Analysis of postprocessing steps for residue function dependent dynamic susceptibility contrast (DSC)-MRI biomarkers and their clinical impact on glioma grading for both 1.5 and 3T
Analysis of postprocessing steps for residue function dependent dynamic susceptibility contrast (DSC)-MRI biomarkers and their clinical impact on glioma grading for both 1.5 and 3T
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DOI：
10.1002/jmri.26837
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发表时间：
2020-02-01
影响因子：
4.4
通讯作者：
Quarles, C. Chad
中科院分区：
文献类型：
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作者：
Bell, Laura C.;Stokes, Ashley M.;Quarles, C. Chad
背景 动态磁敏对比 (DSC)-MRI 分析流程因研究和地点而异，可能会混淆衍生生物标志物的临床价值和使用。目的/假设 探讨计算脑血量 (CBV) 和残留功能相关参数（脑血流量 [CBF]、平均通过时间 [MTT]、毛细血管通过异质性 [CTH]）的后处理步骤如何影响神经胶质瘤分级。研究类型 癌症影像档案 (TCIA) 的回顾性研究。人群 49 名患有低级别和高级别神经胶质瘤的受试者。使用单回波平面成像 (EPI) 采集的场强/序列 1.5 和 3.0T 临床系统。评估手动感兴趣区域 (ROI) 由 TCIA 提供，并通过 k 均值聚类生成自动分段的 ROI。 CBV是根据传统方程计算的。通过两种反卷积方法发现残基功能依赖性生物标志物（CBF、MTT、CTH）：循环离散化，然后采用信噪比 (SNR) 适应的特征值阈值（方法 1）和基于 L 曲线的 Tikhonov 正则化的 Volterra 离散化（方法 2）。统计测试 方差分析、受试者工作特征 (ROC) 和逻辑回归测试。结果仅MTT无法统计区分胶质瘤分级（P > 0.139）。标准化后，肿瘤 CBF、CTH 和 CBV 在场强上没有差异 (P > 0.141)。归一化为自动分割区域的生物标志物与手动绘制的 ROI 表现相同（rCTH AUROC 为 0.73 与 0.74）或更好（rCBF AUROC 从 0.74-0.84 增加；rCBV AUROC 增加 0.78-0.86）。通过更新当前的反卷积步骤（方法 2），rCTH 可以充当神经胶质瘤分级的分类器（P < 0.007），但如果通过当前的传统 DSC 方法（方法 1）处理则不能（P > 0.577）。最后，高阶生物标志物（例如，rCBF 和 rCTH）与 rCBV 一起将区分肿瘤分级的 AUROC 增加至 0.92，而单独 rCBV 的 AUROC 为 0.78 和 0.86（分别为手动和自动参考区域）。数据结论 通过优化的分析流程，与单独的 CBV 相比，高阶灌注生物标志物（rCBF 和 rCTH）可改善神经胶质瘤分级。此外，后处理步骤会影响神经胶质瘤分级所需的阈值。技术功效：第 2 阶段 J. Magn。共振。成像 2020；51：547-553。
Background Dynamic susceptibility contrast (DSC)-MRI analysis pipelines differ across studies and sites, potentially confounding the clinical value and use of the derived biomarkers. Purpose/Hypothesis To investigate how postprocessing steps for computation of cerebral blood volume (CBV) and residue function dependent parameters (cerebral blood flow [CBF], mean transit time [MTT], capillary transit heterogeneity [CTH]) impact glioma grading. Study Type Retrospective study from The Cancer Imaging Archive (TCIA). Population Forty-nine subjects with low- and high-grade gliomas. Field Strength/Sequence 1.5 and 3.0T clinical systems using a single-echo echo planar imaging (EPI) acquisition. Assessment Manual regions of interest (ROIs) were provided by TCIA and automatically segmented ROIs were generated by k-means clustering. CBV was calculated based on conventional equations. Residue function dependent biomarkers (CBF, MTT, CTH) were found by two deconvolution methods: circular discretization followed by a signal-to-noise ratio (SNR)-adapted eigenvalue thresholding (Method 1) and Volterra discretization with L-curve-based Tikhonov regularization (Method 2). Statistical Tests Analysis of variance, receiver operating characteristics (ROC), and logistic regression tests. Results MTT alone was unable to statistically differentiate glioma grade (P > 0.139). When normalized, tumor CBF, CTH, and CBV did not differ across field strengths (P > 0.141). Biomarkers normalized to automatically segmented regions performed equally (rCTH AUROC is 0.73 compared with 0.74) or better (rCBF AUROC increases from 0.74-0.84; rCBV AUROC increases 0.78-0.86) than manually drawn ROIs. By updating the current deconvolution steps (Method 2), rCTH can act as a classifier for glioma grade (P < 0.007), but not if processed by current conventional DSC methods (Method 1) (P > 0.577). Lastly, higher-order biomarkers (eg, rCBF and rCTH) along with rCBV increases AUROC to 0.92 for differentiating tumor grade as compared with 0.78 and 0.86 (manual and automatic reference regions, respectively) for rCBV alone. Data Conclusion With optimized analysis pipelines, higher-order perfusion biomarkers (rCBF and rCTH) improve glioma grading as compared with CBV alone. Additionally, postprocessing steps impact thresholds needed for glioma grading. Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:547-553.