurate. Our method may aid early diagnosis, prognosis, stratification in clinical trials, and future therapeutics. The present approach is applicable to other neurodegenerative diseases, enhancing the differential diagnoses.

Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning.

The WHO classification since 2016 confirms the importance of integrating molecular diagnosis for prognosis and treatment decisions of adult-type diffuse gliomas. This motivates the development of non-invasive diagnostic methods, in particular MRI, to predict molecular subtypes of gliomas before surgery. At present, this development has been focused on deep-learning (DL)-based predictive models, mainly with conventional MRI (cMRI), despite recent studies suggesting multi-shell diffusion MRI (dMRI) offers complementary information to cMRI for molecular subtyping. The aim of this work is to evaluate the potential benefit of combining cMRI and multi-shell dMRI in DL-based models. A model implemented with deep residual neural networks was chosen as an illustrative example. Using a dataset of 146 patients with gliomas (from grade 2 to 4), the model was trained and evaluated, with nested cross-validation, on pre-operative cMRI, multi-shell dMRI, and a combination of the two for the following classification tasks: (i) IDH-mutation; (ii) 1p/19q-codeletion; and (iii) three molecular subtypes according to WHO 2021. The results from a subset of 100 patients with lower grades gliomas (2 and 3 according to WHO 2016) demonstrated that combining cMRI and multi-shell dMRI enabled the best performance in predicting IDH mutation and 1p/19q codeletion, achieving an accuracy of 75 ± 9% in predicting the IDH-mutation status, higher than using cMRI and multi-shell dMRI separately (both 70 ± 7%). Similar findings were observed for predicting the 1p/19q-codeletion status, with the accuracy from combining cMRI and multi-shell dMRI (72 ± 4%) higher than from each modality used alone (cMRI: 65 ± 6%; multi-shell dMRI: 66 ± 9%). These findings remain when we considered all 146 patients for predicting the IDH status (combined: 81 ± 5% accuracy; cMRI: 74 ± 5%; multi-shell dMRI: 73 ± 6%) and for the diagnosis of the three molecular subtypes according to WHO 2021 (combined: 60 ± 5%; cMRI: 57 ± 8%; multi-shell dMRI: 56 ± 7%). Together, these findings suggest that combining cMRI and multi-shell dMRI can offer higher accuracy than using each modality alone for predicting the IDH and 1p/19q status and in diagnosing the three molecular subtypes with DL-based models.

Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI.

The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients' age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8-74.6%) accuracy and 0.572 (0.439-0.705) for AUC. The performances did not change when the patients' age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.

Application of QBA to Assess the Emotional State of Horses during the Loading Phase of Transport.

To identify feasible indicators to evaluate animals' emotional states as a parameter to assess animal welfare, the present study aimed at investigating the accuracy of free choice profiling (FCP) and fixed list (FL) approach of Qualitative Behaviour Assessment (QBA) in horses during the loading phase of transport. A total of 13 stakeholders were trained to score 2 different sets of videos of mixed breed horses loaded for road transport, using both FCP and FL, in 2 sessions. Generalized Procustes Analysis (GPA) consensus profile explained a higher percentage of variation (80.8%) than the mean of 1000 randomized profiles (41.2 ± 1.6%; p = 0.001) for the FCP method, showing an excellent inter-observer agreement. GPA identified two main factors, explaining 65.1% and 3.7% of the total variation. Factor 1 ranging from 'anxious/ to 'calm/relaxed', described the valence of the horses' emotional states. Factor 2, ranging from 'bright' to 'assessing/withdrawn', described the arousal. As for FL, Principal Component Analysis (PCA) first and second components (PC1 and PC2, respectively), explaining on average 59.8% and 12.6% of the data variability, had significant agreement between observers. PC1 ranges from relaxed/confident to anxious/frightened, while PC2 from alert/inquisitive to calm. Our study highlighted the need for the use of descriptors specifically selected, throughout a prior FCP process for the situation we want to evaluate to get a good QBA accuracy level.

Role of NODDI in the MRI Characterization of Hippocampal Abnormalities in Temporal Lobe Epilepsy: Clinico-histopathologic Correlations.

