Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring of Bladder and Rectum for Prostate Radiation Therapy

PurposeAuto-contouring may reduce workload, interobserver variation, and time associated with manual contouring of organs at risk. Manual contouring remains the standard due in part to uncertainty around the time and workload savings after accounting for the review and editing of auto-contours. This preliminary study compares a standard manual contouring workflow with 2 auto-contouring workflows (atlas and deep learning) for contouring the bladder and rectum in patients with prostate cancer.Methods and MaterialsThree contouring workflows were defined based on the initial contour-generation method including manual (MAN), atlas-based auto-contour (ATLAS), and deep-learning auto-contour (DEEP). For each workflow, initial contour generation was retrospectively performed on 15 patients with prostate cancer. Then, radiation oncologists (ROs) edited each contour while blinded to the manner in which the initial contour was generated. Workflows were compared by time (both in initial contour generation and in RO editing), contour similarity, and dosimetric evaluation.ResultsMean durations for initial contour generation were 10.9 min, 1.4 min, and 1.2 min for MAN, DEEP, and ATLAS, respectively. Initial DEEP contours were more geometrically similar to initial MAN contours. Mean durations of the RO editing steps for MAN, DEEP, and ATLAS contours were 4.1 min, 4.7 min, and 10.2 min, respectively. The geometric extent of RO edits was consistently larger for ATLAS contours compared with MAN and DEEP. No differences in clinically relevant dose-volume metrics were observed between workflows.ConclusionAuto-contouring software affords time savings for initial contour generation; however, it is important to also quantify workload changes at the RO editing step. Using deep-learning auto-contouring for bladder and rectum contour generation reduced contouring time without negatively affecting RO editing times, contour geometry, or clinically relevant dose–volume metrics. This work contributes to growing evidence that deep-learning methods are a clinically viable solution for organ-at-risk contouring in radiation therapy.     

Bi-allelic pathogenic variations in DNAJB11 cause Ivemark II syndrome,a renal-hepatic-pancreatic dysplasia

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Association of Multiple Plasma Biomarker Concentrations with Progression of Prevalent Diabetic Kidney Disease: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study

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Development of a core outcome set for venous leg ulceration (CoreVen) research evaluations (protocol)

BackgroundA venous leg ulcer is a chronic leg wound caused by poor venous blood circulation in the lower limbs. It is a recurring condition causing pain, malodour, reduced mobility, and depression. Randomised controlled trials evaluating treatments for venous leg ulcers provide important evidence to inform clinical decision-making. However, for findings to be useful, outcomes need to be clinically meaningful, consistently reported across trials, and fully reported. Research has identified the large number of outcomes reported in venous leg ulcer trials, impacting both synthesis of results, and clinical decision-making. To address this, a core outcome set will be developed. A core outcome set is an agreed standardised set of outcomes which should be, as a minimum, measured and reported in all trials which evaluate treatment effectiveness for a given indication. A core outcome set has the potential to reduce research waste, improve the utility of RCTs, reduce reporting bias, facilitate treatment comparisons across different sources of evidence and expedite the production of systematic reviews, meta-analyses and evidence-based clinical guidelines.AimThe aim of this project is to develop a core outcome set for research evaluating the effectiveness of interventions for treating venous leg ulceration.MethodsThrough a scoping review of the literature on venous leg ulceration, we will firstly identify a list of candidate outcome domains (broad categories in relation to what is being measured) from randomised controlled trials and qualitative research, and outcomes (specific methods in relation to what is being measured). In two further stages, we will use the resulting lists of outcome domains and outcomes to design two online surveys. A range of stakeholders will be invited to participate in the surveys and they will be asked to indicate which outcome domains and outcomes are most important and should be considered as core in future research reports.