Impact of shared decision-making on women's childbirth preferences: A cluster randomised controlled trial.

Background: Midwives provide counselling for birth plans (BPs) to women during prenatal care; however, the impact of individualised BP counselling interventions based on shared decision-making (SDM) regarding women's preferences is unknown.

Methods: This randomised cluster trial included four primary healthcare units. Midwives provided BP counselling based on SDM to women in the intervention group (IG) during prenatal care along with a handout about evidence-based recommendations.

Selective sparing of bladder and rectum sub-regions in radiotherapy of prostate cancer combining knowledge-based automatic planning and multicriteria optimization.

Background And Purpose: The association between dose to selected bladder and rectum symptom-related sub-regions (SRS) and late toxicity after prostate cancer radiotherapy has been evidenced by voxel-wise analyses. The aim of the current study was to explore the feasibility of combining knowledge-based (KB) and multi-criteria optimization (MCO) to spare SRSs without compromising planning target volume (PTV) dose delivery, including pelvic-node irradiation.

Materials And Methods: Forty-five previously treated patients (74.

Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning.

Background And Purpose: The intraprostatic urethra is an organ at risk in prostate cancer radiotherapy, but its segmentation in computed tomography (CT) is challenging. This work sought to: i) propose an automatic pipeline for intraprostatic urethra segmentation in CT, ii) analyze the dose to the urethra, iii) compare the predictions to magnetic resonance (MR) contours.

Materials And Methods: First, we trained Deep Learning networks to segment the rectum, bladder, prostate, and seminal vesicles.

Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data.

Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time- and labor-consuming, as well as observer-dependent.