extract annotations from database:
=====
https://github.com/fuentesdt/imagingsignature/blob/master/dicom_annotation_code.py#L52

save to nifti files
=====
https://github.com/fuentesdt/imagingsignature/blob/master/dicom_annotation_code.py#L72

organize data matrix
=====
make -k -i nifti
mysql  -re "call RandomForestHCCResponse.CRCPyRadMatrix ();"     | sed "s/\t/,/g;s/NULL//g" > datalocation/pyradiomics.csv

process image features
=====
pyradiomicsbatch -v 5 datalocation/pyradiomics.csv datalocation/pyradiomicsout.csv
head -n 1 datalocation/pyradiomicsout.csv > datalocation/pyradiomicsout_experimentalcohort.csv; grep "Experimental cohort" datalocation/pyradiomicsout.csv >>   datalocation/pyradiomicsout_experimentalcohort.csv
grep -v "Experimental cohort" datalocation/pyradiomicsout.csv >>  datalocation/pyradiomics_modeling.csv

compute classifier
=====

install.packages("rmarkdown",repos='http://cran.us.r-project.org')
install pandoc from  - https://github.com/jgm/pandoc/releases/tag/2.1.1

R markdown classifier: 


Rscript -e "rmarkdown::render( 'datamatrixModeling_binary.RMD', output_file = 'myfile.pdf',params = list( csvPath='datalocation/pyradiomics_modeling.csv', target='MutationalStatus', positive_class='mut', inputs=NULL , leaveOneOut=TRUE, test_csv='datalocation/pyradiomicsout_experimentalcohort.csv', rescale=FALSE, removeCorrelated=TRUE, plot=TRUE, semisupervised=FALSE, kClusters=as.numeric(9), genetic=FALSE, boruta=TRUE, univariate=TRUE, unipValThresh=0.05 ))"

Rscript -e "rmarkdown::render( 'datamatrixModeling_binary.RMD', output_file = 'myfileKras.pdf',params = list( csvPath='datalocation/pyradiomicsout.csv', target='MutationalStatusKras', positive_class='mut', inputs=NULL , leaveOneOut=TRUE, test_csv=NULL, rescale=FALSE, removeCorrelated=TRUE, plot=TRUE, semisupervised=FALSE, kClusters=as.numeric(9), genetic=FALSE, boruta=TRUE, univariate=TRUE, unipValThresh=0.05 ))"

Rscript -e "rmarkdown::render( 'datamatrixModeling_binary.RMD', output_file = 'myfileAPC.pdf',params = list( csvPath='datalocation/pyradiomicsout.csv', target='MutationalStatusAPC', positive_class='mut', inputs=NULL , leaveOneOut=TRUE, test_csv=NULL, rescale=FALSE, removeCorrelated=TRUE, plot=TRUE, semisupervised=FALSE, kClusters=as.numeric(9), genetic=FALSE, boruta=FALSE, univariate=TRUE, unipValThresh=0.15 ))"
Rscript -e "rmarkdown::render( 'datamatrixModeling_binary.RMD', output_file = 'myfilep53.pdf',params = list( csvPath='datalocation/pyradiomicsout.csv', target='MutationalStatusp53', positive_class='mut', inputs=NULL , leaveOneOut=TRUE, test_csv=NULL, rescale=FALSE, removeCorrelated=TRUE, plot=TRUE, semisupervised=FALSE, kClusters=as.numeric(9), genetic=FALSE, boruta=FALSE, univariate=TRUE, unipValThresh=0.15 ))"