This inference code was tested on Ubuntu 18.04.3 LTS, conda version 4.8.0, python 3.9.9, fastai 2.5.3, cuda 11.2, pytorch 1.10 and cadene pretrained models 0.7.4.
Inference can be run on the GPU or CPU, and should work with ~4GB of GPU or CPU RAM. For GPU inference, a CUDA 10 capable GPU is required.
Model weights are available via Dropbox: https://www.dropbox.com/sh/ao93iyib605oq5w/AACuqINPnKF0k4ekMPtUSIExa?dl=0
This example is best run in a conda environment:
git lfs clone https://github.com/vineet1992/CXR-CV-Risk/
cd location_of_repo
mkdir models
###Download model weights from Dropbox and include them in the "models" directory
conda create -n CXR_CV python=3.9
conda activate CXR_CV
conda install -c fastai fastai ##follow instructions for your OS here: https://github.com/fastai/fastai>
## You may need to install pytorch according to CUDA version and OS first - see https://pytorch.org/get-started/locally/
conda install docopt
pip install pretrainedmodels==0.7.4
python run_cxr_cv_risk.py dummy_datasets/test_images/ path/to/model/weights/PLCO_CV_Risk_010422 output/output.csvDummy image files are provided in dummy_datasets/test_images/;
PLCO (NCT00047385) data used for model development and testing are available from the National Cancer Institute (NCI, https://biometry.nci.nih.gov/cdas/plco/). NLST (NCT01696968) testing data is available from the NCI (https://biometry.nci.nih.gov/cdas/nlst/) and the American College of Radiology Imaging Network (ACRIN, https://www.acrin.org/acrin-nlstbiorepository.aspx). Due to the terms of our data use agreement, we cannot distribute the original data. Please instead obtain the data directly from the NCI and ACRIN.
The model was developed using a 5-model ensemble approach, where each model training run used hyperparameters randomly chosen from the distributions below. These hyperparameter ranges were chosen based on empirical analysis in the tuning portion of the PLCO dataset. L-1 penalized regression was used to identify which models in the 5-model ensemble had independent predictive power for cardiovascular mortality. Ultimately, only 2 models were used in the final CXR-CVD-Risk model. Additional model development details are available in the manuscript's Supplement.
The main results of our initial study showed that the CXR-CVD-Risk model predicted 10-year cardiovascular events with similar performance to the ASCVD risk score in MGB patients who had a routine chest radiograph image. For full results please reference the paper. Here, we include an additional result comparing demographics and cardiovascular risk profiles of those that had discordant risk estimates from the CXR model and the ASCVD risk score. Overall, we find that those at high-risk according to the CXR model, but low risk according to the ASCVD risk score were generally younger, non-smokers, normotensive, and had lower LDL-C levels.
PLCO radiographs were provided as scanned TIF files by the NCI. TIFs were converted to PNGs with a minimum dimension of 512 pixels with ImageMagick v6.8.9-9.
Many of the PLCO radiographs were rotated 90 or more degrees. To address this, we developed a CNN to identify rotated radiographs. First, we trained a CNN using the resnet34 architecture to identify synthetically rotated radiographs from the CXR14 dataset. We then fine tuned this CNN using 11,000 manually reviewed PLCO radiographs. The rotated radiographs identified by this CNN in preprocessing/plco_rotation_github.csv were then corrected using ImageMagick.
cd path_for_PLCO_tifs
mogrify -path destination_for_PLCO_pngs -trim +repage -colorspace RGB -auto-level -depth 8 -resize 512x512^ -format png "*.tif"
cd path_for_PLCO_pngs
while IFS=, read -ra cols; do mogrify -rotate 90 "${cols[0]}"; done < /path_to_repo/preprocessing/plco_rotation_github.csvNLST radiographs were provided as DCM files by ACRIN. We chose to first convert them to TIF using DCMTK v3.6.1, then to PNGs with a minimum dimension of 512 pixels through ImageMagick to maintain consistency with the PLCO radiographs:
cd path_to_NLST_dcm
for x in *.dcm; do dcmj2pnm -O +ot +G $x "${x%.dcm}".tif; done;
mogrify -path destination_for_NLST_pngs -trim +repage -colorspace RGB -auto-level -depth 8 -resize 512x512^ -format png "*.tif"The orientation of several NLST chest radiographs was manually corrected:
cd destination_for_NLST_pngs
mogrify -rotate "90" -flop 204025_CR_2000-01-01_135015_CHEST_CHEST_n1__00000_1.3.51.5146.1829.20030903.1123713.1.png
mogrify -rotate "-90" 208201_CR_2000-01-01_163352_CHEST_CHEST_n1__00000_2.16.840.1.113786.1.306662666.44.51.9597.png
mogrify -flip -flop 208704_CR_2000-01-01_133331_CHEST_CHEST_n1__00000_1.3.51.5146.1829.20030718.1122210.1.png
mogrify -rotate "-90" 215085_CR_2000-01-01_112945_CHEST_CHEST_n1__00000_1.3.51.5146.1829.20030605.1101942.1.png
We thank the NCI and ACRIN for access to trial data, as well as the PLCO and NLST participants for their contribution to research. I would also like to thank the fastai and Pytorch communities as well as the National Academy of Medicine for their support of this work. A GPU used for this research was donated as an unrestricted gift through the Nvidia Corporation Academic Program. The statements contained herein are mine alone and do not represent or imply concurrence or endorsements by the above individuals or organizations.