27874 Correlation of itch response to roflumilast cream with disease severity and patient-reported outcomes in patients with chronic plaque psoriasis

Roflumilast cream is a nonsteroidal, selective phosphodiesterase-4 inhibitor in development for plaque psoriasis (PsO). A Phase 2b, double-blinded trial randomized adults with PsO (2-20% body surface area) to once daily roflumilast 0.3%, roflumilast 0.15%, or vehicle for 12 weeks (NCT03638258). Throughout the trial, itch and its impact were evaluated via patient reported outcomes (PROs): Worst Itch Numeric Rating Scale (WI–NRS), Itch related Sleep Loss (IRSL), and Dermatology Life Quality Index (DLQI). This posthoc analysis reports correlation of WI–NRS with other PROs and with disease severity. Overall, 331 patients were randomized (109 to roflumilast 0.3%, 113 to 0.15%, and 109 to vehicle). At baseline, the mean WI–NRS score was 5.87. Throughout the trial, both roflumilast doses showed similar improvements in WI–NRS starting at Week 2 and were significantly superior to vehicle (P ≤.002). At baseline, Pearson correlation coefficients (PCCs) for WI–NRS and Psoriasis Area and Severity Index (PASI) were 0.189, 0.282, 0.205 for roflumilast 0.3%, roflumilast 0.15%, and vehicle, respectively (P ≤.033 for all correlations); for WI–NRS and IRSL: 0.548, 0.646, 0.652 (P ˂.001); for WI–NRS and DLQI: 0.445, 0.617, 0.422 (P ˂.001). At Week 8, PCCs for WI–NRS and PASI were 0.420, 0.409, 0.365 (P ˂.001); for WI–NRS and IRSL: 0.673, 0.725, 0.696 (P ˂.001); for WI–NRS and DLQI: 0.607, 0.823, 0.529. Treatment with roflumilast resulted in rapid and robust improvement in the severity of itch associated with PsO. Itch response to roflumilast was independent of disease severity and positively correlated with patient-reported sleep loss and quality of life improvement

3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge

Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, developing automated algorithms for teeth analysis presents significant challenges due to variations in dental anatomy, imaging protocols, and limited availability of publicly accessible data. To address these challenges, the 3DTeethSeg'22 challenge was organized in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans. A dataset comprising a total of 1800 scans from 900 patients was prepared, and each tooth was individually annotated by a human-machine hybrid algorithm. A total of 6 algorithms were evaluated on this dataset. In this study, we present the evaluation results of the 3DTeethSeg'22 challenge. The 3DTeethSeg'22 challenge code can be accessed at: https://github.com/abenhamadou/3DTeethSeg22_challengeComment: 29 pages, MICCAI 2022 Singapore, Satellite Event, Challeng

Classification of caries in third molars on panoramic radiographs using deep learning

Abstract The objective of this study is to assess the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the classification of carious lesions in mandibular and maxillary third molars, based on the CNN MobileNet V2. For this pilot study, the trained MobileNet V2 was applied on a test set consisting of 100 cropped PR(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an accuracy of 0.87, a sensitivity of 0.86, a specificity of 0.88 and an AUC of 0.90 for the classification of carious lesions of third molars on PR(s). A high accuracy was achieved in caries classification in third molars based on the MobileNet V2 algorithm as presented. This is beneficial for the further development of a deep-learning based automated third molar removal assessment in future

Automated chart filing on panoramic radiographs using deep learning

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Positional assessment of lower third molar and mandibular canal using explainable artificial intelligence.

OBJECTIVE: The aim of this study is to automatically assess the positional relationship between lower third molars (M3i) and the mandibular canal (MC) based on the panoramic radiograph(s) (PR(s)). MATERIAL AND METHODS: A total of 1444 M3s were manually annotated and labeled on 863 PRs as a reference. A deep-learning approach, based on MobileNet-V2 combination with a skeletonization algorithm and a signed distance method, was trained and validated on 733 PRs with 1227 M3s to classify the positional relationship between M3i and MC into three categories. Subsequently, the trained algorithm was applied to a test set consisting of 130 PRs (217 M3s). Accuracy, precision, sensitivity, specificity, negative predictive value, and F1-score were calculated. RESULTS: The proposed method achieved a weighted accuracy of 0.951, precision of 0.943, sensitivity of 0.941, specificity of 0.800, negative predictive value of 0.865 and an F1-score of 0.938. CONCLUSION: AI-enhanced assessment of PRs can objectively, accurately, and reproducibly determine the positional relationship between M3i and MC. CLINICAL SIGNIFICANCE: The use of such an explainable AI system can assist clinicians in the intuitive positional assessment of lower third molars and mandibular canals. Further research is required to automatically assess the risk of alveolar nerve injury on panoramic radiographs

