PREDICTION OF PCFDNA LEVELS, PROGRESSION-FREE SURVIVAL, AND OVERALL SURVIVAL THROUGH MACHINE-LEARNING MODELS BASED ON MRI-DERIVED RADIOMIC FEATURES IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA

被引：0
|
作者：
Nocera, G. [1 ,2 ,3 ,4 ]
Pecco, N. [1 ,2 ,3 ]
Pieri, V [1 ,5 ]
Palazzo, L. [1 ,5 ]
D'Oria, F. [1 ]
Della Rosa, P. [2 ,3 ]
Bailo, M. [4 ]
Finocchiaro, G. [5 ]
Mortini, P. [1 ,4 ]
Falini, A. [1 ,2 ,3 ]
Filippi, M. [1 ,5 ]
Berzero, G. [1 ,5 ]
Castellano, A. [1 ,2 ,3 ]
机构：
[1] Univ Vita Salute San Raffaele, Milan, Italy
[2] IRCCS San Raffaele Hosp, Dept Neuroradiol, Milan, Italy
[3] IRCCS San Raffaele Hosp, CERMAC, Milan, Italy
[4] IRCCS San Raffaele Hosp, Dept Neurosurg & Gamma Knife Radiosurg, Milan, Italy
[5] IRCCS San Raffaele Hosp, Dept Neurol, Milan, Italy
来源：
NEURO-ONCOLOGY | 2024年 / 26卷
关键词：
D O I：
10.1093/neuonc/noae144.200
中图分类号：
R73 [肿瘤学];
学科分类号：
100214 ;
摘要：
引用
收藏
页码：V61 / V61
页数：1
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