Prognostic Evaluation of Neurological Assessment of the Neuro-Oncology Scale in Glioblastoma Patients

神经肿瘤量表神经功能评估在胶质母细胞瘤患者预后评价中的应用

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Abstract

BACKGROUND: The aims of this study were to investigate the role of the Neurological Assessment of Neuro-Oncology (NANO) scale in predicting the prognosis of patients with glioblastoma, and compare these results to predicted data of the Karnofsky Performance Scale (KPS), and Eastern Cooperative Oncology Group (ECOG)/World Health Organization (WHO) performance status. Additionally, we examined other prognostic factors in glioblastoma patients. METHODS: The medical records of 76 patients with a new diagnosis of histologically ascertained glioblastoma in the period from January 2002 to December 2015 at the authors' institution were retrospectively reviewed. Clinical factors, including epidemiologic, radiologic, and therapeutic values were reviewed as well as the performance status assessed by the KPS, ECOG/WHO performance status, and NANO scale. RESULTS: The mean overall survival was 19.8 months (95% confidence interval 15.2-25.4 months). At initial diagnosis, the mean value [±standard deviation (SD)] of KPS score, ECOG/WHO performance status, and NANO scale were 81 (±7.4), 1.3 (±0.6), and 7.3 (±3.8), respectively. Multivariate analysis for predicting survival showed odds ratios of KPS score, ECOG/WHO performance status, and NANO scale were 2.502 (≥80 vs. <80; p=0.024), 1.691 (0-1 vs. 2-5; p=0.047), and 2.763 (0-7 vs. 8-23; p=0.020), respectively. At the time of progression, the mean value (±SD) of KPS score, ECOG/WHO performance status, and NANO scale were 69 (±8.2), 1.6 (±0.7), and 11.4 (±4.2), respectively; multivariate analysis for predicting survival showed that the odd ratios for KPS score, ECOG/WHO performance status, and NANO scale were 2.007 (≥80 vs. <80; p=0.035), 1.321 (0-1 vs. 2-5; p=0.143), and 3.182 (0-7 vs. 8-23; p=0.002), respectively. CONCLUSION: The NANO scale provided a more detailed and objective measure of neurologic function than that currently used for predicting the prognosis of glioblastoma patients, especially at the time of progression.

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