BACKGROUND AND OBJECTIVES: The identification of possible hippocampal alterations is a crucial point for the diagnosis and therapy of patients with unilateral temporal lobe epilepsy (TLE). This study aims to investigate the role of neurite orientation dispersion and density imaging (NODDI) compared to diffusion tensor imaging (DTI) in the comprehension of hippocampal microstructure in TLE. METHODS: DTI and NODDI metrics were calculated in the hippocampi of adult patients with TLE, with and without histology-confirmed hippocampal sclerosis (HS), and in age/sex-matched healthy controls (HC). Diffusion metrics and hippocampal volumes of the pathologic side were compared within participants and between participants among the HS, non-HS, and HC groups. Diffusion metrics were also correlated with hippocampal volume and patients' clinical features. After surgery, hippocampal specimens were processed for neuropathology examinations. RESULTS: Fifteen patients with TLE (9 with and 6 without HS) and 11 HC were included. Hippocampal analyses resulted in a significant increase in fractional anisotropy (FA) and mean diffusivity (MD; mm2/s × 10-3) and decrease in orientation dispersion index (ODI) comparing the pathologic side of patients with HS and their relative nonpathologic side (0.203 vs 0.183, 0.825 vs 0.724, 0.366 vs 0.443, respectively), the pathologic side of patients without HS (0.203 vs 0.169, 0.825 vs 0.745, 0.366 vs 0.453, respectively), and HC (0.203 vs 0.172, 0.825 vs 0.729, 0.366 vs 0.447, respectively). Moreover, neurite density (ND) was significantly decreased comparing both hippocampi of patients with HS (0.416 vs 0.460). A significant increase in free-water isotropic volume fraction (fiso) was found in the comparison of pathologic hippocampi of patients with HS and nonpathologic hippocampi of patients with HS (0.323 vs 0.258) and HC (0.323 vs 0.226). Hippocampal volume of all patients with TLE negatively correlated with MD (r = -0.746, p = 0.0145) and positively correlated with ODI (r = 0.719, p = 0.0145). Fiso and ND of sclerotic hippocampi positively correlated with disease duration (r = 0.684, p = 0.0424 and r = 0.670, p = 0.0486, respectively). Immunohistochemistry in sclerotic hippocampal specimens revealed neuronal loss in the pyramidal layer and fiber reorganization at the level of stratum lacunosum-moleculare, confirming ODI and ND metrics. DISCUSSION: This study shows the capability of diffusion MRI metrics to detect hippocampal microstructural alterations. Among them, ODI seems to better highlight the fiber reorganization observed by neuropathology in sclerotic hippocampi.

Over three decades of natural history of limb girdle muscular dystrophy type R1/2A and R2/2B: Mathematical modelling of a multifactorial study.

We aimed to describe the natural history of Limb Girdle Muscular Dystrophy type 2A and 2B over more than three decades by considering muscular strength, motor, cardiac and respiratory function. 428 visits of nineteen 2A and twenty 2B patients were retrospectively analysed through a regression model to create the curves of evolution with disease duration of muscle strength (through Medical Research Council grading), motor function measure scale (D1, D2 and D3 domains) and cardio-pulmonary function tests. Clinically relevant muscular and motor function alterations occurred after the first decade of disease, while mild respiratory function alterations started after the second, with preserved cardiac function. Although type 2A showed relatively stronger distal lower limb muscles, while type 2B started with relatively stronger upper limb muscles, the corresponding motor functions were similar, becoming severely compromised after 25 years of disease. This was the longest retrospective study in types 2A and 2B. It defined curves of disease evolution not only from a neuromuscular, but also from functional, cardiac, and respiratory points of view, to be used to evaluate how the natural progression is changed by therapies. Due to slow disease progression, it was not possible to identify time sensitive endpoints.

Subtype Diagnosis of Sporadic Creutzfeldt-Jakob Disease with Diffusion Magnetic Resonance Imaging.

