Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas

Frontiers in Oncology

Tao Jiang¹  Zhiyan Sun¹  Yinyan Wang¹  Shengyu Fang¹  Shaowu Li²  Yiming Li³  Ziwen Fan³  Yucha Liang³  Qiang Zhu³  Chunyao Zhou³  Yukun Liu³  Lei Wang³  Tianshi Li³  Hong Zhang³  Xing Liu⁴ 

[1] Beijing Neurosurgical Institute, Capital Medical University, Beijing, China;Department of Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China;Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China;Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China;

关键词: radiomics;    1p/19q co-deletion;    low grade glioma;    nested cross-validation;    machine learning;   

DOI  :  10.3389/fonc.2021.616740

来源: DOAJ

【 摘 要 】

PurposeThe present study aimed to preoperatively predict the status of 1p/19q based on radiomics analysis in patients with World Health Organization (WHO) grade II gliomas.MethodsThis retrospective study enrolled 157 patients with WHO grade II gliomas (76 patients with astrocytomas with mutant IDH, 16 patients with astrocytomas with wild-type IDH, and 65 patients with oligodendrogliomas with mutant IDH and 1p/19q codeletion). Radiomic features were extracted from magnetic resonance images, including T1-weighted, T2-weighted, and contrast T1-weighted images. Elastic net and support vector machines with radial basis function kernel were applied in nested 10-fold cross-validation loops to predict the 1p/19q status. Receiver operating characteristic analysis and precision-recall analysis were used to evaluate the model performance. Student’s t-tests were then used to compare the posterior probabilities of 1p/19q co-deletion prediction in the group with different 1p/19q status.ResultsSix valuable radiomic features, along with age, were selected with the nested 10-fold cross-validation loops. Five features showed significant difference in patients with different 1p/19q status. The area under curve and accuracy of the predictive model were 0.8079 (95% confidence interval, 0.733–0.8755) and 0.758 (0.6879–0.8217), respectively, and the F1-score of the precision-recall curve achieved 0.6667 (0.5201–0.7705). The posterior probabilities in the 1p/19q co-deletion group were significantly different from the non-deletion group.ConclusionCombined radiomics analysis and machine learning showed potential clinical utility in the preoperative prediction of 1p/19q status, which can aid in making customized neurosurgery plans and glioma management strategies before postoperative pathology.

【 授权许可】

Unknown