Autosomal dominant optic neuropathy and sensorineual hearing loss associated with a novel mutation of WFS1

PURPOSE: To describe the phenotype of a novel Wolframin (WFS1) mutation in a family with autosomal dominant optic neuropathy and deafness. The study is designed as a retrospective observational case series. METHODS: Seven members of a Dutch family underwent ophthalmological, otological, and genetical examinations in one institution. Fasting serum glucose was assessed in the affected family members. RESULTS: All affected individuals showed loss of neuroretinal rim of the optic nerve at fundoscopy with enlarged blind spots at perimetry. They showed a red-green color vision defect at color vision tests and deviations at visually evoked response tests. The audiograms of the affected individuals showed hearing loss and were relatively flat. The unaffected individuals showed no visual deviations or hearing impairment. The affected family members had no glucose intolerance. Leber hereditary optic neuropathy (LHON) mitochondrial mutations and mutations in the Optic atrophy-1 gene (OPA1) were excluded. In the affected individuals, a novel missense mutation c.2508G>C (p.Lys836Asn) in exon 8 of WFS1 was identified. CONCLUSIONS: This study describes the phenotype of a family with autosomal dominant optic neuropathy and hearing impairment associated with a novel missense mutation in WFS1

Radiomics-Based Prediction of Long-Term Treatment Response of Vestibular Schwannomas Following Stereotactic Radiosurgery

OBJECTIVE: Stereotactic radiosurgery (SRS) is one of the treatment modalities for vestibular schwannomas (VSs). However, tumor progression can still occur after treatment. Currently, it remains unknown how to predict long-term SRS treatment outcome. This study investigates possible magnetic resonance imaging (MRI)-based predictors of long-term tumor control following SRS. STUDY DESIGN: Retrospective cohort study. SETTING: Tertiary referral center. PATIENTS: Analysis was performed on a database containing 735 patients with unilateral VS, treated with SRS between June 2002 and December 2014. Using strict volumetric criteria for long-term tumor control and tumor progression, a total of 85 patients were included for tumor texture analysis. INTERVENTION(S): All patients underwent SRS and had at least 2 years of follow-up. MAIN OUTCOME MEASURE(S): Quantitative tumor texture features were extracted from conventional MRI scans. These features were supplied to a machine learning stage to train prediction models. Prediction accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC) are evaluated. RESULTS: Gray-level co-occurrence matrices, which capture statistics from specific MRI tumor texture features, obtained the best prediction scores: 0.77 accuracy, 0.71 sensitivity, 0.83 specificity, and 0.93 AUC. These prediction scores further improved to 0.83, 0.83, 0.82, and 0.99, respectively, for tumors larger than 5 cm. CONCLUSIONS: Results of this study show the feasibility of predicting the long-term SRS treatment response of VS tumors on an individual basis, using MRI-based tumor texture features. These results can be exploited for further research into creating a clinical decision support system, facilitating physicians, and patients to select a personalized optimal treatment strategy

Heterozygous missense variants of LMX1A lead to nonsyndromic hearing impairment and vestibular dysfunction

Unraveling the causes and pathomechanisms of progressive disorders is essential for the development of therapeutic strategies. Here, we identified heterozygous pathogenic missense variants of LMX1A in two families of Dutch origin with progressive nonsyndromic hearing impairment (HI), using whole exome sequencing. One variant, c.721G > C (p.Val241Leu), occurred de novo and is predicted to affect the homeodomain of LMX1A, which is essential for DNA binding. The second variant, c.290G > C (p.Cys97Ser), predicted to affect a zinc-binding residue of the second LIM domain that is involved in protein–protein interactions. Bi-allelic deleterious variants of Lmx1a are associated with a complex phenotype in mice, including deafness and vestibular defects, due to arrest of inner ear development. Although Lmx1a mouse mutants demonstrate neurological, skeletal, pigmentation and reproductive system abnormalities, no syndromic features were present in the participating subjects of either family. LMX1A has previously been suggested as a candidate gene for intellectual disability, but our data do not support this, as affected subjects displayed normal cognition. Large variability was observed in the age of onset (a)symmetry, severity and progression rate of HI. About half of the affected individuals displayed vestibular dysfunction and experienced symptoms thereof. The late-onset progressive phenotype and the absence of cochleovestibular malformations on computed tomography scans indicate that heterozygous defects of LMX1A do not result in severe developmental abnormalities in humans. We propose that a single LMX1A wild-type copy is sufficient for normal development but insufficient for maintenance of cochleovestibular function. Alternatively, minor cochleovestibular developmental abnormalities could eventually lead to the progressive phenotype seen in the families

