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. Women in the control group (CG) received standard BP counselling from midwives. The main outcome was preference changes concerning BPs. RESULTS: A total of 461 (95.5 %) pregnant women received BP counselling (IG, n = 247; CG, n = 214). Women in the IG changed their BP preferences for 13 items compared with those in the CG. These items were: using an unique space during birth (81.1 % vs 51.6 %; p < 0.001), option for light graduation (63 % vs 44.7 %; p < 0.001), listening to music (57.3 % vs 43.6 %; p = 0.006), drinking fluids during labour (84.6 % vs 93.6 %; p = 0.005), continuous monitoring (59 % vs 37.8 %; p < 0.001); desire for natural childbirth (36.6 % vs 25 %; p = 0.014), epidural analgesia (55.1 % vs 43.6 %; p = 0.023); breathing techniques (65.2 % vs 50.5 %; p = 0.003), massage (74.9 % vs 55.3 %; p < 0.001); birthing ball use (81.9 % vs 56.9 %; p < 0.001), spontaneous pushing (49.3 % vs 28.7 %; p < 0.001), choosing birth position (69.6 % vs 41.5 %) and delayed umbilical cord clamping (67.8 % vs 44.1 %; p = 0.001). CONCLUSION: SDM counselling, together with a handout about evidence-based recommendations on childbirth and newborn care, produced more changes in women's preferences expressed in the BP than standard counselling.

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 (74.2 Gy/28fr) were selected and SRSs (in the bladder, associated with late dysuria/hematuria/retention; in the rectum, associated with bleeding) were generated using deformable registration. A KB model was used to obtain clinically suitable plans (KB-plan). KB-plans were further optimized using MCO, aiming to reduce dose to the SRSs while safeguarding target dose coverage, homogeneity and avoiding worsening dose volume histograms of the whole bladder, rectum and other organs at risk. The resulting MCO-generated plans were examined to identify the best-compromise plan (KB + MCO-plan). Results: The mean SRS dose decreased in almost all patients for each SRS. D1% also decreased in the large majority, less frequently for dysuria/bleeding SRS. Mean differences were statistically significant (p < 0.05) and ranged between 1.3 and 2.2 Gy with maximum reduction of mean dose up to 3-5 Gy for the four SRSs. The better sparing of SRSs was obtained without compromising PTVs coverage. Conclusions: Selectively sparing SRSs without compromising PTV coverage is feasible and has the potential to reduce toxicities in prostate cancer radiotherapy. Further investigation to better quantify the expected risk reduction of late toxicities is warranted.

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. Then, the proposed Deep Learning Urethra Segmentation model was trained with the bladder and prostate distance transforms and 44 labeled CT with visible catheters. The evaluation was performed on 11 datasets, calculating centerline distance (CLD) and percentage of centerline within 3.5 and 5 mm. We applied this method to a dataset of 32 patients treated with intensity-modulated radiation therapy (IMRT) to quantify the urethral dose. Finally, we compared predicted intraprostatic urethra contours to manual delineations in MR for 15 patients without catheter. Results: A mean CLD of 1.6 ± 0.8 mm for the whole urethra and 1.7 ± 1.4, 1.5 ± 0.9, and 1.7 ± 0.9 mm for the top, middle, and bottom thirds were obtained in CT. On average, 94% and 97% of the segmented centerlines were within a 3.5 mm and 5 mm radius, respectively. In IMRT, the urethra received a higher dose than the overall prostate. We also found a slight deviation between the predicted and manual MR delineations. Conclusion: A fully-automatic segmentation pipeline was validated to delineate the intraprostatic urethra in CT images.

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. Deep learning (DL) based segmentation has proven to overcome some of these limitations, but requires large databases of homogeneously contoured image sets for robust training. However, these are not easily obtained from the standard clinical protocols as the OARs delineated may vary depending on the patient's tumor site and specific treatment plan. This results in incomplete or partially labeled data. This paper presents a solution to train a robust DL-based automated segmentation tool exploiting a clinical partially labeled dataset. We propose a two-step workflow for OAR segmentation: first, we developed longitudinal OAR-specific 3D segmentation models for pseudo-contour generation, completing the missing contours for some patients; with all OAR available, we trained a multi-class 3D convolutional neural network (nnU-Net) for final OAR segmentation. Results obtained in 44 independent datasets showed superior performance of the proposed methodology for the segmentation of fifteen OARs, with an average Dice score coefficient and surface Dice similarity coefficient of 80.59% and 88.74%. We demonstrated that the model can be straightforwardly integrated into the clinical workflow for standard and adaptive radiotherapy.

Effectiveness of birth plan counselling based on shared decision making: A cluster randomized controlled trial (APLANT).

BACKGROUND: A birth plan (BP) is a written document in which the pregnant woman explains her wishes and expectations about childbirth to the health professionals and aims to facilitate her decision-making. Midwives' support to women during the development of the BP is essential, but it's unknown if shared decision making (SDM) is effective in birth plan counselling. We hypothesized that women who receive counselling based on SDM during their pregnancy are more likely to present their BP to the hospital, more satisfied with the childbirth experience, and have better obstetric outcomes than women who receive standard counselling. We also aimed to identify if women who presented BP to the hospital have better obstetric outcomes and more satisfied with the childbirth experience. METHODS: This was a randomised cluster trial involving four Primary Care Units. Midwives provided BP counselling based on SDM to the women in the intervention group (IG) during their pregnancy, along with a leaflet with evidence-based recommendations. Women in the control group (CG) only received the standard birth plan counselling from midwives. The primary outcomes were birth plan presentation to the hospital, obstetrics outcomes and satisfaction with childbirth experience. The Mackey Satisfaction with Childbirth Scale (MCSRS) was used to measure childbirth satisfaction. RESULTS: A total of 461 (95.5%) pregnant women received BP counselling (IG n = 214 and CG n = 247). Fewer women in the intervention group presented their BP to the hospital compared to those in the control group (57.8% vs 75.1%; p <0.001). Mean satisfaction with childbirth experience was high in the IG as well as the CG: 150.2 (SD:22.6) vs. 153.4 (SD:21.8); p = 0.224). The information received about childbirth during pregnancy was high in both groups (95.1% vs 94.8%; p = 1.0). Fewer women in the IG used analgesia epidural compared to those in the CG (84.7% vs 91.7%; p = 0.034); women who combined non-pharmacological and pharmacological methods for pain relief were more in number in the IG (48.9% vs 29.5%; p = 0.001) and women who began breastfeeding in the delivery room were more in number in the IG (83.9% vs 66.3%; p = 0.001). Women who presented their BP had a greater probability of using combined non-pharmacological and pharmacological methods for pain relief aOR = 2.06 (95% CI: 1.30-4.30) and early skin-to-skin contact aOR = 2.08 (95% CI: 1.07-4.04). CONCLUSION: This counselling intervention was not effective to increase the presentation of the BP to the hospital and women's satisfaction with childbirth; however, it was related to a lower usage of analgesia epidural, a higher combination of pharmacological and non-pharmacological methods for pain relief and the initiation of breastfeeding in the delivery room. Presenting the BP to the hospital increased the likelihood of using pharmacological and non-pharmacological methods for pain relief, and early skin-to-skin contact.