标题： 基于多参数磁共振成像估算的人类胶质瘤预测疾病组成可以预测内皮增殖、肿瘤分级和总体生存率。 显示英文标题

标题： Predicted disease compositions of human gliomas estimated from multiparametric MRI can predict endothelial proliferation, tumor grade, and overall survival

摘要： 背景与目的：活检是胶质瘤临床管理的主要决定因素，但因其侵入性且无法检测由于肿瘤异质性而相关的特征，因此存在局限性。 本研究的目的是评估基于体素的多参数MRI影像组学方法预测特征的准确性，并开发一种微创方法以客观评估肿瘤。 方法：使用12自由度仿射模型将多参数MRI数据配准到T1加权钆对比增强数据上。 回顾性收集的MRI数据包括T1加权、T1加权钆对比增强、T2加权、液体衰减反转恢复以及在1.5T或3.0T下获取的多b值扩散加权图像。 临床专家为患者子集上的五种疾病状态提供了体素级注释，以建立包含611,930个观察值的训练特征向量。 然后，使用25%留出设计训练了一个k最近邻（k-NN）分类器。 训练好的k-NN模型应用于十七名组织学确诊的胶质瘤患者的13,018,171个观察值。 采用线性回归检验总体生存期（OS）与预测疾病组成（PDC）和诊断年龄之间的关系（α=0.05）。 典型判别分析测试了PDC和诊断年龄是否能够区分临床、遗传和显微镜下的因素（α=0.05）。 结果：该模型预测体素标注类别的Dice相似性系数为94.34%±2.98%。 PDCs和诊断年龄的线性组合预测了OS（p=0.008）、分级（p=0.014）和内皮细胞增殖（p=0.003），但在预测TP53BP1和IDH1基因突变方面表现不足。 结论：这种基于体素的多参数MRI影像组学策略有可能成为临床医生管理胶质瘤患者的一种非侵入性决策辅助工具。 显示英文摘要

摘要： Background and Purpose: Biopsy is the main determinants of glioma clinical management, but require invasive sampling that fail to detect relevant features because of tumor heterogeneity. The purpose of this study was to evaluate the accuracy of a voxel-wise, multiparametric MRI radiomic method to predict features and develop a minimally invasive method to objectively assess neoplasms. Methods: Multiparametric MRI were registered to T1-weighted gadolinium contrast-enhanced data using a 12 degree-of-freedom affine model. The retrospectively collected MRI data included T1-weighted, T1-weighted gadolinium contrast-enhanced, T2-weighted, fluid attenuated inversion recovery, and multi-b-value diffusion-weighted acquired at 1.5T or 3.0T. Clinical experts provided voxel-wise annotations for five disease states on a subset of patients to establish a training feature vector of 611,930 observations. Then, a k-nearest-neighbor (k-NN) classifier was trained using a 25% hold-out design. The trained k-NN model was applied to 13,018,171 observations from seventeen histologically confirmed glioma patients. Linear regression tested overall survival (OS) relationship to predicted disease compositions (PDC) and diagnostic age (alpha = 0.05). Canonical discriminant analysis tested if PDC and diagnostic age could differentiate clinical, genetic, and microscopic factors (alpha = 0.05). Results: The model predicted voxel annotation class with a Dice similarity coefficient of 94.34% +/- 2.98. Linear combinations of PDCs and diagnostic age predicted OS (p = 0.008), grade (p = 0.014), and endothelia proliferation (p = 0.003); but fell short predicting gene mutations for TP53BP1 and IDH1. Conclusions: This voxel-wise, multi-parametric MRI radiomic strategy holds potential as a non-invasive decision-making aid for clinicians managing patients with glioma.