文章：

多序列MRI脑膜瘤分级中的双层次增强放射组学分析

Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading

原文发布日期：17 November 2023

DOI: 10.3390/cancers15225459

类型: Article

开放获取: 是

英文摘要：

Background: Preoperative, noninvasive prediction of meningioma grade is important for therapeutic planning and decision making. In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation (IA) and feature-level augmentation (FA) to tackle class imbalance and improve the predictive performance of radiomics for meningioma grading on Magnetic Resonance Imaging (MRI). Methods: This study recruited 160 consecutive patients with pathologically proven meningioma (129 low-grade (WHO grade I) tumors; 31 high-grade (WHO grade II and III) tumors) with preoperative multisequence MRI imaging. A dual-level augmentation strategy combining IA and FA was applied and evaluated in 100 repetitions in 3-, 5-, and 10-fold cross-validation. Results: The best area under the receiver operating characteristics curve of our method in 100 repetitions was ≥0.78 in all cross-validations. The corresponding cross-validation sensitivities (cross-validation specificity) were 0.72 (0.69), 0.76 (0.71), and 0.63 (0.82) in 3-, 5-, and 10-fold cross-validation, respectively. The proposed method achieved significantly better performance and distribution of results, outperforming single-level augmentation (IA or FA) or no augmentation in each cross-validation. Conclusions: The dual-level augmentation strategy using IA and FA significantly improves the performance of the radiomics model for meningioma grading on MRI, allowing better radiomics-based preoperative stratification and individualized treatment.

摘要翻译： 

背景：术前无创预测脑膜瘤分级对治疗规划和决策制定具有重要意义。本研究提出一种融合图像级增强（IA）与特征级增强（FA）的双层级增强策略，以解决磁共振成像（MRI）脑膜瘤分级中放射组学面临的类别不平衡问题并提升预测性能。方法：本研究连续纳入160例经病理证实的脑膜瘤患者（129例低级别（WHO I级）肿瘤；31例高级别（WHO II级和III级）肿瘤），所有患者术前均接受多序列MRI扫描。采用结合IA与FA的双层级增强策略，通过3折、5折及10折交叉验证进行100次重复实验评估。结果：在100次重复实验中，本方法在所有交叉验证中受试者工作特征曲线下面积最佳值均≥0.78。3折、5折和10折交叉验证对应的灵敏度（特异度）分别为0.72（0.69）、0.76（0.71）和0.63（0.82）。所提方法在每次交叉验证中均显著优于单层级增强（IA或FA）或无增强策略，表现出更优的性能指标和结果分布。结论：采用IA与FA的双层级增强策略显著提升了MRI脑膜瘤分级放射组学模型的性能，有助于实现更精准的术前放射组学分型与个体化治疗。

原文链接：

Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading