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Radiomics Features from Public Medical Imaging Datasets

This repo gathers together the available open-source datasets suitable for radiomics research.

More information about each dataset and the extracted radiomics features as well as the labels can be accessed at https://radiomics.uk.

Datasets

Dataset Name Website Task Status
LIDC-IDRI TCIA binary classification ✔️
LNDb Zenodo multiclass classification ✔️
NSCLC-Radiogenomics TCIA survival analysis ✔️
NSCLC-Radiomics TCIA survival analysis ✔️
LUAD-CT-Survival TCIA binary classification ✔️
RIDER-Lung-CT TCIA repeatability ✔️
BraTS-2021 Kaggle binary classification ✔️
UCSF-PDGM TCIA binary classification, survival analysis ✔️
UPENN-GBM TCIA survival analysis ✔️
Meningioma-SEG-CLASS TCIA binary classification ✔️
LGG-1p19qDeletion TCIA binary classification ✔️
PI-CAI Grand Challenge multiclass classification ✔️
Prostate-MRI-US-Biopsy TCIA multiclass classification ✔️
QIN-PROSTATE TCIA repeatability ✔️
Head-Neck-Radiomics-HN1 TCIA survival analysis ✔️
HNSCC TCIA survival analysis ✔️
Head-Neck-PET-CT TCIA survival analysis ✔️
OPC-Radiomics TCIA survival analysis ✔️
QIN-HEADNECK TCIA repeatability ✔️
Colorectal-Liver-Metastases TCIA survival analysis ✔️
HCC-TACE-Seg TCIA survival analysis ✔️
C4KC-KiTS TCIA survival analysis ✔️
Soft-Tissue-Sarcoma TCIA binary classification ✔️
WORC GitHub binary classification ✔️

Folder structure

Each dataset adheres to the following structure, with minor variations:

<dataset_name>
├── raw
│   ├── dicom       # depending on the format of the original dataset
│   └── tables
└── derived
    ├── nifti       # converted to NIfTI format
    └── tables