| --- |
| 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 An unpublished automatic algorithm provided an initial mask of the |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} |
| } |
| ``` |
|
|
