Steven H J Nagtegaal1, An Claes2, Karijn P M Suijkerbuijk3, Franz M N H Schramel4, Tom J Snijders5, Joost J C Verhoeff2. 1. Department of Radiation Oncology, University Medical Center Utrecht, the Netherlands. Electronic address: s.h.j.nagtegaal-2@umcutrecht.nl. 2. Department of Radiation Oncology, University Medical Center Utrecht, the Netherlands. 3. Department of Medical Oncology, University Medical Center Utrecht, the Netherlands. 4. Department of Pulmonary Diseases, St Antonius Hospital, Utrecht/Nieuwegein, the Netherlands. 5. Department of Neurology, University Medical Center Utrecht, the Netherlands.
Abstract
BACKGROUND AND PURPOSE: Multiple prognostic models for predicting survival after treatment for brain metastases have been developed. One of them, the diagnosis-specific Graded Prognostic Assessment (DS-GPA), has been developed to predict the median survival for brain metastases from the most frequent primary sites: lung carcinoma, breast cancer, melanoma, renal cell cancer and gastrointestinal tumours. In this study we aim to compare the survival predicted by the DS-GPA to actual survival, and to assess this models performance on both population and individual levels. METHODS: We identified a consecutive cohort of patients treated with SRS for brain metastases in our institute. DS-GPA scores were calculated for each patient, and the median survival for each DS-GPA group was calculated. Differences in survival between DS-GPA groups were tested with Wilcoxon Signed Rank tests and log-rank tests. RESULTS: In total 367 patients were included in the analysis. Median survival in our cohort is largely comparable to corresponding DS-GPA cohorts, but some notable differences are present. There was a significantly shorter median survival (15.4 months, compared to 26.5 months) in the adenocarcinoma NSCLC subgroup with a GPA score of 2.3-3. We confirmed the significant differences in survival time for most cancer-specific subgroups. CONCLUSION: DS-GPA seems to be a reliable tool to classify patients with brain metastases treated with SRS into prognostic subgroups. However, we found some aberrations from predicted median survival times, which may be due to specific characteristics of the populations of patients treated with SRS versus other patients.
BACKGROUND AND PURPOSE: Multiple prognostic models for predicting survival after treatment for brain metastases have been developed. One of them, the diagnosis-specific Graded Prognostic Assessment (DS-GPA), has been developed to predict the median survival for brain metastases from the most frequent primary sites: lung carcinoma, breast cancer, melanoma, renal cell cancer and gastrointestinal tumours. In this study we aim to compare the survival predicted by the DS-GPA to actual survival, and to assess this models performance on both population and individual levels. METHODS: We identified a consecutive cohort of patients treated with SRS for brain metastases in our institute. DS-GPA scores were calculated for each patient, and the median survival for each DS-GPA group was calculated. Differences in survival between DS-GPA groups were tested with Wilcoxon Signed Rank tests and log-rank tests. RESULTS: In total 367 patients were included in the analysis. Median survival in our cohort is largely comparable to corresponding DS-GPA cohorts, but some notable differences are present. There was a significantly shorter median survival (15.4 months, compared to 26.5 months) in the adenocarcinoma NSCLC subgroup with a GPA score of 2.3-3. We confirmed the significant differences in survival time for most cancer-specific subgroups. CONCLUSION: DS-GPA seems to be a reliable tool to classify patients with brain metastases treated with SRS into prognostic subgroups. However, we found some aberrations from predicted median survival times, which may be due to specific characteristics of the populations of patients treated with SRS versus other patients.
Authors: Chao Zhang; Guijun Xu; Yao Xu; Haixiao Wu; Xu Guo; Min Mao; Vladimir P Baklaushev; Vladimir P Chekhonin; Karl Peltzer; Ye Bai; Guowen Wang; Wenjuan Ma; Xin Wang Journal: Aging (Albany NY) Date: 2020-08-27 Impact factor: 5.682
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