Problem description
Predict the status of a genetic biomarker important for brain cancer treatment.
A malignant tumour in the brain is a life-threatening condition. Known as glioblastoma, it's both the most common form of brain cancer in adults and the one with the worst prognosis, with median survival being less than a year. The presence of a specific genetic sequence in the tumour known as MGMT promoter methylation has been shown to be a favourable prognostic factor and a strong predictor of responsiveness to chemotherapy.
Currently, genetic analysis of cancer requires surgery to extract a tissue sample. Then it can take several weeks to determine the genetic characterization of the tumour. Depending upon the results and type of initial therapy chosen, a subsequent surgery may be necessary. If an accurate method to predict the genetics of the cancer through imaging (i.e., radiogenomics) alone could be developed, this would potentially minimise the number of surgeries and refine the type of therapy required.
The Radiological Society of North America (RSNA) has teamed up with the Medical Image Computing and Computer Assisted Intervention Society (the MICCAI Society) to improve diagnosis and treatment planning for patients with glioblastoma.
The introduction of new and customised treatment strategies before surgery has the potential to improve the management, survival, and prospects of patients with brain cancer.
Problem source (URL)
https://www.kaggle.com/competitions/rsna-miccai-brain-tumor-radiogenomic-classification/overview
Citation:
Adam Flanders, Chris Carr, Evan Calabrese, FelipeKitamura, inversion, JeffRudie, John Mongan, Julia Elliott, Luciano Prevedello, Michelle Riopel, sprint, Spyridon Bakas, Ujjwal. (2021). RSNA-MICCAI Brain Tumour Radiogenomic Classification. Kaggle. https://kaggle.com/competitions/rsna-miccai-brain-tumor-radiogenomic-classification
Codebase description
This codebase predicts the genetic subtype of glioblastoma using MRI scans to train and test the EfficientNet3D image classification model to detect for the presence of MGMT promoter methylation.
(partly taken from the competition description https://www.kaggle.com/competitions/rsna-miccai-brain-tumor-radiogenomic-classification/overview)
Codebase source (URL)
https://www.kaggle.com/code/rluethy/efficientnet3d-with-one-mri-type/notebook
Dataset description (same as for Brain Tumour - EDA with Animations and Modelling)
This dataset is defined by three cohorts: Training, Validation (Public), and Testing (Private). The “Training” and the “Validation” cohorts were provided to the competition participants, whereas the “Testing” cohort was kept hidden at all times, during and after the competition.
These 3 cohorts are structured as follows: Each independent case has a dedicated folder identified by a five-digit number. Within each of these “case” folders, there are four sub-folders, each of them corresponding to each of the structural multi-parametric MRI (mpMRI) scans, in DICOM format. The exact mpMRI scans included are:
Fluid Attenuated Inversion Recovery (FLAIR)
T1-weighted pre-contrast (T1w)
T1-weighted post-contrast (T1Gd)
T2-weighted (T2)
Dataset source (URL) (same as for Brain Tumour - EDA with Animations and Modelling)
https://www.kaggle.com/competitions/rsna-miccai-brain-tumor-radiogenomic-classification/data
Citation:
U.Baid, et al., “The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumour Segmentation and Radiogenomic Classification”, arXiv:2107.02314, 2021.
