用多序列MRI与新小波融合法提升胶质瘤分级准确率
Glioma Classification using Multi-sequence MRI and Novel Wavelets-based Feature Fusion
- 基于小波变换融合T1、T1CE、T2、FLAIR四类MRI特征
- 在BraTS 2018数据集上达96.19%召回率和94.60% AUC
- 适合医学影像辅助诊断系统开发者参考
胶质瘤是起源于神经胶质细胞的常见且异质性肿瘤，根据世界卫生组织标准可分为低级别胶质瘤（LGG）和高级别胶质瘤（HGG）。精准分类对治疗方案制定至关重要。磁共振成像（MRI）为非侵入性评估提供关键形态与位置信息，可基于纹理、灌注和扩散特性进行分类，有助于提高诊断准确性并实现个性化治疗。然而，肿瘤异质性和重叠的放射组学特征使分类困难。本文在多序列T1、T1增强（T1CE）、T2和液体抑制反转恢复（FLAIR）MRI图像上，采用新型小波基特征融合算法提取放射组学特征，并通过主成分分析降维，结合XGBoost、支持向量机（SVM）和随机森林分类器进行分类。结果表明，SVM在BraTS 2018数据集上表现最佳，准确率达90.17%，精确率为91.04%，召回率为96.19%，F1得分为93.53%，AUC为94.60%；在另一组测试中准确率为91.34%，精确率为93.05%，召回率为96.13%，F1得分为94.53%，AUC为93.71%。该方法具有应用于胶质瘤辅助诊断与分级系统的潜力。
原文摘要 · Abstract (English)
Glioma, a prevalent and heterogeneous tumor originating from the glial cells, can be differentiated as Low Grade Glioma (LGG) and High Grade Glioma (HGG) according to World Health Organization's norms. Classifying gliomas is essential for treatment protocols that depend extensively on subtype differentiation. For non-invasive glioma evaluation, Magnetic Resonance Imaging (MRI) offers vital information about the morphology and location of the the tumor. The versatility of MRI allows the classification of gliomas as LGG and HGG based on their texture, perfusion, and diffusion characteristics, and further for improving the diagnosis and providing tailored treatments. Nevertheless, the precise classification is complicated by tumor heterogeneity and overlapping radiomic characteristics. Thus, in this work, wavelet based novel fusion algorithm were implemented on multi-sequence T1, T1-contrast enhanced (T1CE), T2 and Fluid Attenuated Inversion Recovery (FLAIR) MRI images to compute the radiomics features. Furthermore, principal component analysis is applied to reduce the feature space and XGBoost, Support Vector Machine, and Random Forest Classifier are used for the classification. The result shows that the SVM algorithm performs comparatively well with an accuracy of 90.17%, precision of 91.04% and recall of 96.19%, F1-score of 93.53%, and AUC of 94.60% when implemented on BraTS 2018 dataset and with an accuracy of 91.34%, precision of 93.05% and recall of 96.13%, F1-score of 94.53%, and AUC of 93.71% for BraTS 2018 dataset. Thus, the proposed algorithm could be potentially implemented for the computer-aided diagnosis and grading system for gliomas.
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