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Cancer burdens not only the patients themselves but also their personal environment. A few studies have already focused on the mental health and personal needs of caregivers of patients. The purpose of this retrospective analysis was to further assess the emotional burden and unmet needs for support of caregivers in a population of brain metastasis patients. In the time period 2013–2020, we identified 42 informal caregivers of their respective patients after palliative radiation treatment for brain metastases. The caregivers completed two standardized questionnaires about different treatment aspects, their emotional burden, and unmet needs for support. Involvement of psycho-oncology and palliative care was examined in a chart review. The majority of the caregivers (71.4%, n = 30) suffered from high emotional burden during cancer treatment of their relatives and showed unmet needs for emotional and psychosocial support, mostly referring to information needs and the involvement in the patient’s treatment decisions. Other unmet needs referred to handling personal needs and fears of dealing with the sick cancer patient in terms of practical care tasks and appropriate communication. Palliative care was involved in 30 cases and psycho-oncology in 12 cases. There is a high need for emotional and psychosocial support in informal caregivers of cancer patients. There might still be room for an improvement of psychosocial and psycho-oncological support. Care planning should cater to the emotional burden and unmet needs of informal caregivers as well. Further prospective studies in larger samples should be performed in order to confirm this analysis.

Background/Aim: To evaluate patients and treatment characteristics as well as clinical outcome in patients with intracranial metastases from prostate cancer (PCA) treated with palliative radiotherapy. Patients and Methods: Fifteen patients treated for intracranial metastases of PCA were identified. The median age of patients was 69 years. 80% of patients received whole brain radiotherapy and 20% received partial brain radiotherapy. Clinical outcome was assessed. Univariate analysis was performed to analyze the impact of patient specific parameters on survival. Results: There was no >G2 acute or any late toxicity. Median time from the first diagnosis of PCA to first diagnosis of intracranial metastases was 62 months (range=15-160 months). Median survival from first diagnosis of intracranial metastases was 14 weeks (range=0-126 weeks) and 6 weeks (range=0-47 weeks) from the start of radiotherapy. In univariate analysis, survival was significantly better for patients with an Eastern Cooperative Oncology Group (ECOG) performance status 1 compared to ECOG 2-3 [18 weeks (range=5-47 weeks) vs. 3 weeks (range=0-21 weeks), p=0.030] and Recursive Partitioning Analysis (RPA) class 2 compared to RPA class 3 [18 weeks (range=5-47 weeks) vs. 6 weeks (range=0-21 weeks), p=0.045]. Conclusion: Overall survival of the patients with wide-spread intracranial metastases from PCA was poor. The decision for a radiotherapy should be done on individual patient basis.

INTRODUCTION Prostate bed radiotherapy (RT) is a major affecter of patients' long-term quality of life (QoL). To ensure the best possible outcome of these patients, dose constraints are key for optimal RT planning and delivery. However, establishing refined dose constraints requires access to patient-level data. Therefore, we aimed to provide such data on the relationship between OAR and gastrointestinal (GI) as well as genitourinary (GU) QoL outcomes of a homogenous patient cohort who received dose-intensified post-operative RT to the prostate bed. Furthermore, we aimed to conduct an exploratory analysis of the resulting data. METHODS Patients who were treated with prostate bed RT between 2010 and 2020 were inquired about their QoL based on the Expanded Prostate Cancer Index Composite (EPIC). Those (n = 99) who received volumetric arc therapy (VMAT) of at least 70 Gy to the prostate bed were included. Dose-volume histogram (DVH) parameters were gathered and correlated with the EPIC scores. RESULTS The median age at the time of prostate bed RT was 68.9 years, and patients were inquired about their QoL in the median 2.3 years after RT. The median pre-RT prostate-specific antigen (PSA) serum level was 0.35 ng/mL. The median duration between surgery and RT was 1.5 years. The median prescribed dose to the prostate bed was 72 Gy. A total of 61.6% received prostate bed RT only. For the bladder, the highest level of statistical correlation (p < 0.01) was seen for V10-20Gy, Dmean and Dmedian with urinary QoL. For bladder wall, the highest level of statistically significant correlation (p < 0.01) was seen for V5-25Gy, Dmean and Dmedian with urinary QoL. Penile bulb V70Gy was statistically significantly correlated with sexual QoL (p < 0.05). A larger rectal volume was significantly correlated with improved bowel QoL (p < 0.05). Sigmoid and urethral DVH parameters as well as the surgical approach were not statistically significantly correlated with QoL. CONCLUSION Specific dose constraints for bladder volumes receiving low doses seem desirable for the further optimization of prostate bed RT. This may be particularly relevant in the context of the aspiration of establishing focal RT of prostate cancer and its local recurrences. Our comprehensive dataset may aid future researchers in achieving these goals.

Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception.