Longitudinal Evaluation of Cerebellar Signs of H-ABC Tubulinopathy in a Patient and in the taiep Model.

Hypomyelination with atrophy of the basal ganglia and cerebellum (H-ABC) is a central neurodegenerative disease due to mutations in the tubulin beta-4A (TUBB4A) gene, characterized by motor development delay, abnormal movements, ataxia, spasticity, dysarthria, and cognitive deficits. Diagnosis is made by integrating clinical data and radiological signs. Differences in MRIs have been reported in patients that carry the same mutation; however, a quantitative study has not been performed so far. Our study aimed to provide a longitudinal analysis of the changes in the cerebellum (Cb), corpus callosum (CC), ventricular system, and striatum in a patient suffering from H-ABC and in the taiep rat. We correlated the MRI signs of the patient with the results of immunofluorescence, gait analysis, segmentation of cerebellum, CC, and ventricular system, performed in the taiep rat. We found that cerebellar and callosal changes, suggesting a potential hypomyelination, worsened with age, in concomitance with the emergence of ataxic gait. We also observed a progressive lateral ventriculomegaly in both patient and taiep, possibly secondary to the atrophy of the white matter. These white matter changes are progressive and can be involved in the clinical deterioration. Hypomyelination with atrophy of the basal ganglia and cerebellum (H-ABC) gives rise to a spectrum of clinical signs whose pathophysiology still needs to be understood.

Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning.

Purpose: To determine and characterize the radiomics features from structural MRI (MPRAGE) and Diffusion Tensor Imaging (DTI) associated with the presence of mild traumatic brain injuries on student athletes with post-concussive syndrome (PCS). Material and Methods: 122 student athletes (65 M, 57 F), median (IQR) age 18.8 (15-20) years, with a mixed level of play and sports activities, with a known history of concussion and clinical PCS, and 27 (15 M, 12 F), median (IQR) age 20 (19, 21) years, concussion free athlete subjects were MRI imaged in a clinical MR machine. MPRAGE and DTI-FA and DTI-ADC images were used to extract radiomic features from white and gray matter regions within the entire brain (2 ROI) and the eight main lobes of the brain (16 ROI) for a total of 18 analyzed regions. Radiomic features were divided into five different data sets used to train and cross-validate five different filter-based Support Vector Machines. The top selected features of the top model were described. Furthermore, the test predictions of the top four models were ensembled into a single average prediction. The average prediction was evaluated for the association to the number of concussions and time from injury. Results: Ninety-one PCS subjects passed inclusion criteria (91 Cases, 27 controls). The average prediction of the top four models had a sensitivity of 0.80, 95% CI: [0.71, 0.88] and specificity of 0.74 95%CI [0.54, 0.89] for distinguishing subjects from controls. The white matter features were strongly associated with mTBI, while the whole-brain analysis of gray matter showed the worst association. The predictive index was significantly associated with the number of concussions (p < 0.0001) and associated with the time from injury (p < 0.01). Conclusion: MRI Radiomic features are associated with a history of mTBI and they were successfully used to build a predictive machine learning model for mTBI for subjects with PCS associated with a history of one or more concussions.

Prospective Assessment of No Evidence of Disease Activity-4 Status in Early Disease Stages of Multiple Sclerosis in Routine Clinical Practice.

Background: In relapsing-remitting multiple sclerosis, no evidence of disease activity-3 (NEDA-3) is defined as no relapses, no disability progression and no MRI activity. NEDA-4 status is defined as meeting all NEDA-3 criteria plus having an annualized brain volume loss (a-BVL) of ≤0.4%. Prospective real-world studies presenting data on NEDA-4 are scarce. Objective: To determine the proportion of patients failing to meet one or more NEDA-4 criteria and the contribution of each component to this failure. Methods: Forty-eight patients were followed for 12 months. Structural image evaluation, using normalization, of atrophy was used to assess a-BVL. Results: The patients had a mean age of 33.0 years (range 18-57), disease duration of 1.7 years (0.4-4) and Expanded Disability Status Scale score of 1.3 (0-4); 71% were women. All patients were on disease-modifying therapies. During follow-up, 21% of the patients had at least one relapse, 21% had disability progression, 8% had new T2 lesions, and 10% had gadolinium-enhanced lesions. Fifty-eight percent (28/48) achieved NEDA-3 status. a-BVL of >0.4% was observed in 52% (25/48). Only 29% (14/48) achieved NEDA-4 status. Conclusion: a-BVL is a good marker to detect subclinical disease activity. a-BVL is parameter to continue investigating for guiding clinical practice in relapsing-remitting multiple sclerosis.

Prostate cancer discrimination in the peripheral zone with a reduced field-of-view T(2)-mapping MRI sequence.

OBJECTIVES: To evaluate the performance of T2 mapping in discriminating prostate cancer from normal prostate tissue in the peripheral zone using a practical reduced field-of-view MRI sequence requiring less than 3 minutes of scan time. MATERIALS AND METHODS: Thirty-six patients with biopsy-proven peripheral zone prostate cancer without prior treatment underwent routine multiparametric MRI at 3.0T with an endorectal coil. An Inner-Volume Carr-Purcell-Meiboom-Gill imaging sequence that required 2.8 minutes to obtain data for quantitative T2 mapping covering the entire prostate gland was added to the routine multiparametric protocol. Suspected cancer (SC) and suspected healthy (SH) tissue in the peripheral zone were identified in consensus by three radiologists and were correlated with available biopsy results. Differences in mean T2 values in SC and SH regions-of-interest (ROIs) were tested for significance using unpaired Student's two-tailed t-test. The area under the receiver operating characteristic curve was used to assess the optimal threshold T2 value for cancer discrimination. RESULTS: ROI analyses revealed significantly (p<0.0001) shorter T2 values in SC (85.4±12.3ms) compared to SH (169.6±38.7ms). An estimated T2 threshold of 99ms yielded a sensitivity of 92% and a specificity of 97% for prostate cancer discrimination. CONCLUSIONS: Quantitative values derived from this clinically practical T2-mapping sequence allow high precision discrimination between healthy and cancerous peripheral zone in the prostate.