Deep Learning-Based Radiomics for Prognostic Stratification of Low-Grade Gliomas Using a Multiple-Gene Signature

To our knowledge, no previous study has applied a deep learning-based radiomic approach for novel biomarkers, such as the aforementioned multiple-gene genetic sig-natures. Our study utilized the 3-gene signature that stratifies LGG patients into high- and low-risk groups, according to Xiao et al. (doi:10.7717/peerj.8312). We aimed to determine whether neuroimaging features on preoperative MRI studies entered into a CNN pipeline could facilitate the proposed LGG stratification. The code was developed based on the project by Zunair et. al (https://keras.io/examples/vision/3D_image_classification).

Files

The .py files are sorted by the recommended order of running.

1. get_clinic.py: This file serves as getting clinical information and creating data for risk groups.
2. get_nifti.py: This file serves as reading the imaging data.
3. train.py: This file serves to train the models.
4. predict.py: This file serves to make predictions from trained models and get performance metrics.

augment.py, configs.py and generator.py should be in the working directory when running these .py files.

Authors of the Study

1. Mert Karabacak Department of Neurosurgery, Mount Sinai Health System, 1468 Madison Avenue, New York, NY, 10029, USA; mert.karabacak@mountsinai.org
2. Burak B. Ozkara Department of Neuroradiology, MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX, 77030, USA; bbozkara@mdanderson.org
3. Kaan Senparlak School of Computation, Information and Technology, Technical University of Munich, Theresienstr. 90, München, Bayern, 80333, Germany; kaansenparlak@hotmail.com
4. Sotirios Bisdas* Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London WC1N 3BG, UK; s.bisdas@ucl.ac.uk