Long-term prevention of bladder cancer progression by alpha1-oleate alone or in combination with chemotherapy

Bladder cancer is common and one of the most costly cancer forms, due to a lack of curative therapies. Recently, clinical safety and efficacy of the alpha1-oleate complex was demonstrated in a placebo-controlled study of nonmuscle invasive bladder cancer. Our study investigated if long-term therapeutic efficacy is improved by repeated treatment cycles and by combining alpha1-oleate with low-dose chemotherapy. Rapidly growing bladder tumors were treated by intravesical instillation of alpha1-oleate, Epirubicin or Mitomycin C alone or in combination. One treatment cycle arrested tumor growth, with a protective effect lasting at least 4 weeks in mice receiving 8.5 mM of alpha1-oleate alone or 1.7 mM of alpha-oleate combined with Epirubicin or Mitomycin C. Repeated treatment cycles extended protection, defined by a lack of bladder pathology and a virtual absence of bladder cancer-specific gene expression. Synergy with Epirubicin was detected at the lower alpha1-oleate concentration and in vitro, alpha1-oleate was shown to enhance the uptake and nuclear translocation of Epirubicin, by tumor cells. Effects at the chromatin level affecting cell proliferation were further suggested by reduced BrdU incorporation. In addition, alpha1-oleate triggered DNA fragmentation, defined by the TUNEL assay. The results suggest that bladder cancer development may be prevented long-term in the murine model, by alpha1-oleate alone or in combination with low-dose Epirubicin. In addition, the combination of alpha1-oleate and Epirubicin reduced the size of established tumors. Exploring these potent preventive and therapeutic effects will be of immediate interest in patients with bladder cancer.     

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.     

Incorporating respondent-driven sampling into web-based discrete choice experiments: preferences for COVID-19 mitigation measures

Health Services and Outcomes Research Methodology - To slow the spread of COVID-19, most countries implemented stay-at-home orders, social distancing, and other nonpharmaceutical mitigation...