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README.md

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 An unpublished automatic algorithm provided an initial mask of the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