Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering

Background and ObjectivesRecent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed, and the question arises of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers.MethodsWe used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups.ResultsWe included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters.DiscussionUsing a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset REM periods in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features

Calcul de dose à partir d’images Cone Beam CT : état de l’art

National audienceIn external beam radiotherapy, the dose planning is currently based on computed tomography (CT) images. A relation between Hounsfield numbers and electron densities (or mass densities) is necessary for dose calculation taking heterogeneities into account. In image-guided radiotherapy process, the cone beam CT is classically used for tissue visualization and registration. Cone beam CT for dose calculation is also attractive in dose reporting/monitoring perspectives and particularly in a context of dose-guided adaptive radiotherapy. The accuracy of cone beam CT-based dose calculation is limited by image characteristics such as quality, Hounsfield numbers consistency and restrictive sizes of volume acquisition. The analysis of the literature identifies three kinds of strategies for cone beam CT-based dose calculation: establishment of Hounsfield numbers versus densities curves, density override to regions of interest, and deformable registration between CT and cone beam CT images. Literature results show that discrepancies between the reference CT-based dose calculation and the cone beam CT-based dose calculation are often lower than 3%, regardless of the method. However, they can also reach 10% with unsuitable method. Even if the accuracy of the cone beam CT-based dose calculation is independent of the method, some strategies are promising but need improvements in the automating process for a routine implementation

Measurement of narcolepsy symptoms: The Narcolepsy Severity Scale.

To validate the Narcolepsy Severity Scale (NSS), a brief clinical instrument to evaluate the severity and consequences of symptoms in patients with narcolepsy type 1 (NT1). A 15-item scale to assess the frequency and severity of excessive daytime sleepiness, cataplexy, hypnagogic hallucinations, sleep paralysis, and disrupted nighttime sleep was developed and validated by sleep experts with patients' feedback. Seventy untreated and 146 treated adult patients with NT1 were evaluated and completed the NSS in a single reference sleep center. The NSS psychometric properties, score changes with treatment, and convergent validity with other clinical parameters were assessed. The NSS showed good psychometric properties with significant item-total score correlations. The factor analysis indicated a 3-factor solution with good reliability, expressed by satisfactory Cronbach α values. The NSS total score temporal stability was good. Significant NSS score differences were observed between untreated and treated patients (dependent sample, 41 patients before and after sleep therapy; independent sample, 29 drug-free and 105 treated patients). Scores were lower in the treated populations (10-point difference between groups), without ceiling effect. Significant correlations were found among NSS total score and daytime sleepiness (Epworth Sleepiness Scale, Mean Sleep Latency Test), depressive symptoms, and health-related quality of life. The NSS can be considered a reliable and valid clinical tool for the quantification of narcolepsy symptoms to monitor and optimize narcolepsy management

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review

International audiencePURPOSE: In radiotherapy, MRI is used for target volume and organs-at-risk delineation for its superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the electron density of tissue necessary for dose calculation. Several methods of synthetic-CT (sCT) generation from MRI data have been developed for radiotherapy dose calculation. This work reviewed deep learning (DL) sCT generation methods and their associated image and dose evaluation, in the context of MRI-based dose calculation. METHODS: We searched the PubMed and ScienceDirect electronic databases from January 2010 to March 2021. For each paper, several items were screened and compiled in figures and tables. RESULTS: This review included 57 studies. The DL methods were either generator-only based (45% of the reviewed studies), or generative adversarial network (GAN) architecture and its variants (55% of the reviewed studies). The brain and pelvis were the most commonly investigated anatomical localizations (39% and 28% of the reviewed studies, respectively), and more rarely, the head-and-neck (HandN) (15%), abdomen (10%), liver (5%) or breast (3%). All the studies performed an image evaluation of sCTs with a diversity of metrics, with only 36 studies performing dosimetric evaluations of sCT. CONCLUSIONS: The median mean absolute errors were around 76 HU for the brain and HandN sCTs and 40 HU for the pelvis sCTs. For the brain, the mean dose difference between the sCT and the reference CT was <2%. For the HandN and pelvis, the mean dose difference was below 1% in most of the studies. Recent GAN architectures have advantages compared to generator-only, but no superiority was found in term of image or dose sCT uncertainties. Key challenges of DL-based sCT generation methods from MRI in radiotherapy is the management of movement for abdominal and thoracic localizations, the standardization of sCT evaluation, and the investigation of multicenter impacts

Idling for Decades: A European Study on Risk Factors Associated with the Delay Before a Narcolepsy Diagnosis

Purpose: Narcolepsy type-1 (NT1) is a rare chronic neurological sleep disorder with excessive daytime sleepiness (EDS) as usual first and cataplexy as pathognomonic symptom. Shortening the NT1 diagnostic delay is the key to reduce disease burden and related low quality of life. Here we investigated the changes of diagnostic delay over the diagnostic years (1990–2018) and the factors associated with the delay in Europe. Patients and Methods: We analyzed 580 NT1 patients (male: 325, female: 255) from 12 European countries using the European Narcolepsy Network database. We combined machine learning and linear mixed-effect regression to identify factors associated with the delay. Results: The mean age at EDS onset and diagnosis of our patients was 20.9±11.8 (mean ± standard deviation) and 30.5±14.9 years old, respectively. Their mean and median diagnostic delay was 9.7±11.5 and 5.3 (interquartile range: 1.7−13.2 years) years, respectively. We did not find significant differences in the diagnostic delay over years in either the whole dataset or in individual countries, although the delay showed significant differences in various countries. The number of patients with short (≤2-year) and long (≥13-year) diagnostic delay equally increased over decades, suggesting that subgroups of NT1 patients with variable disease progression may co-exist. Younger age at cataplexy onset, longer interval between EDS and cataplexy onsets, lower cataplexy frequency, shorter duration of irresistible daytime sleep, lower daytime REM sleep propensity, and being female are associated with longer diagnostic delay. Conclusion: Our findings contrast the results of previous studies reporting shorter delay over time which is confounded by calendar year, because they characterized the changes in diagnostic delay over the symptom onset year. Our study indicates that new strategies such as increasing media attention/awareness and developing new biomarkers are needed to better detect EDS, cataplexy, and changes of nocturnal sleep in narcolepsy, in order to shorten the diagnostic interval

αvβ8 integrin-expression by BATF3-dependent dendritic cells facilitates early IgA responses to Rotavirus

International audienceSecretory intestinal IgA can protect from re-infection with rotavirus (RV), but very little is known about the mechanisms that induce IgA production during intestinal virus infections. Classical dendritic cells (cDCs) in the intestine can facilitate both T cell-dependent and -independent secretory IgA. Here, we show that BATF3-dependent cDC1, but not cDC2, are critical for the optimal induction of RV-specific IgA responses in the mesenteric lymph nodes. This depends on the selective expression of the TGF beta-activating integrin alpha v beta 8 by cDC1. In contrast, alpha v beta 8 on cDC1 is dispensible for steady state immune homeostasis. Given that cDC2 are crucial in driving IgA during steady state but are dispensable for RV-specific IgA responses, we propose that the capacity of DC subsets to induce intestinal IgA responses reflects the context, as opposed to an intrinsic property of individual DC subsets