ABSTRACT
INTRODUCTION: Palliative WBRT is the main treatment for multiple BMs. Recent studies report no benefit in survival after WBRT compared to palliative supportive care in patients (pts) with poor prognosis. A new era of systemic treatment strategies based on targeted therapies are improving the prognosis of patients with BMs. The purpose of this study is to develop a prognostic score in palliative pts with BMs who undergo WBRT in this new setting. METHODS: 239 pts with BMs who received palliative WBRT between 2013-2022 in our center were analyzed retrospectively. The score was designed according to the value of the ß coefficient of each variable with statistical significance in the multivariate model using Cox regression. Once the score was established, a comparison was performed according to Kaplan-Meier and was analyzed by log-rank test. RESULTS: 149 pts (62.3%) were male and median (m) age was 60 years. 139 (58,2%) were lung cancer and 35 (14,6%) breast cancer. All patients received 30Gys in 10 sessions. m overall survival (OS) was 3,74 months (ms). 37 pts (15,5%) had a specific target mutation. We found that 62 pts were in group < 4 points with mOS 6,89 ms (CI 95% 3,18-10,62), 84 in group 4-7 points with mOS 4,01 ms (CI 95% 3,40-4,62) and 92 pts in group > 7 points with mOS 2,72 ms (CI 95% 1,93-3,52) (p < 0,001). CONCLUSIONS: METASNCore items are associated with OS and they could be useful to select palliative pts to receive WBRT. More studies are necessary to corroborate our findings.
Subject(s)
Brain Neoplasms , Cranial Irradiation , Palliative Care , Humans , Female , Male , Palliative Care/methods , Brain Neoplasms/radiotherapy , Brain Neoplasms/secondary , Brain Neoplasms/mortality , Middle Aged , Prognosis , Retrospective Studies , Aged , Cranial Irradiation/methods , Precision Medicine , Adult , Aged, 80 and over , Survival RateABSTRACT
We consider the classical deterministic susceptible-infective-susceptible epidemic model, where the infection and recovery rates depend on a background environmental process that is modeled by a continuous time Markov chain. This framework is able to capture several important characteristics that appear in the evolution of real epidemics in large populations, such as seasonality effects and environmental influences. We propose computational approaches for the determination of various distributions that quantify the evolution of the number of infectives in the population.