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Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status
In this work, we aimed to evaluate the potential value of the machine learning (ML)–based MRI texture analysis for predicting 1p/19q codeletion status of lower-grade gliomas (LGG). This retrospective study was totally based on public data. We reduced the high-dimensionality of the radiomic data with collinearity analysis and ReliefF algorithm. Then, we used seven ML classifiers for the development of