Radiomics signature for temporal evolution and recurrence patterns of glioblastoma using multimodal magnetic resonance imaging
Radiomics signature for temporal evolution and recurrence patterns of glioblastoma using multimodal magnetic resonance imaging
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DOI：
10.1002/nbm.4647
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发表时间：
2021-11-11
影响因子：
2.9
通讯作者：
Ingalhalikar, Madhura
中科院分区：
文献类型：
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作者：
Chougule, Tanay;Gupta, Rakesh K.;Ingalhalikar, Madhura
胶质母细胞瘤是一种高度浸润性肿瘤，具有高复发倾向。复发的位置通常无法预测，取决于各种因素，包括手术切除边缘。目前，放射计划利用来自T2-FLAIR MRI的高强度信号，并被输送到由标准化指南定义的有限区域。为此，无创性早期预测和描绘复发可以帮助定制的靶向治疗，这可能会延迟复发，从而提高总生存率。在这项工作中，我们假设，基于放射组学的表型量化器可能支持检测复发之前，它是可视化的多模态MRI。我们采用回顾性纵向数据，从29名受试者的不同数量的时间点（3至13），其中包括胶质母细胞瘤复发。从多模态MRI（T1对比增强[T1 CE]，FLAIR和表观扩散系数）计算体素纹理和强度特征，主要是为了了解从术前MRI到复发的纵向放射组学变化，随后使用机器学习预测复发前143 +/- 42天的复发区域。T1 CE MRI一阶和灰度共生矩阵特征在检测局部复发中至关重要，而多模态灰度差矩阵和一阶特征对远处复发具有高度预测性，远处复发的体素检验准确率为80.1%，局部复发的准确率为71.4%。总之，我们的工作证实了使用基于放射组学的表型变化预测胶质母细胞瘤复发的一个进步，这些表型变化可能作为基于MR的生物标志物用于定制的治疗干预。
Glioblastoma is a highly infiltrative neoplasm with a high propensity of recurrence. The location of recurrence usually cannot be anticipated and depends on various factors, including the surgical resection margins. Currently, radiation planning utilizes the hyperintense signal from T2-FLAIR MRI and is delivered to a limited area defined by standardized guidelines. To this end, noninvasive early prediction and delineation of recurrence can aid in tailored targeted therapy, which may potentially delay the relapse, consequently improving overall survival. In this work, we hypothesize that radiomics-based phenotypic quantifiers may support the detection of recurrence before it is visualized on multimodal MRI. We employ retrospective longitudinal data from 29 subjects with a varying number of time points (three to 13) that includes glioblastoma recurrence. Voxelwise textural and intensity features are computed from multimodal MRI (T1-contrast enhanced [T1CE], FLAIR, and apparent diffusion coefficient), primarily to gain insights into longitudinal radiomic changes from preoperative MRI to recurrence and subsequently to predict the region of relapse from 143 +/- 42 days before recurrence using machine learning. T1CE MRI first-order and gray-level co-occurrence matrix features are crucial in detecting local recurrence, while multimodal gray-level difference matrix and first-order features are highly predictive of the distant relapse, with a voxelwise test accuracy of 80.1% for distant recurrence and 71.4% for local recurrence. In summary, our work exemplifies a step forward in predicting glioblastoma recurrence using radiomics-based phenotypic changes that may potentially serve as MR-based biomarkers for customized therapeutic intervention.