Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma

• Published in: European journal of radiology 2019-09, Vol.118, p.81-87
• Main Authors: Li, Longfei, Wang, Ke, Ma, Xiujian, Liu, Zhenyu, Wang, Shuo, Du, Jiang, Tian, Kaibing, Zhou, Xuezhi, wei, Wei, Sun, Kai, Lin, Yusong, Wu, Zhen, Tian, Jie
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Aged; Algorithms; Child; Child, Preschool; Chondrosarcoma; Chondrosarcoma - pathology; Chordoma; Chordoma - pathology; Cohort Studies; Diagnosis, Differential; Female; Humans; Male; Middle Aged; Multiparametric Magnetic Resonance Imaging - methods; Multiparametric MRI; Prognosis; Radiomics; Retrospective Studies; ROC Curve; Skull Base Neoplasms - pathology; Support Vector Machine
• ISSN: 0720-048X; 1872-7727
• DOI: 10.1016/j.ejrad.2019.07.006

•Preoperative identification of chordoma and chondrosarcoma can help treatment plan.•Radiomics can differentiate chordoma and chondrosarcoma preoperatively.•Radiomics based on multiparametric MRI showed good differentiative performance.•Multiparametric MRI has better differentiative performance than single MR sequence. 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