Lateralization of facial emotion processing and facial paresis in Vestibular Schwannoma patients

Objective: This study investigates whether there exist differences in lateralization of facial emotion processing in patients suffering from Vestibular Schwannoma (VS) based on the presence of a facial paresis and their degree of facial functioning as measured by the House Brackmann Grading scale (HBG). Methods: Forty-four VS patients, half of them with a facial paresis and half of them without a facial paresis, rated how emotive they considered images of faces showing emotion in the left versus right visual field. Stimuli consisted of faces with a neutral half and an emotional (happy or angry) half. The study had a mixed design with emotional expression (happy vs. angry) and emotional half (left vs. right visual field) of the faces as repeated measures, and facial paresis (present vs. absent) and HBG as between subjects’ factors. The visual field bias was the main dependent variable. Results: In line with typical findings in the normal population, a left visual field bias showed in the current sample: patients judged emotional expressions shown in the left visual field as more emotive than those shown in the right visual field. No differences in visual field bias showed based on the presence of a facial paresis nor based on patients’ HBG. Conclusion: VS patients show a left visual field bias when processing facial emotion. No differences in lateralization showed based on the presence of a facial paresis or on patients’ HBG. Based on this study, facial paresis thus does not affect the lateralization of facial emotion processing in patients with VS

Computer-aided prediction of tumor response one year after Gamma Knife radiosurgery on vestibular schwannoma using deep learning

Introduction Approximately one third of all vestibular schwannomas (VS) show a volumetric increase one year after Gamma Knife radiosurgery (GKRS) treatment, which can indicate transient tumor enlargement (TTE) and may cause symptoms due to increased mass effect. It is therefore clinically relevant to be able to predict tumor response relatively early after GKRS. Computer-aided prediction models using classical machine learning have already shown promising results in predicting GKRS tumor response in VS patients. The objective of this study is to investigate the feasibility of the modern machine learning method of deep learning to predict early tumor response after GKRS. Methods A total of 1118 contrast-enhanced T1 MRI scans, obtained during GKRS treatment of VS patients, were used. These images were cropped to include only the tumor and its direct surroundings using a bounding box. This data was subsequently used to train a three-dimensional autoencoder model to extract 256 tumor image features automatically. The learned features were subsequently employed in a neural network classifier to predict tumor response based on the treatment scan. For this classification task, tumor enlargement and shrinkage were defined as at least a 15% volumetric increase and decrease with respect to the tumor volume at the time of GKRS. Furthermore, patients without a one-year follow-up scan were excluded, as well as patients with tumors smaller than 0.25cc due to the lack of sufficient image features. Results The exclusion criteria resulted in the inclusion of 395 patients with a mean one-year follow-up time of 12.14±0.70 months. Using our volumetric criteria, enlargement was observed in 122 (30.9%) tumors and shrinkage in 273 (69.1%). The prediction model achieved a sensitivity and specificity of 72.3% and 82.5%, respectively. Conclusion The specificity shows that the model is more tuned to predicting tumor shrinkage. Although tumor enlargement prediction performance (i.e., sensitivity) is lower, the overall performance of the model indicates that deep learning can be of predictive value for tumor response in VS patients. These methods should therefore be further investigated to improve upon the presented results

Inter-observer variability in volumetric annotation of vestibular schwannomas in contrast-enhanced T1-weighted MRI scans

