Lung Treatment response

Machine learning model to investigate lung cancer response after SBRT (radiotherapy treatment).

We investigated the clinical and radiomics data regarding lung cancer response after SBRT for the following predictions:

• survival
• local relapse
• remote relapse

We also investigated feature removal, data-preprocessing and prediction timeframe.

Authors: Camille Invernizzi, Pierre-Louis Benveniste

1. Instructions to install everything

Create a new environment

conda create -n venv_lung_response python=3.9

Activate it

conda activate venv_lung_response

Then install all required libraries

pip install -r requirements.txt

2. Code in this repository

The code is divided in two folders:

• data_preprocessing: here we investigate the data for data preprocessing, dataset merging, dataset statistics and feature elimination.
• model training: here we investigate the training of model prediction for survival, local relapse and final relapse.

NB: the investigations are detailed in the issues.

3. Performing a prediction

After doing the steps in installation section (section 1) and downloading the model from the release, you can run an inference using the file predict_3year_survival.py. Use the following command:

python predict_3year_survival.py --model-path PATH/TO/MODEL --sex X --BMI X --score_charlson X --smoke_cessation X --dose_tot X --BED_10 X --MeanIntensity X --IntensitySkewness X --IntensityKurtosis X --AreaUnderCurveCIVH X --RootMeanSquareIntensity X --IntensityHistogramMean X --IntensityHistogramVariance X --NGTDM_Strength X