
# The Cancer Genome Atlas Ovarian Cancer (NSCLC-Radiomics)

The models featured in this repository uses images from the publicly available [NSCLC-Radiomics](https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics) Dataset. 

Download the data from TCIA with **Descriptive Directory Name** download option.

## Converting Format

Convert DICOM images and segmentation to NIFTI format using [pydicom](https://pydicom.github.io/) and [pydicom-seg](https://razorx89.github.io/pydicom-seg/guides/read.html). Run:

```shell
user@machine:~/NSCLC-Radiomics-NIFTI$ python convert.py
```

## Segmentations

Images will have one of the following segmentation files:

```
─ seg-Esophagus.nii.gz
─ seg-GTV-1.nii.gz
─ seg-Heart.nii.gz
─ seg-Lung-Left.nii.gz
─ seg-Lung-Right.nii.gz
─ seg-Spinal-Cord.nii.gz
```

## Requirements

```
dicom2nifti==2.4.6
pandas==1.5.0
pydicom==2.3.1
pydicom-seg==0.4.1
SimpleITK==2.2.0
tqdm==4.64.1
```

## Citation

If using this repository, please cite the following works:

```
Data Citation

  Aerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P.,
  Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F.,
  Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., Lambin, P. (2019).
  Data From NSCLC-Radiomics (version 4) [Data set].
  The Cancer Imaging Archive.
  https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI 

Publication Citation

  Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S.,
  Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M.,
  Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., Lambin, P. (2014, June 3).
  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
  Nature Communications. Nature Publishing Group.
  https://doi.org/10.1038/ncomms5006  (link)

TCIA Citation

  Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M,
  Tarbox L, Prior F.
  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository,
  Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057.
  https://doi.org/10.1007/s10278-013-9622-7
```
license:cc-by-3.0 medical region:us
{"license": "cc-by-3.0", "tags": ["medical"], "viewer": false}
TAGS
#license-cc-by-3.0 #medical #region-us 


# The Cancer Genome Atlas Ovarian Cancer (NSCLC-Radiomics)

The models featured in this repository uses images from the publicly available NSCLC-Radiomics Dataset. 

Download the data from TCIA with Descriptive Directory Name download option.

## Converting Format

Convert DICOM images and segmentation to NIFTI format using pydicom and pydicom-seg. Run:



## Segmentations

Images will have one of the following segmentation files:



## Requirements



If using this repository, please cite the following works:


# The Cancer Genome Atlas Ovarian Cancer (NSCLC-Radiomics)

The models featured in this repository uses images from the publicly available NSCLC-Radiomics Dataset. 

Download the data from TCIA with Descriptive Directory Name download option. ## Converting Format

Convert DICOM images and segmentation to NIFTI format using pydicom and pydicom-seg. Run: ## Segmentations

Images will have one of the following segmentation files: ## Requirements



If using this repository, please cite the following works:
TAGS
#license-cc-by-3.0 #medical #region-us 
 # The Cancer Genome Atlas Ovarian Cancer (NSCLC-Radiomics)

The models featured in this repository uses images from the publicly available NSCLC-Radiomics Dataset. 

Download the data from TCIA with Descriptive Directory Name download option. ## Converting Format

Convert DICOM images and segmentation to NIFTI format using pydicom and pydicom-seg. Run: ## Segmentations

Images will have one of the following segmentation files: ## Requirements



If using this repository, please cite the following works:
passage: TAGS
#license-cc-by-3.0 #medical #region-us 
# The Cancer Genome Atlas Ovarian Cancer (NSCLC-Radiomics)

The models featured in this repository uses images from the publicly available NSCLC-Radiomics Dataset. 

Download the data from TCIA with Descriptive Directory Name download option.## Converting Format

Convert DICOM images and segmentation to NIFTI format using pydicom and pydicom-seg. Run:## Segmentations

Images will have one of the following segmentation files:## Requirements



If using this repository, please cite the following works: