School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, People's Republic of China.
Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, 710121, People's Republic of China.
Phys Med Biol. 2023 Jun 8;68(12). doi: 10.1088/1361-6560/acd6d2.
Aiming at accurate survival prediction of Glioblastoma (GBM) patients following radiation therapy, we developed a subregion-based survival prediction framework via a novel feature construction method on multi-sequence MRIs. The proposed method consists of two main steps: (1) a feature space optimization algorithm to determine the most appropriate matching relation derived between multi-sequence MRIs and tumor subregions, for using multimodal image data more reasonable; (2) a clustering-based feature bundling and construction algorithm to compress the high-dimensional extracted radiomic features and construct a smaller but effective set of features, for accurate prediction model construction. For each tumor subregion, a total of 680 radiomic features were extracted from one MRI sequence using Pyradiomics. Additional 71 geometric features and clinical information were collected resulting in an extreme high-dimensional feature space of 8231 to train and evaluate the survival prediction at 1 year, and the more challenging overall survival prediction. The framework was developed based on 98 GBM patients from the BraTS 2020 dataset under five-fold cross-validation, and tested on an external cohort of 19 GBM patients randomly selected from the same dataset. Finally, we identified the best matching relationship between each subregion and its corresponding MRI sequence, a subset of 235 features (out of 8231 features) were generated by the proposed feature bundling and construction framework. The subregion-based survival prediction framework achieved AUCs of 0.998 and 0.983 on the training and independent test cohort respectively for 1 year survival prediction, compared to AUCs of 0.940 and 0.923 for survival prediction using the 8231 initial extracted features for training and validation cohorts respectively. Finally, we further constructed an effective stacking structure ensemble regressor to predict the overall survival with the C-index of 0.872. The proposed subregion-based survival prediction framework allow us to better stratified patients towards personalized treatment of GBM.
针对接受放疗后的胶质母细胞瘤 (GBM) 患者的精确生存预测，我们开发了一种基于子区域的生存预测框架，该框架通过一种新的特征构建方法在多序列 MRI 上实现。该方法包括两个主要步骤：(1) 特征空间优化算法，用于确定多序列 MRI 和肿瘤子区域之间最合适的匹配关系，以便更合理地使用多模态图像数据；(2) 基于聚类的特征捆绑和构建算法，用于压缩高维提取的放射组学特征，并构建更小但有效的特征集，从而构建准确的预测模型。对于每个肿瘤子区域，总共从一个 MRI 序列中使用 Pyradiomics 提取了 680 个放射组学特征。收集了额外的 71 个几何特征和临床信息，导致特征空间的维度高达 8231，用于训练和评估 1 年的生存预测，以及更具挑战性的总生存预测。该框架是基于 BraTS 2020 数据集的 98 名 GBM 患者在五折交叉验证的基础上开发的，并在来自同一数据集的 19 名 GBM 患者的外部队列上进行了测试。最后，我们确定了每个子区域与其对应 MRI 序列之间的最佳匹配关系，通过所提出的特征捆绑和构建框架生成了 235 个特征（在 8231 个特征中）的子集。基于子区域的生存预测框架在训练和独立测试队列上分别实现了 1 年生存预测的 AUC 为 0.998 和 0.983，而使用 8231 个初始提取特征进行训练和验证队列的生存预测的 AUC 分别为 0.940 和 0.923。最后，我们进一步构建了有效的堆叠结构集成回归器，以预测整体生存率，C 指数为 0.872。所提出的基于子区域的生存预测框架使我们能够更好地对患者进行分层，从而实现 GBM 的个性化治疗。
