Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, Henan, 450052, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100050, China.
Eur J Radiol. 2019 Sep;118:81-87. doi: 10.1016/j.ejrad.2019.07.006. Epub 2019 Jul 5.
Patients with skull base chordoma and chondrosarcoma have different prognoses and are not readily differentiated preoperatively on imaging. Multiparametric magnetic resonance imaging (MRI) is a routine diagnostic tool that can noninvasively characterize the salient characteristics of tumors. In the present study, we developed and validated a preoperative multiparametric MRI-based radiomic signature for differentiating these tumors.
This retrospective study enrolled 210 patients and consecutively divided them into the primary and validation cohorts. A total of 1941 radiomic features were acquired from preoperative T1-weighted imaging, T2-weighted imaging and contrast-enhanced T1-weighted imaging for each patient. The most discriminative features were selected by minimum-redundancy maximum-relevancy and recursive feature elimination algorithms in the primary cohort. The multiparametric and single-sequence MRI signatures were constructed with the selected features using a support vector machine model in the primary cohort. The ability of the novel radiomic signatures to differentiate chordoma from chondrosarcoma were assessed using receiver operating characteristic curve analysis in the validation cohort.
The multiparametric radiomic signature, which consisted of 11 selected features, reached an area under the receiver operating characteristic curve of 0.9745 and 0.8720 in the primary and validation cohorts, respectively. Moreover, compared with each single-sequence MRI signature, the multiparametric radiomic signature exhibited better classification performance with significant improvement (p < 0.05, Delong's test) in the primary cohorts.
By combining features from three MRI sequences, the multiparametric radiomics signature can accurately and robustly differentiate skull base chordoma from chondrosarcoma.
颅底脊索瘤和软骨肉瘤患者的预后不同，术前影像学检查不易区分。多参数磁共振成像（MRI）是一种常规的诊断工具，可以无创地对肿瘤的显著特征进行定性。本研究旨在开发和验证一种基于术前多参数 MRI 的放射组学特征，用于区分这些肿瘤。
本回顾性研究纳入了 210 名患者，并将其连续分为主要队列和验证队列。对每位患者的术前 T1 加权成像、T2 加权成像和增强 T1 加权成像采集了 1941 个放射组学特征。在主要队列中，通过最小冗余最大相关性和递归特征消除算法选择最具鉴别力的特征。使用支持向量机模型，利用所选特征构建多参数和单序列 MRI 特征，在主要队列中对新的放射组学特征区分脊索瘤和软骨肉瘤的能力进行评估。
多参数放射组学特征由 11 个选定的特征组成，在主要和验证队列中的曲线下面积分别为 0.9745 和 0.8720。此外，与每个单序列 MRI 特征相比，多参数放射组学特征在主要队列中的分类性能更好，差异具有统计学意义（p < 0.05，DeLong 检验）。
通过结合三个 MRI 序列的特征，多参数放射组学特征可以准确、稳健地区分颅底脊索瘤和软骨肉瘤。
