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Background and Purpose: To evaluate the impact of a deep learning (DL)-assisted interactive contouring tool on inter-observer variability and the time taken to complete tumour contouring., Materials and Methods: Nine clinicians contoured the gross tumour volume (GTV) using the PET-CT scans of 10 non-small cell lung cancer (NSCLC) patients, either using DL-assisted or manual contouring tools. After contouring a case using one contouring method, the same case was contoured one week later using the other method. The contours and time taken were compared., Results: Use of the DL-assisted tool led to a statistically significant decrease in active contouring time of 23 % relative to the standard manual segmentation method (p < 0.01). The mean observation time for all clinicians and cases made up nearly 60 % of interaction time for both contouring approaches. On average the time spent contouring per case was reduced from 22 min to 19 min when using the DL-assisted tool. Additionally, the DL-assisted tool reduced contour variability in the parts of tumour where clinicians tended to disagree the most, while the consensus contour was similar whichever of the two contouring approaches was used., Conclusions: A DL-assisted interactive contouring approach decreased active contouring time and local inter-observer variability when used to delineate lung cancer GTVs compared to a standard manual method. Integration of this tool into the clinical workflow could assist clinicians in contouring tasks and improve contouring efficiency., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective: Juvenile localized scleroderma (jLS) is a chronic autoimmune disease commonly associated with poor outcomes, including contractures, hemiatrophy, uveitis, and seizures. Despite improvements in treatment, >25% of patients with jLS have functional impairment. To improve patient evaluation, our workgroup developed the Localized scleroderma Total Severity Scale (LoTSS), an overall disease severity measure., Methods: LoTSS was developed as a weighted measure by a consensus process involving literature review, surveys, case vignettes, and multicriteria decision analysis. Feasibility was assessed in larger Childhood Arthritis and Rheumatology Research Alliance groups. Construct validity with physician assessment and inter-rater reliability was assessed using case vignettes. Additional evaluation was performed in a prospective patient cohort initiating treatment., Results: LoTSS severity items were organized into modules that reflect jLS disease patterns, with modules for skin, extracutaneous, and craniofacial manifestations. Construct validity of LoTSS was supported by a strong positive correlation with the Physician Global Assessment (PGA) of severity and damage and weak positive correlation with PGA-Activity, as expected. LoTSS was responsive, with a small effect size identified. Moderate-to-excellent inter-rater reliability was demonstrated. LoTSS was able to discriminate between patient subsets, with higher scores identified in those with greater disease burden and functional limitation., Conclusion: We developed a new LS measure for assessing cutaneous and extracutaneous severity and have shown it to be reliable, valid, and responsive. LoTSS is the first measure that assesses and scores all the major extracutaneous manifestations in LS. Our findings suggest LoTSS could aid assessment and management of patients and facilitate outcome evaluation in treatment studies., (© 2023 American College of Rheumatology.)