Abstract
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.
Highlights
Molecular pathology is valuable for determining strategies for treating gliomas and for predicting the prognostic outcome [1]
A total of 431 radiomic features were extracted from each sequence, and a total of 1,293 radiomic features grouped by age and gender were screened by the E-net in the nested crossvalidation
Six valuable radiomic features and age were selected in each outer loop (Table 2)
Summary
Molecular pathology is valuable for determining strategies for treating gliomas and for predicting the prognostic outcome [1]. Patients without chromosome 1p/19q co-deletions showed poor overall and progression-free survival [2, 3]. Neurosurgeons intended to protect the fundamental functions for patients whose eloquent cortices or white matter were invaded by gliomas, especially gliomas with 1p/19q co-deletions [4, 5]. The association between prognosis and extent of tumor resection in gliomas with 1p/19q co-deletion remains controversial, some studies have indicated that gross total resection showed better prognosis than that in subtotal resection [6, 7]. The prediction of the 1p/19q status before performing neurosurgery can aid in making customized neurosurgery plans and glioma management
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