kwahid / Radiomic-Prediction-of-Tumor-Grade-and-Overall-Survival-from-the-BraTS-Glioma-Dataset

Folder corresponding to 2017 summer project at MD Anderson Cancer Center.

Radiomic-Prediction-of-Tumor-Grade-and-Overall-Survival-from-the-BraTS-Glioma-Dataset

Folder corresponding to 2017 summer project at MD Anderson Cancer Center. Correspondance: [e-mail].

This repo contains the following files:

PDF of project report (Radiomic Prediction of Tumor Grade and Overall Survival from the BraTS Glioma Dataset.pdf).

PDF of supplementary information (Radiomic Prediction of Tumor Grade and Overall Survival from the BraTS Glioma Dataset SI.pdf).

Folder containing csv files neccessary for running notebook (input files).

Jupyter notebook of code implementation (Radiomic Prediction of Tumor Grade and Overall Survival from the BraTS Glioma Dataset.ipynb).

HTML copy of Jupyter notebook (Radiomic+Prediction+of+Tumor+Grade+and+Overall+Survival+from+the+BraTS+Glioma+Dataset).

Parameter file used in radiomic feature extaction (Params.yaml).

Utilized the following python libraries in project:

pyradiomics to generate features of dataset. http://pyradiomics.readthedocs.io/en/latest/

pandas for data manipulation.

sklearn for machine learning.

imblearn for upsampling.

matplotlib for graphing.

seaborn for data visualizations.

statsmodels for ANOVA statistical test.