OBJECTIVE: Sporadic Creutzfeldt-Jakob disease (sCJD) comprises several subtypes as defined by genetic and prion protein characteristics, which are associated with distinct clinical and pathological phenotypes. To date, no clinical test can reliably diagnose the subtype. We established two procedures for the antemortem diagnosis of sCJD subtype using diffusion magnetic resonance imaging (MRI). METHODS: MRI of 1,458 patients referred to the National Prion Disease Pathology Surveillance Center were collected through its consultation service. One neuroradiologist blind to the diagnosis scored 12 brain regions and generated a lesion profile for each MRI scan. We selected 487 patients with autopsy-confirmed diagnosis of "pure" sCJD subtype and at least one positive diffusion MRI examination. We designed and tested two data-driven procedures for subtype diagnosis: the first procedure-prion subtype classification algorithm with MRI (PriSCA_MRI)-uses only MRI examinations; the second-PriSCA_MRI + Gen-includes knowledge of the prion protein codon 129 genotype, a major determinant of sCJD subtypes. Both procedures were tested on the first MRI and the last MRI follow-up. RESULTS: PriSCA_MRI classified the 3 most prevalent subtypes with 82% accuracy. PriSCA_MRI + Gen raised the accuracy to 89% and identified all subtypes. Individually, the 2 most prevalent sCJD subtypes, MM1 and VV2, were diagnosed with sensitivities up to 95 and 97%, respectively. The performances of both procedures did not change in 168 patients with longitudinal MRI studies when the last examination was used. INTERPRETATION: This study provides the first practical algorithms for antemortem diagnosis of sCJD subtypes. MRI diagnosis of subtype is likely to be attainable at early disease stages to prognosticate clinical course and design future therapeutic trials. ANN NEUROL 2021;89:560-572.

Nursing Management of Prone Positioning in Patients With COVID-19.

BACKGROUND: At the height of the coronavirus disease 2019 (COVID-19) pandemic, Italy had the highest number of deaths in Europe; most occurred in the Lombardy region. Up to 4% of patients with COVID-19 required admission to an intensive care unit because they developed a critical illness (eg, acute respiratory distress syndrome). Numerous patients with acute respiratory distress syndrome who had been admitted to the intensive care unit required rescue therapy like prone positioning. OBJECTIVE: To describe the respiratory management of and the extensive use of prone positioning in patients with COVID-19 at the intensive care unit hub in Lombardy, Italy. METHODS: A total of 89 patients (67% male; median age, 59 years [range, 23-80 years]) with confirmed COVID-19 who were admitted between February 23 and March 31, 2020, were enrolled in this quality improvement project. RESULTS: Endotracheal intubation was required in 86 patients (97%). Prone positioning was used as rescue therapy in 43 (48%) patients. Significantly more younger patients (age ≤ 59 years) were discharged alive (43 of 48 [90%]) than were older patients (age ≥ 60 years; 26 of 41 [63%]; P < .005). Among the 43 patients treated with prone ventilation, 15 (35% [95% CI, 21%-51%]) died in the intensive care unit, of which 10 (67%; P < .001) were older patients. CONCLUSIONS: Prone positioning is one strategy available for treating acute respiratory distress syndrome in patients with COVID-19. During this pandemic, prone positioning can be used extensively as rescue therapy, per a specific protocol, in intensive care units.

Prion propagation estimated from brain diffusion MRI is subtype dependent in sporadic Creutzfeldt-Jakob disease.

Sporadic Creutzfeldt-Jakob disease (sCJD) is a transmissible brain proteinopathy. Five main clinicopathological subtypes (sCJD-MM(V)1, -MM(V)2C, -MV2K, -VV1, and -VV2) are currently distinguished. Histopathological evidence suggests that the localisation of prion aggregates and spongiform lesions varies among subtypes. Establishing whether there is an initial site with detectable imaging abnormalities (epicentre) and an order of lesion propagation would be informative for disease early diagnosis, patient staging, management and recruitment in clinical trials. Diffusion magnetic resonance imaging (MRI) is the most-used and most-sensitive test to detect spongiform degeneration. This study was designed to identify, in vivo and for the first time, subtype-dependent epicentre and lesion propagation in the brain using diffusion-weighted images (DWI), in the largest known cross-sectional dataset of autopsy-proven subjects with sCJD. We estimate lesion propagation by cross-sectional DWI using event-based modelling, a well-established data-driven technique. DWI abnormalities of 594 autopsy-diagnosed subjects (448 patients with sCJD) were scored in 12 brain regions by 1 neuroradiologist blind to the diagnosis. We used the event-based model to reconstruct sequential orderings of lesion propagation in each of five pure subtypes. Follow-up data from 151 patients validated the estimated sequences. Results showed that epicentre and ordering of lesion propagation are subtype specific. The two most common subtypes (-MM1 and -VV2) showed opposite ordering of DWI abnormality appearance: from the neocortex to subcortical regions, and vice versa, respectively. The precuneus was the most likely epicentre also in -MM2 and -VV1 although at variance with -MM1, abnormal signal was also detected early in cingulate and insular cortices. The caudal-rostral sequence of lesion propagation that characterises -VV2 was replicated in -MV2K. Combined, these data-driven models provide unprecedented dynamic insights into subtype-specific epicentre at onset and propagation of the pathologic process, which may also enhance early diagnosis and enable disease staging in sCJD.

