Radiation therapy (RT) results in pain relief for about 6 of 10 patients with cancer induced bone pain (CIBP) caused by bone metastases. The high number of non-responders, the long median time from RT to pain response and the risk of adverse effects, makes it important to determine predictors of treatment response. Clinical features such as cancer type, performance status and pain intensity, and biomarkers for osteoclast activity are proposed as predictors of response to RT. However, results are inconsistent and there is a need for better predictors of RT response. A similar argument can be stated for the development of cachexia; there are currently no predictors that can identify patients who will develop cachexia later in the cancer disease trajectory. Experimental and preclinical studies show that pain, depression and cachexia are related to inflammation. However, it is not known if inflammatory biomarkers can predict CIBP, depression or development of cachexia. This multicenter, multinational longitudinal observational study will include 600 adult patients receiving RT for CIBP. Demographic data, clinical variables, osteoclast and inflammatory biomarkers will be assessed before start of RT, and 3, 8, 16, 24 and 52 weeks after last course of RT. The primary aim of the study is to identify potential predictors for pain relief from RT. Secondary aims are to explore potential predictors for development of cachexia, the longitudinal relationship between pain intensity and depression, and if inflammatory biomarkers are associated with changes in pain intensity, cachexia and depression during one-year follow up. The immediate clinical implication of the PRAIS study is to identify potential predictive factors for a RT response on CIBP, and thereby reduce non-efficacious RT. Patient benefits are fewer hospital visits, reduced risk of adverse effects and more individualized pain treatment. The long-term clinical implication of the PRAIS study is to improve the knowledge about inflammation in relation to CIBP, cachexia and depression and potentially identify associations and mechanisms that can be targeted for treatment. ClinicalTrials.gov NCT02107664 , date of registration April 8, 2014 (retrospectively registered). The European Palliative Care Research Centre (PRC), Department of Clinical and Molecular Medicine, NTNU, Faculty of medicine and Health Sciences, Trondheim, N-7491, Norway.

Radiotherapy (RT) reduces pain in about 60% of patients with painful bone metastases, leaving many patients without clinical benefit. This study assesses predictors for RT effectiveness in patients with painful bone metastases. We included adult patients receiving RT for painful bone metastases in a multicenter, multinational longitudinal observational study. Pain response within 8 weeks was defined as ≥2-point decrease on a 0-10 pain score scale, without increase in analgesics; or a decrease in analgesics of ≥25% without increase in pain score. Potential predictors were related to patient demographics, RT administration, pain characteristics, tumor characteristics, depression and inflammation (C-reactive protein (CRP)). Multivariate logistic regression analysis with multiple imputation of missing data were applied to identify predictors of RT response. Of 513 eligible patients, 460 patients (90 %) were included in the regression model. 224 patients (44%, 95% confidence interval (CI) 39%-48%) responded to RT. Better Karnofsky performance status (Odds ratio (OR) 1.39, CI 1.15-1.68), breast cancer (OR 2.54, CI 1.12-5.73), prostate cancer (OR 2.83, CI 1.27-6.33) and soft tissue expansion (OR 2.00, CI 1.23-3.25) predicted RT response. Corticosteroids were a negative predictor (OR 0.57, CI 0.37-0.88). Single and multiple fraction RT had similar response. The discriminative ability of the model was moderate; C-statistic 0.69. This study supports previous findings that better performance status and type of cancer diagnosis predicts analgesic RT response, and new data showing that soft tissue expansion predicts RT response and that corticosteroids is a negative predictor for RT response in patients with painful bone metastases. This is the first prospective multicenter study to investigate predictors of RT response in patients with painful bone metastases. Performance status, cancer diagnosis and soft tissue expansions predicted RT response, while use of corticosteroids was a negative predictor. These results may be helpful in selecting patients for palliative RT.

