Complex structural variation and nonsense variant in trans cause VPS50-related disorder.

Homozygous VPS50 variants have been previously described in two unrelated patients with a neurodevelopmental disorder with microcephaly, seizures and neonatal cholestasis. VPS50 encodes a subunit that is unique to the heterotetrameric endosome-associated recycling protein (EARP) complex. The other subunits of the EARP complex, such as VPS51, VPS52 and VPS53, are also shared by the Golgi-associated retrograde protein complex. We report on an 18-month-old female patient with biallelic VPS50 variants. She carried a paternally inherited heterozygous nonsense c.13A>T; p.(Lys5*) variant. By long-read genome sequencing, we characterised a structural variant with a 4.3 Mb inversion flanked by deletions at both breakpoints on the maternal allele. The ~428 kb deletion at the telomeric inversion breakpoint encompasses the entire VPS50 gene. We demonstrated a deficiency of VPS50 in patient-derived fibroblasts, confirming the loss-of-function nature of both VPS50 variants. VPS53 and VPS52 protein levels were significantly reduced and absent, respectively, in fibroblasts of the patient. These data show that VPS50 and/or EARP deficiency and the associated functional defects underlie the phenotype in patients with VPS50 pathogenic variants. The VPS50-related core phenotype comprises severe developmental delay, postnatal microcephaly, hypoplastic corpus callosum, neonatal low gamma-glutamyl transpeptidase cholestasis and failure to thrive. The disease is potentially fatal in early childhood.

Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference.

Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here, we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high-resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in GBM patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and, thus, is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design.

Predicting conversion from clinically isolated syndrome to multiple sclerosis-An imaging-based machine learning approach.

Magnetic resonance imaging (MRI) scans play a pivotal role in the evaluation of patients presenting with a clinically isolated syndrome (CIS), as these may depict brain lesions suggestive of an inflammatory cause. We hypothesized that it is possible to predict the conversion from CIS to multiple sclerosis (MS) based on the baseline MRI scan by studying image features of these lesions. We analyzed 84 patients diagnosed with CIS from a prospective observational single center cohort. The patients were followed up for at least three years. Conversion to MS was defined according to the 2010 McDonald criteria. Brain lesions were segmented based on 3D FLAIR and 3D T1 images. We generated brain lesion masks by a computer assisted manual segmentation. We also generated a set of automated segmentations using the Lesion Segmentation Toolbox for SPM to assess the influence of different segmentation methods. Shape and brightness features were automatically calculated from the segmented masks and used as input data to train an oblique random forest classifier. Prediction accuracies of the resulting model were validated through a three-fold cross-validation. Conversion from CIS to MS occurred in 66 of 84 patients (79%). The conversion or non-conversion was predicted correctly in 71 patients based on shape features derived from the computer assisted manual segmentation masks (84.5% accuracy). This predictor was more accurate than predicting conversion using dissemination in space at baseline according to the 2010 McDonald criteria (75% accuracy). While shape features strongly contributed to the accuracy of the predictor, including intensity features did not further improve performance. As patients who convert to definite MS benefit from early treatment, an early classification model is highly desirable. Our study shows that shape parameters of lesions can contribute to predicting the future course of CIS patients more accurately.

Diffusion tensor image features predict IDH genotype in newly diagnosed WHO grade II/III gliomas.

We hypothesized that machine learning analysis based on texture information from the preoperative MRI can predict IDH mutational status in newly diagnosed WHO grade II and III gliomas. This retrospective study included in total 79 consecutive patients with a newly diagnosed WHO grade II or III glioma. Local binary pattern texture features were generated from preoperative B0 and fractional anisotropy (FA) diffusion tensor imaging. Using a training set of 59 patients, a single hidden layer neural network was then trained on the texture features to predict IDH status. The model was validated based on the prediction accuracy calculated in a previously unseen set of 20 gliomas. Prediction accuracy of the generated model was 92% (54/59 cases; AUC = 0.921) in the training and 95% (19/20; AUC = 0.952) in the validation cohort. The ten most important features were comprised of tumor size and both B0 and FA texture information, underlining the joint contribution of imaging data to classification. Machine learning analysis of DTI texture information and tumor size reliably predicts IDH status in preoperative MRI of gliomas. Such information may increasingly support individualized surgical strategies, supplement pathological analysis and highlight the potential of radiogenomics.

Automated segmentation reveals silent radiographic progression in adult-onset vanishing white-matter disease.

Adult-onset vanishing white-matter disease (VWM) is a rare autosomal recessive disease with neurological symptoms such as ataxia and paraparesis, showing extensive white-matter hyperintensities (WMH) on magnetic resonance (MR) imaging. Besides symptom-specific scores like the International Cooperative Ataxia Rating Scale (ICARS), there is no established tool to monitor disease progression. Because of extensive WMH, visual comparison of MR images is challenging. Here, we report the results of an automated method of segmentation to detect alterations in T2-weighted fluid-attenuated-inversion-recovery (FLAIR) sequences in a one-year follow-up study of a clinically stable patient with genetically diagnosed VWM. Signal alterations in MR imaging were quantified with a recently published WMH segmentation method by means of extreme value distribution (EVD). Our analysis revealed progressive FLAIR alterations of 5.84% in the course of one year, whereas no significant WMH change could be detected in a stable multiple sclerosis (MS) control group. This result demonstrates that automated EVD-based segmentation allows a precise and rapid quantification of extensive FLAIR alterations like in VWM and might be a powerful tool for the clinical and scientific monitoring of degenerative white-matter diseases and potential therapeutic interventions.

A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation--With Application to Tumor and Stroke.

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the extended discriminative -discriminative model to be one of the top ranking methods in the BRATS evaluation.