Evaluation of a New Criterion for Detecting Prion Disease With Diffusion Magnetic Resonance Imaging.

Importance: Early diagnosis is a requirement for future treatment of prion diseases. Magnetic resonance imaging (MRI) with diffusion-weighted images and improved real-time quaking-induced conversion (RT-QuIC) in cerebrospinal fluid (CSF) have emerged as reliable tests. Objectives: To assess the sensitivity and specificity of diffusion MRI for the diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD) with a new criterion (index test) of at least 1 positive brain region among the cortex of the frontal, parietal, temporal, and occipital lobes; the caudate; the putamen; and the thalamus. Design, Setting, and Participants: This diagnostic study with a prospective and a retrospective arm was performed from January 1, 2003, to October 31, 2018. MRIs were collected from 1387 patients with suspected sCJD consecutively referred to the National Prion Disease Pathology Surveillance Center as part of a consultation service. Intervention: Magnetic resonance imaging. Four neuroradiologists blinded to the diagnosis scored the MRIs of 200 randomly selected patients. One neuroradiologist scored the MRIs of all patients. Main Outcomes and Measures: Sensitivity and specificity of the index test compared with currently used criteria and CSF diagnostic (improved RT-QuIC, 14-3-3, and tau CSF tests). Results: A total of 872 patients matched the inclusion criteria (diffusion MRI and autopsy-confirmed diagnosis), with 619 having sCJD, 102 having other prion diseases, and 151 having nonprion disease. The primary analysis included 200 patients (mean [SD] age, 63.6 [12.9] years; 100 [50.0%] male). Sensitivity of the index test of 4 neuroradiologists was 90% to 95% and superior to sensitivity of current MRI criteria (69%-76%), whereas specificity was 90% to 100% and unchanged. Interrater reliability of the 4 neuroradiologists was high (κ = 0.81), and individual intrarater reliability was excellent (κ ≥0.87). The sensitivity of the index test of 1 neuroradiologist for 770 patients was 92.1% (95% CI, 89.7%-94.1%) and the specificity was 97.4% (95% CI, 93.4%-99.3%) compared with a sensitivity of 69.8% (95% CI, 66.0%-73.4%; P < .001) and a specificity of 98.0% (95% CI, 94.3%-99.6%; P > .99) according to the current criteria. For 88 patients, index test sensitivity (94.9%; 95% CI, 87.5%-98.6%) and specificity (100%; 95% CI, 66.4%-100%) were similar to those of improved RT-QuIC (86.1% [95% CI, 76.5%-92.8%] and 100% [95% CI, 66.4%-100%], respectively). Lower specificities were found for 14-3-3 and tau CSF tests in 452 patients. Conclusions and Relevance: In this study, the diagnostic performance of diffusion MRI with the new criterion was superior to that of current standard criteria and similar to that of improved RT-QuIC. These results may have important clinical implications because MRI is noninvasive and typically prescribed at disease presentation.

A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary Study.

Coccidiosis is still one of the major parasitic infections in poultry. It is caused by protozoa of the genus Eimeria, which cause concrete economic losses due to malabsorption, bad feed conversion rate, reduced weight gain, and increased mortality. The greatest damage is registered in commercial poultry farms because birds are reared together in large numbers and high densities. Unfortunately, these enteric pathologies are not preventable, and their diagnosis is only available when the disease is full-blown. For these reasons, the preventive use of anticoccidials-some of these with antimicrobial action-is a common practice in intensive farming, and this type of management leads to the release of drugs in the environment which contributes to the phenomenon of antibiotic resistance. Due to the high relevance of this issue, the early detection of any health problem is of great importance to improve animal welfare in intensive farming. Three prototypes, previously calibrated and adjusted, were developed and tested in three different experimental poultry farms in order to evaluate whether the system was able to identify the coccidia infection in intensive poultry farms early. For this purpose, a data-driven machine learning algorithm was built, and specific critical values of volatile organic compounds (VOCs) were found to be associated with abnormal levels of oocystis count at an early stage of the disease. This result supports the feasibility of building an automatic data-driven machine learning algorithm for an early warning of coccidiosis.