Intra-oral scan segmentation using deep learning

Abstract Objective Intra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments, which require teeth segmentations with high accuracy and resolution. Manual teeth segmentation, the gold standard up until now, is time-consuming, tedious, and observer-dependent. This study aims to develop an automated teeth segmentation and labeling system using deep learning. Material and methods As a reference, 1750 OS were manually segmented and labeled. A deep-learning approach based on PointCNN and 3D U-net in combination with a rule-based heuristic algorithm and a combinatorial search algorithm was trained and validated on 1400 OS. Subsequently, the trained algorithm was applied to a test set consisting of 350 OS. The intersection over union (IoU), as a measure of accuracy, was calculated to quantify the degree of similarity between the annotated ground truth and the model predictions. Results The model achieved accurate teeth segmentations with a mean IoU score of 0.915. The FDI labels of the teeth were predicted with a mean accuracy of 0.894. The optical inspection showed excellent position agreements between the automatically and manually segmented teeth components. Minor flaws were mostly seen at the edges. Conclusion The proposed method forms a promising foundation for time-effective and observer-independent teeth segmentation and labeling on intra-oral scans. Clinical significance Deep learning may assist clinicians in virtual treatment planning in orthodontics, prosthetics, implantology, and orthognathic surgery. The impact of using such models in clinical practice should be explored

Two Multicenter, Randomized, Double-Blind, Parallel Group Comparison Studies of a Novel Enhanced Lotion Formulation of Halobetasol Propionate, 0.05% Versus Its Vehicle in Adult Subjects With Plaque Psoriasis

BACKGROUND: A novel lotion formulation of halobetasol propionate, 0.05% (HBP Lotion) with enhanced vehicle characteristics of a cream while preserving the ease of use and cosmetic elegance of a lotion has been developed to treat plaque psoriasis. OBJECTIVE: Determine the safety and effectiveness of HBP Lotion in patients with plaque psoriasis. METHODS: Two prospective, randomized, vehicle-controlled clinical studies were conducted in 443 adult subjects with moderate-severe plaque psoriasis. Subjects applied the test article to psoriatic plaques within the treatment area twice daily for 14 days. Efficacy data are based upon treatment success defined as those subjects that achieved scores of 0=clear or 1=almost clear with at least a two-grade improvement relative to baseline for an Investigator\u27s Global Assessment (IGA) and clinical signs (plaque elevation, erythema, scaling). Safety data are presented as adverse events and local skin reactions. RESULTS: After two weeks of treatment with HBP Lotion, 44.5% of the HBP Lotion treated subjects in each study achieved (a) treatment success (ie, an IGA score of 0=clear or 1=almost clear and \u3e2 grade improvement compared to baseline) and (b) a notable reduction in plaque elevation, erythema, scaling, and pruritus. In contrast, only 6.3% and 7.1% of VEH subjects in Studies 1 and 2, respectively, achieved treatment success and the reduction of disease related signs was materially lower. Statistically, at day 15 in both Phase 3 studies, treatment success with HBP Lotion was superior to VEH (P less than 0.001). From a safety perspective the outcomes were in general unremarkable with similar findings in the HBP Lotion and VEH treatment groups. CONCLUSIONS: The results demonstrate the safety and effectiveness of HBP Lotion in the treatment of plaque psoriasis. Furthermore, this novel HBP lotion formulation is also distinguished by its moisturization qualities and ease of use. J Drugs Dermatol. 2017;16(3):234-240.

