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Fig. 2 | BMC Neurology

Fig. 2

From: Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images

Fig. 2

The main procedure of the radiomic strategy for preoperative ODGs grading. Based on T1 CE and FLAIR data (a) and tumor volume of interest (VOI) manually drawn on resampled T1 CE and FLAIR images (b), a group of parametric images are derived and the corresponding parametric maps of the whole tumor region are extracted (c). Utilizing radiomic features analysis; a big collection of tumor parameter attributes was acquired for the following machine learning process (d). Feature selection methods were implemented and compared using random forest (RF) classifier with additional discussion on model parameters to construct the optimal ODG grading model (e)

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BMC Neurology

ISSN: 1471-2377

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