Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma.
Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma.
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DOI：
10.1371/journal.pone.0141506
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Li J
中科院分区：
文献类型：
--
作者：
Hu LS;Ning S;Eschbacher JM;Gaw N;Dueck AC;Smith KA;Nakaji P;Plasencia J;Ranjbar S;Price SJ;Tran N;Loftus J;Jenkins R;O'Neill BP;Elmquist W;Baxter LC;Gao F;Frakes D;Karis JP;Zwart C;Swanson KR;Sarkaria J;Wu T;Mitchell JR;Li J
基因图谱代表了神经肿瘤学的未来，但在胶质母细胞瘤（GBM）等异质性肿瘤中活检不足。对比增强MRI（CE-MRI）靶向增强核心（ENH），但仅在约60%的病例中产生足够的肿瘤。此外，CE-MRI对周围非增强实质或肿瘤周围脑（BAT）内的浸润性肿瘤定位较差，尽管表征普遍复发的该肿瘤段很重要。在这项研究中，我们使用多纹理分析和机器学习（ML）算法来分析多参数MRI，并产生新的图像，指示GBM中富含肿瘤的目标。我们招募了接受图像引导活检的原发性GBM患者，并获得了术前MRI：CE-MRI、动态敏感性加权对比增强MRI和扩散张量成像。在图像配准和活检位置的感兴趣区域放置之后，我们比较了相应样本的MRI指标和区域纹理与高与低肿瘤含量（≥80%与<80%肿瘤核）的组织学诊断。在训练集中，我们使用了三种纹理分析算法和三种ML方法来识别MRI纹理特征，这些特征优化了模型准确性以区分肿瘤内容。我们在一个单独的验证集中确认了模型的准确性。我们从ENH和BAT的18个GBM中收集了82份活检标本。基于MRI的模型在训练集（60例活检，11例患者）中诊断高肿瘤与低肿瘤的交叉验证准确率达到85%。该模型在验证集（22例活检，7例患者）中实现了81.8%的准确性。多参数MRI和纹理分析可以帮助表征和可视化GBM的空间组织学异质性，以识别区域肿瘤丰富的活检靶点。
Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM. We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set. We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients). Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.