Hsu Justin Bo-Kai, Lee Gilbert Aaron, Chang Tzu-Hao, Huang Shiu-Wen, Le Nguyen Quoc Khanh, Chen Yung-Chieh, Kuo Duen-Pang, Li Yi-Tien, Chen Cheng-Yu
Department of Medical Research, Taipei Medical University Hospital, Taipei 110, Taiwan.
Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan.
Cancers (Basel). 2020 Oct 19;12(10):3039. doi: 10.3390/cancers12103039.
Characterization of immunophenotypes in glioblastoma (GBM) is important for therapeutic stratification and helps predict treatment response and prognosis. Radiomics can be used to predict molecular subtypes and gene expression levels. However, whether radiomics aids immunophenotyping prediction is still unknown. In this study, to classify immunophenotypes in patients with GBM, we developed machine learning-based magnetic resonance (MR) radiomic models to evaluate the enrichment levels of four immune subsets: Cytotoxic T lymphocytes (CTLs), activated dendritic cells, regulatory T cells (Tregs), and myeloid-derived suppressor cells (MDSCs). Independent testing data and the leave-one-out cross-validation method were used to evaluate model effectiveness and model performance, respectively. We identified five immunophenotypes (G1 to G5) based on the enrichment level for the four immune subsets. G2 had the worst prognosis and comprised highly enriched MDSCs and lowly enriched CTLs. G3 had the best prognosis and comprised lowly enriched MDSCs and Tregs and highly enriched CTLs. The average accuracy of T1-weighted contrasted MR radiomics models of the enrichment level for the four immune subsets reached 79% and predicted G2, G3, and the "immune-cold" phenotype (G1) according to our radiomics models. Our radiomic immunophenotyping models feasibly characterize the immunophenotypes of GBM and can predict patient prognosis.
胶质母细胞瘤（GBM）免疫表型的特征对于治疗分层很重要，有助于预测治疗反应和预后。放射组学可用于预测分子亚型和基因表达水平。然而，放射组学是否有助于免疫表型预测仍不清楚。在本研究中，为了对GBM患者的免疫表型进行分类，我们开发了基于机器学习的磁共振（MR）放射组学模型，以评估四种免疫亚群的富集水平：细胞毒性T淋巴细胞（CTL）、活化树突状细胞、调节性T细胞（Treg）和髓源性抑制细胞（MDSC）。分别使用独立测试数据和留一法交叉验证方法来评估模型有效性和模型性能。我们基于四种免疫亚群的富集水平确定了五种免疫表型（G1至G5）。G2预后最差，包含高度富集的MDSC和低度富集的CTL。G3预后最好，包含低度富集的MDSC和Treg以及高度富集的CTL。四种免疫亚群富集水平的T1加权对比MR放射组学模型的平均准确率达到79%，并根据我们的放射组学模型预测了G2、G3和“免疫冷”表型（G1）。我们的放射组学免疫表型模型可有效地对GBM的免疫表型进行特征描述，并能预测患者预后。
