Department of Radiological Sciences, University of California, Irvine, CA, USA.
Department of Radiology, E-DA Hospital, E-DA Cancer Hospital, I-Shou University, Kaohsiung, Taiwan.
Neuroradiology. 2019 Dec;61(12):1355-1364. doi: 10.1007/s00234-019-02259-0. Epub 2019 Jul 19.
A subset of skull base meningiomas (SBM) may show early progression/recurrence (P/R) as a result of incomplete resection. The purpose of this study is the implementation of MR radiomics to predict P/R in SBM.
From October 2006 to December 2017, 60 patients diagnosed with pathologically confirmed SBM (WHO grade I, 56; grade II, 3; grade III, 1) were included in this study. Preoperative MRI including T2WI, diffusion-weighted imaging (DWI), and contrast-enhanced T1WI were analyzed. On each imaging modality, 13 histogram parameters and 20 textural gray level co-occurrence matrix (GLCM) features were extracted. Random forest algorithms were utilized to evaluate the importance of these parameters, and the most significant three parameters were selected to build a decision tree for prediction of P/R in SBM. Furthermore, ADC values obtained from manually placed ROI in tumor were also used to predict P/R in SBM for comparison.
Gross-total resection (Simpson Grades I-III) was performed in 33 (33/60, 55%) patients, and 27 patients received subtotal resection. Twenty-one patients had P/R (21/60, 35%) after a postoperative follow-up period of at least 12 months. The three most significant parameters included in the final radiomics model were T1 max probability, T1 cluster shade, and ADC correlation. In the radiomics model, the accuracy for prediction of P/R was 90%; by comparison, the accuracy was 83% using ADC values measured from manually placed tumor ROI.
The results show that the radiomics approach in preoperative MRI offer objective and valuable clinical information for treatment planning in SBM.
一部分颅底脑膜瘤（SBM）由于手术切除不完全，可能会出现早期进展/复发（P/R）。本研究旨在利用磁共振影像组学来预测 SBM 的 P/R。
本研究纳入了 2006 年 10 月至 2017 年 12 月期间经病理证实的 60 例 SBM 患者（WHO 分级 I 级 56 例，II 级 3 例，III 级 1 例）。对所有患者术前 MRI 包括 T2WI、弥散加权成像（DWI）和对比增强 T1WI 进行分析。在每种成像模式上，提取 13 个直方图参数和 20 个灰度共生矩阵（GLCM）纹理特征。利用随机森林算法评估这些参数的重要性，并选择三个最重要的参数构建决策树，以预测 SBM 的 P/R。此外，还使用手动在肿瘤 ROI 上获得的 ADC 值来预测 SBM 的 P/R。
33 例患者（33/60，55%）行全切除（Simpson 分级 I-III），27 例患者行次全切除。术后至少随访 12 个月，21 例患者发生 P/R（21/60，35%）。最终的影像组学模型中包含的三个最重要的参数是 T1 最大概率、T1 聚类阴影和 ADC 相关性。在影像组学模型中，P/R 的预测准确率为 90%；相比之下，使用手动放置的肿瘤 ROI 上测量的 ADC 值，预测准确率为 83%。
结果表明，术前 MRI 的影像组学方法可为 SBM 的治疗计划提供客观且有价值的临床信息。