ObjectiveAccurate and reliable longitudinal monitoring of vestibular schwannomas (VSs) is of importance in VS management, in particular for wait-and-scan protocols and after radiosurgical treatment. However, inter-observer variability is a significant source of error in volumetric annotation. In current clinical practice and research, a smallest detectable change (SDC) of 20% is widely used [1]. However, we hypothesize that the SDC will heavily depend on tumor volume, making the 20%-definition unsuitable.MethodsAn inter-observer variability study was conducted with 4 experienced observers. A total of 96 patients were selected from our own extensive volumetrically annotated VS database of patients treated with Gamma Knife radiosurgery (GKRS). Of each patient, the first follow-up MRI scan (axial contrast-enhanced T1-weighted image) was used. The patients were selected based on the natural,distribution of VS volumes seen in both wait-and-scan and radiosurgery cohorts. This resulted in a median volume of 1017 mm3 (IQR: 280 – 3329). Additionally, 11 (11.5%) cystic tumors were included. Tumors were volumetrically annotated using the GKRS treatment planning software. SDC values were calculated and analyzed.ResultsThe median of the average volumes of the 4 observers was found to be 1027 mm3 (IQR: 279 – 3436). Figure 1 shows the SDC results, split between the four volume quartiles. The maximum SDC (95th percentile) for each quartile, from small to large, was found to be 30%, 9.3%, 7.3%, and 4.5%. This shows a negative relation between tumor volume and SDC. The single outlier in the fourth quartile is a cystic tumor.ConclusionsIt can be argued that the current SDC definition of 20% is unsuitable, because annotation of smaller tumors is more error-prone than previously thought. As such, smaller VSs require a higher SDC. Conversely, annotation of larger tumors shows less variation among observers, thereby indicating that a lower growth threshold can be used. Furthermore, there is evidence that the SDC is higher in cystic tumors. All in all, to ensure reliable VS monitoring, a new standard for volumetric growth should be proposed for clinical and research purposes.References[1] Katherine A. Lees, Nicole M. Tombers, Michael J. Link, Colin L. Driscoll, Brian A. Neff, Jamie J. Van Gompel, John I. Lane, Christine M. Lohse, Matthew L. Carlson, (2018), Natural History of Sporadic Vestibular Schwannoma: A Volumetric Study of Tumor Growth, Otolaryngology–Head and Neck Surgery, 535-542, 159.3 <br/

Validation and comparison of a single and multi-institutional automated segmentation algorithm for vestibular schwannoma from contrast-enhanced T1-weighted MRI scans

Introduction In an earlier study by King’s College London (KCL), a framework for the automatic segmentation of vestibular schwannomas (VS) from MRI scans was proposed. They demonstrated that artificial intelligence is capable of annotating and calculating VS tumor volumes, which can benefit tumor progression monitoring. In this study, their algorithm is validated using data from another institution. The obtained results are subsequently compared to a multi-institutional model, in which the data from both centers is combined. Methods In addition to the original KCL dataset of 375 contrast-enhanced T1-weighted (ceT1) MRI scans, a total of 1,115 ceT1 scans of individual VS patients from the ETZ hospital in Tilburg, the Netherlands, were used. All tumors were manually annotated by a neurosurgeon at time of treatment. Employing the original 2.5D convolutional neural network, two different models were subsequently trained. First, one model is trained solely on 176 scans from the KCL dataset, using an additional 20 and 46 scans for hyper-parameter tuning and internal validation, respectively. This single-institution model is externally validated on the entire ETZ dataset. The second model employed 242 scans from the KCL dataset and all of the ETZ data from before 2015 (733 scans), with the remainder of the ETZ data equally distributed between a tuning and validation set. The remaining 133 scans from the KCL dataset were also used for validation. Results The single-institutional model achieved internal and external validation mean Dice scores of 92.0±5.1% and 64.5±32.4%, respectively. The external validation set included 175 scans where the model failed to recognize any VS, resulting in a Dice score of zero for these scans, which skews the results. The second model yielded validation mean dice scores of 92.5±5.1% for the ETZ scans and 89.1±9.6% for the KCL scans. During validation, most of the low-scoring segmentations are of tumors that are either very small or cystic. Conclusions The significant difference in performance between internal and external validation of the first model, thereby surmising a poor generalization, can be explained by variations in spatial resolution and image acquisition parameters between the two datasets. The second model shows performances approaching inter-observer variability of human annotators (cf. 93.8±3.1%). The results show that creating a robust and well-generalizing model from single-institution data is challenging. However, the multi-institutional results empower the earlier demonstrated capabilities of the framework for the automatic segmentation of VS