Challenges in establishing optimal pediatric palliative care at the university hospital in Slovenia.

The integration of pediatric palliative care (PPC) should become a standard of care for all children with life-limiting and life-threatening illnesses. There are many barriers and misperceptions in pediatrics which hinder the early implementation of PPC. The aim of the study was to design starting points for the establishment of accessible PPC with early involvement of patients in a tertiary-level children's hospital. An intervention, presentation, and discussion on PPC were offered by the hospital PPC team to all employees in the hospital. A total of 237 participants (physicians 30.4%, nurses 49.4%, psychologists 8.4%, and others) completed a questionnaire before and after the intervention. The personnel's knowledge, self-assessment of their ability to perform PPC, attitude to participate in PPC, and their awareness and understanding of the need for PPC were evaluated. The results were analyzed using Pandas and SciPy libraries in Python. The knowledge, awareness, and attitude of the physicians, nurses, and other professionals improved significantly after the intervention. However, the self-assessment of their ability to perform PPC did not increase. Previous experience with the death of a patient has proven to be a stimulus for self-initiative in acquiring knowledge in PPC and was linked with a better attitude and higher awareness of the need for PPC.Conclusions: More education and practical work tailored to the different professional profiles are needed, with adjustments for specific subspecialist areas, especially where patients could be included in early PPC. Although additional studies are needed, we identified the main directions for the further implementation of PPC in clinical practice in our setting.

Label-set impact on deep learning-based prostate segmentation on MRI.

BACKGROUND: Prostate segmentation is an essential step in computer-aided detection and diagnosis systems for prostate cancer. Deep learning (DL)-based methods provide good performance for prostate gland and zones segmentation, but little is known about the impact of manual segmentation (that is, label) selection on their performance. In this work, we investigated these effects by obtaining two different expert label-sets for the PROSTATEx I challenge training dataset (n = 198) and using them, in addition to an in-house dataset (n = 233), to assess the effect on segmentation performance. The automatic segmentation method we used was nnU-Net. RESULTS: The selection of training/testing label-set had a significant (p < 0.001) impact on model performance. Furthermore, it was found that model performance was significantly (p < 0.001) higher when the model was trained and tested with the same label-set. Moreover, the results showed that agreement between automatic segmentations was significantly (p < 0.0001) higher than agreement between manual segmentations and that the models were able to outperform the human label-sets used to train them. CONCLUSIONS: We investigated the impact of label-set selection on the performance of a DL-based prostate segmentation model. We found that the use of different sets of manual prostate gland and zone segmentations has a measurable impact on model performance. Nevertheless, DL-based segmentation appeared to have a greater inter-reader agreement than manual segmentation. More thought should be given to the label-set, with a focus on multicenter manual segmentation and agreement on common procedures. CRITICAL RELEVANCE STATEMENT: Label-set selection significantly impacts the performance of a deep learning-based prostate segmentation model. Models using different label-set showed higher agreement than manual segmentations. KEY POINTS: • Label-set selection has a significant impact on the performance of automatic segmentation models. • Deep learning-based models demonstrated true learning rather than simply mimicking the label-set. • Automatic segmentation appears to have a greater inter-reader agreement than manual segmentation.