Yan Jiun-Lin, Toh Cheng-Hong, Ko Li, Wei Kuo-Chen, Chen Pin-Yuan
Department of Neurosurgery, Keelung Chang Gung Memorial Hospital, Keelung 204, Taiwan.
Department of Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan.
Cancers (Basel). 2021 Apr 21;13(9):2006. doi: 10.3390/cancers13092006.
The phenotypes of glioblastoma (GBM) progression after treatment are heterogeneous in both imaging and clinical prognosis. This study aims to apply radiomics and neural network analysis to preoperative multimodal MRI data to characterize tumor progression phenotypes. We retrospectively reviewed 41 patients with newly diagnosed cerebral GBM from 2009-2016 who comprised the machine learning training group, and prospectively included 18 patients from 2017-2018 for data validation. Preoperative MRI examinations included structural MRI, diffusion tensor imaging, and perfusion MRI. Tumor progression patterns were categorized as diffuse or localized. A supervised machine learning model and neural network-based models (VGG16 and ResNet50) were used to establish the prediction model of the pattern of progression. The diffuse progression pattern showed a significantly worse prognosis regarding overall survival ( = 0.032). A total of 153 of the 841 radiomic features were used to classify progression patterns using different machine learning models with an overall accuracy of 81% (range: 77.5-82.5%, AUC = 0.83-0.89). Further application of the pretrained ResNet50 and VGG 16 neural network models demonstrated an overall accuracy of 93.1 and 96.1%. The progression patterns of GBM are an important prognostic factor and can potentially be predicted by combining multimodal MR radiomics with machine learning.
胶质母细胞瘤（GBM）治疗后的进展在影像学和临床预后方面均具有异质性。本研究旨在将放射组学和神经网络分析应用于术前多模态MRI数据，以表征肿瘤进展表型。我们回顾性分析了2009年至2016年新诊断的41例脑GBM患者，组成机器学习训练组，并前瞻性纳入了2017年至2018年的18例患者进行数据验证。术前MRI检查包括结构MRI、扩散张量成像和灌注MRI。肿瘤进展模式分为弥漫性或局限性。使用监督机器学习模型和基于神经网络的模型（VGG16和ResNet50）建立进展模式的预测模型。弥漫性进展模式在总生存期方面预后明显更差（ = 0.032）。使用不同的机器学习模型，共841个放射组学特征中的153个用于分类进展模式，总体准确率为81%（范围：77.5 - 82.5%，AUC = 0.83 - 0.89）。预训练的ResNet50和VGG 16神经网络模型的进一步应用显示总体准确率分别为93.1%和96.1%。GBM的进展模式是一个重要的预后因素，通过将多模态MR放射组学与机器学习相结合有可能进行预测。