Trial of Roflumilast Cream for Chronic Plaque Psoriasis

BACKGROUND: Systemic oral phosphodiesterase type 4 (PDE-4) inhibitors have been effective in the treatment of psoriasis. Roflumilast cream contains a PDE-4 inhibitor that is being investigated for the topical treatment of psoriasis. METHODS: In this phase 2b, double-blind trial, we randomly assigned adults with plaque psoriasis in a 1:1:1 ratio to use roflumilast 0.3% cream, roflumilast 0.15% cream, or vehicle (placebo) cream once daily for 12 weeks. The primary efficacy outcome was the investigator\u27s global assessment (IGA) of a status of clear or almost clear at week 6 (assessed on a 5-point scale of plaque thickening, scaling, and erythema; a score of 0 indicates clear, 1 almost clear, and 4 severe). Secondary outcomes included an IGA score indicating clear or almost clear plus a 2-grade improvement in the IGA score for the intertriginous area and the change in the Psoriasis Area and Severity Index (PASI) score (range, 0 to 72, with higher scores indicating worse disease). Safety was also assessed. RESULTS: Among 331 patients who underwent randomization, 109 were assigned to roflumilast 0.3% cream, 113 to roflumilast 0.15% cream, and 109 to vehicle cream. An IGA score indicating clear or almost clear at week 6 was observed in 28% of the patients in the roflumilast 0.3% group, in 23% in the roflumilast 0.15% group, and in 8% in the vehicle group (P\u3c0.001 and P = 0.004 vs. vehicle for roflumilast 0.3% and 0.15%, respectively). Among the approximately 15% of patients overall who had baseline intertriginous psoriasis of at least mild severity, an IGA score at week 6 indicating clear or almost clear plus a 2-grade improvement in the intertriginous-area IGA score occurred in 73% of the patients in the roflumilast 0.3% group, 44% of those in the roflumilast 0.15% group, and 29% of those in the vehicle group. The mean baseline PASI scores were 7.7 in the roflumilast 0.3% group, 8.0 in the roflumilast 0.15% group, and 7.6 in the vehicle group; the mean change from baseline at week 6 was -50.0%, -49.0%, and -17.8%, respectively. Application-site reactions occurred with similar frequency in the roflumilast groups and the vehicle group. CONCLUSIONS: Roflumilast cream administered once daily to affected areas of psoriasis was superior to vehicle cream in leading to a state of clear or almost clear at 6 weeks. Longer and larger trials are needed to determine the durability and safety of roflumilast in psoriasis. (Funded by Arcutis Biotherapeutics; ARQ-151 201 ClinicalTrials.gov number, NCT03638258.)

Effect of Roflumilast Cream (ARQ-151) on Itch and Itch-Related Sleep Loss in Adults with Chronic Plaque Psoriasis: Patient-Reported Itch Outcomes of a Phase 2b Trial

BACKGROUND: Itch is the most bothersome symptom reported by patients with psoriasis. Safe and effective treatments for psoriasis that also address itch are needed. OBJECTIVES: To report effects of roflumilast cream on itch-related outcomes from a Phase 2b trial. METHODS: Adults with chronic plaque psoriasis were randomized to roflumilast 0.3%, roflumilast 0.15%, or vehicle once-daily for 12 weeks. Psoriasis severity was assessed via the Investigator Global Assessment (IGA; a 5-point scale assessing plaque thickening, scaling, and erythema ranging from 0 [clear] to 4 [severe]) and ≥ 2 on a modified Psoriasis Area and Severity Index (PASI-HD, which combines severity of lesions and area affected, ranging from 0 [no disease] to 72 [maximal disease], with the actual percentage of the anatomical area involved in those patients with \u3c 10% of anatomical area involved [e.g., 0.1 for 1% to 0.9 for 9%]). Itch was evaluated via Worst Itch Numeric Rating Scale (WI-NRS), Psoriasis Symptom Diary (PSD) Items 1 (severity of itch) and 2 (bother of itch), and itch-related sleep loss NRS scores. Post hoc correlation analyses between WI-NRS and PASI, WI-NRS and itch-related sleep loss, and WI-NRS and DLQI were also performed. RESULTS: Roflumilast-treated patients had significantly greater improvements than vehicle-treated patients in WI-NRS and PSD Items 1 and 2 beginning at Week 2 and in itch-related sleep loss Weeks 6 through 12. Among patients with baseline WI-NRS ≥ 6, significantly more patients achieved ≥ 4-point improvement with roflumilast than with vehicle as early as Week 2. Itch severity had low correlation with PASI while WI-NRS and IGA were not always aligned. LIMITATIONS: The first assessment was at 2 weeks, limiting the ability to assess early onset of itch response. CONCLUSION: Roflumilast cream improved itch and itch-related sleep loss associated with chronic plaque psoriasis. GOV IDENTIFIER: NCT03638258

Novel LOX variants in five families with aortic/arterial aneurysm and dissection with variable connective tissue findings

Thoracic aortic aneurysm and dissection (TAAD) is a major cause of cardiovascular morbidity and mortality. Loss-of-function variants in LOX, encoding the extracellular matrix crosslinking enzyme lysyl oxidase, have been reported to cause familial TAAD. Using a next-generation TAAD gene panel, we identified five additional probands carrying LOX variants, including two missense variants affecting highly conserved amino acids in the LOX catalytic domain and three truncating variants. Connective tissue manifestations are apparent in a substantial fraction of the variant carriers. Some LOX variant carriers presented with TAAD early in life, while others had normal aortic diameters at an advanced age. Finally, we identified the first patient with spontaneous coronary artery dissection carrying a LOX variant. In conclusion, our data demonstrate that loss-of-function LOX variants cause a spectrum of aortic and arterial aneurysmal disease, often combined with connective tissue findings