Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning.

Introduction: Organ-at-risk segmentation for head and neck cancer radiation therapy is a complex and time-consuming process (requiring up to 42 individual structure, and may delay start of treatment or even limit access to function-preserving care. Feasibility of using a deep learning (DL) based autosegmentation model to reduce contouring time without compromising contour accuracy is assessed through a blinded randomized trial of radiation oncologists (ROs) using retrospective, de-identified patient data. Methods: Two head and neck expert ROs used dedicated time to create gold standard (GS) contours on computed tomography (CT) images. 445 CTs were used to train a custom 3D U-Net DL model covering 42 organs-at-risk, with an additional 20 CTs were held out for the randomized trial. For each held-out patient dataset, one of the eight participant ROs was randomly allocated to review and revise the contours produced by the DL model, while another reviewed contours produced by a medical dosimetry assistant (MDA), both blinded to their origin. Time required for MDAs and ROs to contour was recorded, and the unrevised DL contours, as well as the RO-revised contours by the MDAs and DL model were compared to the GS for that patient. Results: Mean time for initial MDA contouring was 2.3 hours (range 1.6-3.8 hours) and RO-revision took 1.1 hours (range, 0.4-4.4 hours), compared to 0.7 hours (range 0.1-2.0 hours) for the RO-revisions to DL contours. Total time reduced by 76% (95%-Confidence Interval: 65%-88%) and RO-revision time reduced by 35% (95%-CI,-39%-91%). All geometric and dosimetric metrics computed, agreement with GS was equivalent or significantly greater (p<0.05) for RO-revised DL contours compared to the RO-revised MDA contours, including volumetric Dice similarity coefficient (VDSC), surface DSC, added path length, and the 95%-Hausdorff distance. 32 OARs (76%) had mean VDSC greater than 0.8 for the RO-revised DL contours, compared to 20 (48%) for RO-revised MDA contours, and 34 (81%) for the unrevised DL OARs. Conclusion: DL autosegmentation demonstrated significant time-savings for organ-at-risk contouring while improving agreement with the institutional GS, indicating comparable accuracy of DL model. Integration into the clinical practice with a prospective evaluation is currently underway.

Detoxification Enhanced Lifestyle Intervention Targeting Endotoxemia (DELITE) in the Setting of Obesity and Pain: Results of a Pilot Group Intervention.

BACKGROUND: Obesity is a complex multifactorial disorder affecting a growing proportion of the population. While therapeutic lifestyle change (TLC) is foundational, results of interventional programs are often inconsistent. Factors related to systemic inflammation, toxin load and endotoxemia have been postulated to play a contributory role. This pilot study sought to evaluate the role of TLC with enhanced laboratory evaluation and interventions to address these emerging therapeutic targets. METHODS: Twelve participants with a body mass index (BMI) greater than 30 (or 27 with metabolic co-morbidities) were recruited from an outpatient clinic for participation with a primary outcome of pre/post changes in body composition. Participants completed a 12-week program involving weekly group and individualized dietary, exercise, and behavioral support, supplemented with a commercial, 30-day dietary detoxification intervention and ongoing nutritional counseling. All participants completed baseline and post-intervention evaluation including metabolic, toxin load, endotoxin, body composition and functional fitness profiles. RESULTS: After 12-weeks, participants as a group significantly improved body composition parameters including BMI, body fat, fat mass, and waist and hip circumference (P < .01). Significant improvement in several secondary outcomes including levels of lipopolysaccharide, zonulin and leptin were noted. Additionally, results demonstrate substantial improvements in pain, pain interference and functional fitness. Upon completion, all participants rated the program favorably with a high likelihood of continuing or recommending participation to others. CONCLUSIONS: Obesity remains a challenging and often refractory clinical scenario with emerging evidence indicating the potential role of systemic inflammation, toxin load and endotoxemia. A group therapeutic lifestyle change program enhanced with a detoxification component is feasible and may provide a promising intervention for achieving weight loss while also addressing functional and pain related co-morbidities. Future randomized trials evaluating the components of such a program are needed to better delineate the role of specific interventions in the complex setting of obesity.

Large-volume protein crystal growth for neutron macromolecular crystallography.

Neutron macromolecular crystallography (NMC) is the prevailing method for the accurate determination of the positions of H atoms in macromolecules. As neutron sources are becoming more available to general users, finding means to optimize the growth of protein crystals to sizes suitable for NMC is extremely important. Historically, much has been learned about growing crystals for X-ray diffraction. However, owing to new-generation synchrotron X-ray facilities and sensitive detectors, protein crystal sizes as small as in the nano-range have become adequate for structure determination, lessening the necessity to grow large crystals. Here, some of the approaches, techniques and considerations for the growth of crystals to significant dimensions that are now relevant to NMC are revisited. These include experimental strategies utilizing solubility diagrams, ripening effects, classical crystallization techniques, microgravity and theoretical considerations.