Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas
To perform radiomics analysis for non-invasively predicting chromosome 1p/19q co-deletion in World Health Organization grade II and III (lower-grade) gliomas.
This retrospective study included 277 patients histopathologically diagnosed with lower-grade glioma. Clinical parameters were recorded for each patient. We performed a radiomics analysis by extracting 647 MRI-based features and applied the random forest algorithm to generate a radiomics signature for predicting 1p/19q co-deletion in the training cohort (n = 184). The clinical model consisted of pertinent clinical factors, and was built using a logistic regression algorithm. A combined model, incorporating both the radiomics signature and related clinical factors, was also constructed. The receiver operating characteristics curve was used to evaluate the predictive performance. We further validated the predictability of the three developed models using a time-independent validation cohort (n = 93).
The radiomics signature was constructed as an independent predictor for differentiating 1p/19q co-deletion genotypes, which demonstrated superior performance on both the training and validation cohorts with areas under curve (AUCs) of 0.887 and 0.760, respectively. These results outperformed the clinical model (AUCs of 0.580 and 0.627 on training and validation cohorts). The AUCs of the combined model were 0.885 and 0.753 on training and validation cohorts, respectively, which indicated that clinical factors did not present additional improvement for the prediction.
Our study highlighted that an MRI-based radiomics signature can effectively identify the 1p/19q co-deletion in histopathologically diagnosed lower-grade gliomas, thereby offering the potential to facilitate non-invasive molecular subtype prediction of gliomas.
KeywordsLower-grade glioma 1p/19q Co-deletion Prediction Radiomics Magnetic resonance imaging
This work was supported by the National Natural Science Foundation of China under Grant Numbers 81227901, 81527805, 81501616, and 81771924, the National Key Research and Development Program of China Grant under Grant Number 2106YFC0103702 and 2017YFA0205200. Olivier Gevaert is supported by the National Institute of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R01EB020527. The authors would like to express their deep appreciation to all anonymous reviewers for their kind comments.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.
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