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license: cc-by-3.0
task_categories:
- image-segmentation
tags:
- medical
- ct
- lung
- non-small-cell-lung-cancer
- tumor-segmentation
- radiogenomics
- dicom
- tcia
pretty_name: NSCLC-Radiogenomics
size_categories:
- n<1K
---
# NSCLC-Radiogenomics
Non-small cell lung cancer (NSCLC) radiogenomic dataset on TCIA: pretreatment
CT scans of 211 NSCLC patients with matching gene-expression, clinical, and
mutation data. **This HuggingFace mirror contains only the 144 patients with
a DICOM SEG of the primary lung tumor** (the segmentation-usable subset).
## Dataset Details
| Field | Value |
|---|---|
| Modality | CT (pretreatment, multi-vendor, multi-slice-thickness) |
| Body part | Lung (primary non-small cell lung cancer tumor) |
| Task | 3D binary segmentation (foreground = primary lung tumor) |
| Patients (TCIA) | 211 |
| Patients (this mirror) | 144 (those with a DICOM SEG) |
| CT series (uploaded) | 144 patients' worth |
| SEG series | 144 (one per patient) |
| Format | DICOM (images) + DICOM SEG (segmentations) |
| License | CC BY 3.0 |
| Original source | TCIA collection NSCLC Radiogenomics |
PT/PET series (480 series, 201 patients) are excluded — the published
segmentation masks are on CT only, so PET adds no signal for the
segmentation task.
## Annotation Pipeline
Per Bakr et al. (Sci. Data 2018), each segmentation was produced by:
1. An unpublished automatic algorithm provided an initial mask of the
primary tumor on the axial CT.
2. Thoracic radiologist **M.K.** (5+ yrs experience) viewed every case
and edited the masks as necessary in ePAD.
3. Thoracic radiologist **A.N.L.** (20+ yrs experience) independently
reviewed every case; disagreements were resolved by discussion
between M.K. and A.N.L.
4. **Final approval by A.N.L.**
Only one consolidated DICOM SEG is published per patient — it already
reflects this consensus, so callers do not have to pick between annotators.
## Cohorts (not splits)
| Cohort | Patients (TCIA) |
|---|---|
| R01 (Stanford + Palo Alto VA) | 162 |
| AMC (Stanford retrospective) | 49 |
| Total | 211 |
The R01/AMC split is encoded in patient IDs (`R01-xxx` vs `AMC-xxx`). The
SEG-having subset is not evenly distributed — see `series_to_patient.json`
for the exact list of included patients. There is no predefined
train/val/test split.
## Structure
```
images/<PatientID>/<StudyInstanceUID>/<SeriesInstanceUID>/*.dcm # CT
segmentations/<PatientID>/<StudyInstanceUID>/<SeriesInstanceUID>/*.dcm # DICOM SEG
series_to_patient.json # series-level metadata
```
Each SEG references its source CT via `ReferencedSeriesSequence` (top-level
CT SeriesInstanceUID) and `PerFrameFunctionalGroupsSequence →
DerivationImageSequence → SourceImageSequence` (per-frame source CT
SOPInstanceUID), enabling loss-less alignment to the CT slice grid.
## Notes for Loaders
- A patient may have multiple CT studies/series — pair the SEG to its
exact referenced CT series, not the first CT under the patient ID.
- **DICOM SEG ⇄ ITK conversion** is needed to get a labelmap volume; use
`dcmqi`'s `segimage2itkimage` or `pydicom-seg`.
- All masks are binary (single primary-tumor foreground class).
## Source
- TCIA collection: https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics/
- DOI: `10.7937/K9/TCIA.2017.7hs46erv`
- Released: 2015 (Version 4, updated 2021-06-01). Fully public since 2025-07-07.
## Citation
```bibtex
@article{bakr2018nsclcradiogenomics,
author = {Bakr, Shaimaa and Gevaert, Olivier and Echegaray, Sebastian and
Ayers, Kelsey and Zhou, Mu and Shafiq, Majid and Zheng, Hong and
Zhang, Weiruo and Leung, Ann and Kadoch, Michael and
Shrager, Joseph and Quon, Andrew and Rubin, Daniel L. and
Plevritis, Sylvia K. and Napel, Sandy},
title = {A radiogenomic dataset of non-small cell lung cancer},
journal = {Scientific Data},
volume = {5},
pages = {180202},
year = {2018},
doi = {10.1038/sdata.2018.202}
}
@misc{nsclcradiogenomics2017tcia,
author = {Bakr, S. and Gevaert, O. and Echegaray, S. and Ayers, K. and
Zhou, M. and Shafiq, M. and Zheng, H. and Zhang, W. and
Leung, A. and Kadoch, M. and Shrager, J. and Quon, A. and
Rubin, D. L. and Plevritis, S. K. and Napel, S.},
title = {Data for NSCLC Radiogenomics (Version 4) [Dataset]},
year = {2021},
publisher = {The Cancer Imaging Archive},
doi = {10.7937/K9/TCIA.2017.7hs46erv}
}
```
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