University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland.
Department of Radiation Oncology, University of Bern, Bern, Switzerland.
Magn Reson Med. 2018 Dec;80(6):2339-2355. doi: 10.1002/mrm.27359. Epub 2018 Jun 12.
To improve the detection of peritumoral changes in GBM patients by exploring the relation between MRSI information and the distance to the solid tumor volume (STV) defined using structural MRI (sMRI).
Twenty-three MRSI studies (PRESS, TE 135 ms) acquired from different patients with untreated GBM were used in this study. For each MRSI examination, the STV was identified by segmenting the corresponding sMRI images using BraTumIA, an automatic segmentation method. The relation between different metabolite ratios and the distance to STV was analyzed. A regression forest was trained to predict the distance from each voxel to STV based on 14 metabolite ratios. Then, the trained model was used to determine the expected distance to tumor (EDT) for each voxel of the MRSI test data. EDT maps were compared against sMRI segmentation.
The features showing abnormal values at the longest distances to the tumor were: %NAA, Glx/NAA, Cho/NAA, and Cho/Cr. These four features were also the most important for the prediction of the distances to STV. Each EDT value was associated with a specific metabolic pattern, ranging from normal brain tissue to actively proliferating tumor and necrosis. Low EDT values were highly associated with malignant features such as elevated Cho/NAA and Cho/Cr.
The proposed method enables the automatic detection of metabolic patterns associated with different distances to the STV border and may assist tumor delineation of infiltrative brain tumors such as GBM.
通过探索磁共振波谱成像(MRSI)信息与基于结构磁共振成像(sMRI)的肿瘤实体体积(STV)之间的关系,提高胶质母细胞瘤(GBM)患者肿瘤周围变化的检测能力。
本研究使用了 23 项来自不同未经治疗的 GBM 患者的 MRSI 研究(PRESS,TE 135 ms)。对于每次 MRSI 检查,使用 BraTumIA 自动分割方法对相应的 sMRI 图像进行分割,以确定 STV。分析了不同代谢物比率与距离 STV 的关系。使用回归森林算法基于 14 种代谢物比率来训练预测每个体素距离 STV 的模型。然后,使用训练好的模型来确定 MRSI 测试数据中每个体素的预期肿瘤距离(EDT)。将 EDT 图与 sMRI 分割进行比较。
距离肿瘤最远的异常值特征包括:%NAA、Glx/NAA、Cho/NAA 和 Cho/Cr。这四个特征对于预测 STV 距离也非常重要。每个 EDT 值都与特定的代谢模式相关,从正常脑组织到活跃增殖的肿瘤和坏死。低 EDT 值与恶性特征高度相关,如升高的 Cho/NAA 和 Cho/Cr。
该方法能够自动检测与 STV 边界不同距离相关的代谢模式,有助于对胶质母细胞瘤等浸润性脑肿瘤进行肿瘤勾画。