Inflammation is proposed to influence tumor response in radiotherapy (RT). Clinical studies to investigate the relationship between inflammatory markers and RT response is warranted to understand the variable RT efficacy in patients with painful bone metastases. To evaluate the association between inflammatory markers and analgesic response to RT in patients with painful bone metastases. Adult patients from 7 European study sites undergoing RT for painful bone metastases were included in this prospective and longitudinal analysis. The association between RT response and 17 inflammatory markers at baseline, as well as the association between RT response and the changes observed in inflammatory markers between baseline and three and eight weeks after RT, was analyzed with univariate regression analyses. Baseline analyses were adjusted for potential clinical predictors of RT response. None of the inflammatory markers were significantly associated with an upcoming RT response in the analysis of 448 patients with complete baseline data. In patients available for follow-up, the three-week change in TNF (P 0.017), IL-8 (P 0.028), IP-10 (P 0.032), eotaxin (P 0.043), G-CSF (P 0.033) and MCP-1 (P 0.002) were positively associated with RT response, while the three-week change in CRP (P 0.006) was negatively associated. Results from this study show an association between RT response and change in pro-inflammatory mediators and indicate that inflammation may be important to achieve an analgesic RT response in patients with painful bone metastases. None of the investigated inflammatory markers were found to be pre-treatment predictors of RT response.

PURPOSE: The estimation of prognosis and life expectancy is critical in the care of patients with advanced cancer. To aid clinical decision making, we build a prognostic strategy combining a machine learning (ML) model with explainable artificial intelligence to predict 1-year survival after palliative radiotherapy (RT) for bone metastasis. MATERIALS AND METHODS: Data collected in the multicentric PRAIS trial were extracted for 574 eligible adults diagnosed with metastatic cancer. The primary end point was the overall survival (OS) at 1 year (1-year OS) after the start of RT. Candidate covariate predictors consisted of 13 clinical and tumor-related pre-RT patient characteristics, seven dosimetric and treatment-related variables, and 45 pre-RT laboratory variables. ML models were developed and internally validated using the Python package. The effectiveness of each model was evaluated in terms of discrimination. A Shapley Additive Explanations (SHAP) explainability analysis to infer the global and local feature importance and to understand the reasons for correct and misclassified predictions was performed. RESULTS: The best-performing model for the classification of 1-year OS was the extreme gradient boosting algorithm, with AUC and F1-score values equal to 0.805 and 0.802, respectively. The SHAP technique revealed that higher chance of 1-year survival is associated with low values of interleukin-8, higher values of hemoglobin and lymphocyte count, and the nonuse of steroids. CONCLUSION: An explainable ML approach can provide a reliable prediction of 1-year survival after RT in patients with advanced cancer. The implementation of SHAP analysis provides an intelligible explanation of individualized risk prediction, enabling oncologists to identify the best strategy for patient stratification and treatment selection.

Background: Undernutrition and cachexia are common in advanced cancer and often linked to systemic inflammation. While inflammation is associated with poorer prognosis, accelerated weight loss, and reduced treatment tolerance, its direct impact on food intake remains insufficiently investigated. Aim: To examine the association between systemic inflammation and energy and protein intake over time in patients with advanced cancer. Methods: A total of 170 patients from the Palliative Radiotherapy and Inflammation Study were included. Nutritional status was assessed using PG-SGA SF. Dietary intake was recorded using repeated 24 h recalls. Systemic inflammation was defined as CRP > 10 mg/L. Mixed linear models were applied to evaluate the association between inflammation energy and protein intake over time. Results: Systemic inflammation (CRP >10 mg/L) was present in 87 (51%) patients and associated with significantly lower energy (−3.6 kcal/kg, p = 0.04) and lower protein intake (−0.25 g/kg, p = 0.003). Patients with inflammation were more often undernourished and had shorter survival. Conclusions: Systemic inflammation is likely associated with clinically relevant reductions in energy and protein intake in advanced cancer. CRP may help identify patients for whom standard nutritional support is insufficient.