This repository contains data and code accompanying our publication on "Repeatability of Multiparametric Prostate MRI Radiomics Features"
Schwier, M., van Griethuysen, J., Vangel, M. G., Pieper, S., Peled, S., Tempany, C., Aerts, H. J. W. L., Kikinis, R., Fennessy, F. M. & Fedorov, A. Repeatability of Multiparametric Prostate MRI Radiomics Features. Sci. Rep. 9, 9441 (2019). https://www.nature.com/articles/s41598-019-45766-z
Preprint: https://arxiv.org/abs/1807.06089
NOTE Some specific adaptions for the figure generations have been done for the Nature Scientific Reports version. You can find these in the "ScientificReportsRevision*" branches.
The EvalData folder contains all extracted features we used in the study.
The files are in CSV format. Each row contains all features extracted for one image and mask combination. The organization and naming of the columns is directly taken from pyradiomics. We just added a few columns containing some additional meta information about the image/mask from which the features were derived.
The first few columns contain general info about the feature extraction (prefixed with "general_info"):
| Column Name (w/o prefix) | Meaning |
|---|---|
| BoundingBox | The bounding box considered around the mask |
| EnabledImageTypes | Indicates which pre-filtering options were activated for the extraction |
| GeneralSettings | Indicates which general settings were activated for the extraction (e.g. normalization, resampling, ...) |
| ImageHash | Unique identifier of the image |
| ImageSpacing | Voxel spacing |
| MaskHash | Unique identifier of the mask |
| NumpyVersion | Numpy version used by pyradiomics |
| PyWaveletVersion | PyWavelet version used by pyradiomics |
| SimpleITKVersion | SimpleITK version used by pyradiomics |
| Version | Pyradiomics version used for the extraction |
| VolumeNum | Number of zones (connected components) within the mask for the specified label |
| VoxelNum | Number of voxels in the mask |
Afterwards follow the columns for each feature. The feature column names follow this pattern:
[pre-filter]_[feature group]_[feature name]
For example:
- original_shape_Volume
- wavelet-HH_glcm_JointEnergy
At the end we added a few columns with additional meta information:
| Column Name | Meaning |
|---|---|
| study | Identifying the study this image belongs to |
| series | Identifying the series this image belongs to |
| canonicalType | Type/modality of the image (e.g. ADC, T2w, SUB) |
| segmentedStructure | Type of structure segmented by the mask |
The filenames of the feature data CSVs also contain some additional meta information about their content. The jupyter notebook for generating figures will parse this information and save it with the statistics it creates from the feature data (so you don't really have to worry about these too much).
The following table explains the different "codes" in the filename of a feature CSV file:
| File name contains | Meaning |
|---|---|
| FullStudySettings | Simply indicates that the extraction settings were according to this study (we also did smaller studies) |
| noNormalization | Indicates that the default pyradiomics whole-image normalization was deactivated |
| 2d/3D | Indicates if texture features were computed in 2D or 3D |
| biasCorrected | Indicates that we applied bias correction to the T2w images before processing them with pyradiomics |
| TP2Registered | Indicates that for the T2w images we didn't use the first manual segmentation but used registration to transfer the second timepoint masks to the first timepoint (see paper for more info) |
| MuscleRefNorm | Indicates that we normalized the T2w images against a consistent reference region in muscle tissue |
| T2AX | Contains only results for T2w images |
| bin10/bin15/bin20/bin40 | Bin size used for texture feature computation |
The jupyter notebooks require Python >= 3.6.
The FullStudy_RepeatabilityStudy.ipynb contains the code that was used to generate the figures for the pre-print paper. It also contains additional figures and can be used as a good basis to further analyze the data and create your own additional figures.
This notebook should run out-of-the-box, if you have the whole repository cloned and all Python dependencies installed (see imports in notebook). The generated figures will be saved into the EvalData/plots folder.
Re-running the feature extraction (i.e. re-creating the files you find in the EvalData folder) does not work out-of-the-box. You will need to request access and download the QIN-PROSTATE-Repeatability TCIA collection (check the paper for details). You can then use 3D Slicer to convert the data into "mpReview" style data. This is the structure we used for running the extraction with pyradiomics. You can use the mpReviewPreprocessor converter utility of the 3D Slicer Multiparametric Review (mpReview) module to convert the TCIA data into the required format (see the mpReview readme). However, as noted in the paper, we also performed additional processing to create variations of the data, like bias correction and registration. Please see the paper for details (all non-pyradiomics pre-processing was done with 3D Slicer).
The FullStudy_ExtractPyRadiomics.ipynb should give you an idea how we performed the feature extraction. You will also need the pyradiomics library. Note that it is not guaranteed that a different version of pyradiomics will create the same results for all features. Check the paper as well as the feature data files for the version that was used for this study. The basic settings for pyradiomics feature extractions, as used in the jupyter notebook, are located in the PyRadiomicsSettings folder.
The FullStudy_ExtractPyRadiomics.ipynb notebook requires a custom Python library, which you should download and put in a location where it will be found by Python (e.g. point the PYTHONPATH environment variable to the parent folder):
- mpReviewUtils (only required for the feature extraction notebook)