APOLLO: An accurate and independently validated prediction model of lower-grade gliomas overall survival and a comparative study of model performance

作者：Jiajin Chen, Sipeng Shen, Yi Li, Juanjuan Fan, Shiyu Xiong, Jingtong Xu, Chenxu Zhu, Lijuan Lin, Xuesi Dong, Weiwei Duan, Yang Zhao, Qian Xu, Zhonghua Liu, Yongyue Wei, David C. Christiani, Ruyang Zhang, Feng Chen · 发表于：EBioMedicine · 年份：2022 · DOI：10.1016/j.ebiom.2022.104007 · 被引用次数：41 · 研究领域：Glioma Diagnosis and Treatment、Ferroptosis and cancer prognosis、Brain Tumor Detection and Classification

Background Virtually few accurate and robust prediction models of lower-grade gliomas (LGG) survival exist that may aid physicians in making clinical decisions. We aimed to develop a prognostic prediction model of LGG by incorporating demographic, clinical and transcriptional biomarkers with either main effects or gene-gene interactions. Methods Based on gene expression profiles of 1,420 LGG patients from six independent cohorts comprising both European and Asian populations, we proposed a 3-D analysis strategy to develop and validate an A ccurate P rediction m O del of L ower-grade g L iomas O verall survival (APOLLO). We further conducted decision curve analysis to assess the net benefit ( NB ) of identifying true positives and the net reduction ( NR ) of unnecessary interventions. Finally, we compared the performance of APOLLO and the existing prediction models by the first systematic review. Findings APOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20 th percentile of APOLLO risk score, patients with more than the 90 th percentile of APOLLO risk score had significantly worse overall survival ( HR =54·18, 95% CI: 34·73-84·52, P =2·66 × 10 −69 ). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC 36-month =0·901 (95% CI: 0·879-0·923), AUC 60-month =0·843 (95% CI: 0·815-0·871) and C-index=0·818 (95% CI: 0·800-0·835). Moreover, ...