To report the primary endpoint of a randomized trial comparing pain response following palliative stereotactic body radiation therapy (SBRT) versus conventionally-fractionated 3D-conformal radiotherapy (3DCRT) for previously untreated spinal metastases. Fifty-five patients with histologically/radiologically confirmed painful spinal metastases were analyzed in this single-institutional, non-blinded, randomized explorative trial. Participants were randomly assigned (1:1) to receive single-fraction SBRT (24 Gy) or 3DCRT (30 Gy in 10 fractions). The primary endpoint was pain relief of >2 points on the visual analog scale (VAS) measured within the irradiated region at 3 months following radiotherapy completion. Other recorded parameters included pain response (per International Bone Consensus response definitions), use of concurrent medications and opioid usage (oral morphine equivalent dose, OMED). All parameters were assessed at baseline and at three and six months after RT. Intention-to-treat analysis was applied. This trial is registered with ClinicalTrials.gov, number NCT02358720. Despite no significant differences for VAS at 3 months between groups (p = 0.13), pain values decreased faster within this time period in the SBRT arm (p = 0.01). At 6 months following RT, significantly lower VAS values were reported in the SBRT group (p = 0.002). There were no differences in OMED consumption at 3 (p = 0.761) and 6 months (p = 0.174). There was a trend toward improved pain response in the SBRT arm at 3 months (p = 0.057), but significantly so after 6 months (p = 0.003). No patient in the SBRT group experienced grade ≥3 toxicities according to the Common Terminology Criteria for Adverse Events v.4.03. This randomized trial demonstrates the utility of palliative SBRT for spinal metastases, which was associated with a quicker and improved pain response. Larger ongoing randomized studies will assist in further addressing these endpoints.

Purpose: We assessed the predictive potential of positron emission tomography (PET)/CT-based radiomics, lesion volume, and routine blood markers for early differentiation of pseudoprogression from true progression at 3 months.Experimental Design: 112 patients with metastatic melanoma treated with immune checkpoint inhibition were included in our study. Median follow-up duration was 22 months. 716 metastases were segmented individually on CT and 2[18F]fluoro-2-deoxy-D-glucose (FDG)-PET imaging at three timepoints: baseline (TP0), 3 months (TP1), and 6 months (TP2). Response was defined on a lesion-individual level (RECIST 1.1) and retrospectively correlated with FDG-PET/CT radiomic features and the blood markers LDH/S100. Seven multivariate prediction model classes were generated.Results: Two-year (median) overall survival, progression-free survival, and immune progression–free survival were 69% (not reached), 24% (6 months), and 42% (16 months), respectively. At 3 months, 106 (16%) lesions had progressed, of which 30 (5%) were identified as pseudoprogression at 6 months. Patients with pseudoprogressive lesions and without true progressive lesions had a similar outcome to responding patients and a significantly better 2-year overall survival of 100% (30 months), compared with 15% (10 months) in patients with true progressions/without pseudoprogression (P = 0.002). Patients with mixed progressive/pseudoprogressive lesions were in between at 53% (25 months). The blood prediction model (LDH+S100) achieved an AUC = 0.71. Higher LDH/S100 values indicated a low chance of pseudoprogression. Volume-based models: AUC = 0.72 (TP1) and AUC = 0.80 (delta-volume between TP0/TP1). Radiomics models (including/excluding volume-related features): AUC = 0.79/0.78. Combined blood/volume model: AUC = 0.79. Combined blood/radiomics model (including volume-related features): AUC = 0.78. The combined blood/radiomics model (excluding volume-related features) performed best: AUC = 0.82.Conclusions: Noninvasive PET/CT-based radiomics, especially in combination with blood parameters, are promising biomarkers for early differentiation of pseudoprogression, potentially avoiding added toxicity or delayed treatment switch.

Background: Male breast cancer (MBC) is a rare disease accounting for approximately 1% of all breast carcinomas. Presently treatment recommendations are derived from the standards for female breast cancer. However, those approaches might be inadequate because of distinct gender specific differences in tumor biology of breast cancer. This study was planned in order to contrast potential differences between female and male breast cancer in both tumor biological behavior and clinical management.Methods: MBC diagnosed between 1995-2007 (region Chemnitz/Zwickau, Saxony, Germany) was retrospectively analyzed. Tumor characteristics, treatment and follow-up of the patients were documented. In order to highlight potential differences each MBC was matched with a female counterpart (FBC) that showed accordance in at least eight tumor characteristics (year of diagnosis, age, tumor stage, nodal status, grade, estrogen- and progesterone receptors, HER2 status).Results: 108 male/female matched-pairs were available for survival analyses. In our study men and women with breast cancer had similar disease-free (DFS) and overall (OS) survival. The 5-years DFS was 53.4% (95% CI, range 54.1-66.3) in men respectively 62.6% (95% CI, 63.5-75.3) in women (p > 0.05). The 5-years OS was 71.4% (95% CI, 62.1-72.7%) and 70.3% (95% CI, 32.6-49.6) in women (p > 0.05). In males DFS analyses revealed progesterone receptor expression as the only prognostic relevant factor (p = 0.006). In multivariate analyses for OS both advanced tumor size (p = 0.01) and a lack of progesterone receptor expression were correlated (p = 0.01) with poor patients outcome in MBC.Conclusion: Our comparative study revealed no survival differences between male and female breast cancer patients and gives evidence that gender is no predictor for survival in breast cancer. This was shown despite of significant gender specific differences in terms of frequency and intensity of systemic therapy in favor to female breast cancer.

To compare the effects of resistance training versus passive physical therapy on bone survival in the metastatic bone during radiation therapy (RT) as combined treatment in patients with spinal bone metastases. Secondly, to evaluate overall survival and progression-free-survival (PFS) as well as to quantify prognostic factors of bone survival after combined treatment. In this randomized trial 60 patients were allocated from September 2011 until March 2013 into one of the two groups: resistance training (group A) or passive physical therapy (group B) with thirty patients in each group during RT. We estimated patient survival using Kaplan-Meier survival method. The Wald-test was used to evaluate the prognostic importance of pathological fracture, primary site, Karnofsky performance status, localization of metastases, number of metastases, and cerebral metastases. Median follow-up was 10 months (range 2–35). Bone survival showed no significant difference between groups (p = .303). Additionally no difference between groups could be detected in overall survival (p = .688) and PFS (p = .295). Local bone progression was detected in 16.7 % in group B, no irradiated bone in group A showed a local progression over the course (p = 0.019). In univariate analysis breast cancer, prostate cancer, and the presence of cerebral metastases had a significant impact on bone survival in group B, while no impact could be demonstrated in group A. In this group of patients with spinal bone metastases we were able to show that guided resistance training of the paravertebral muscles had no essential impact on survival concomitant to RT. Importantly, no local bone progression in group A was detected, nevertheless no prognostic factor for combined treatment could be evaluated. Clinical trial identifier NCT 01409720 . Registered 8 February 2011.

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.