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Datasets:
MedOtter
/
NSCLC-Radiogenomics

Tasks:
Image Segmentation
Size:
n<1K
Tags:
medical
ct
lung
non-small-cell-lung-cancer
tumor-segmentation
radiogenomics
License:
Dataset card Files
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Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    HfHubHTTPError
Message:      Server error '504 Gateway Time-out' for url 'https://huggingface.co/api/datasets/MedOtter/NSCLC-Radiogenomics/tree/cf7cb183ff59b3f7f7d634d9232ce34c1b7e3bf4?recursive=true&expand=false'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/504
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 268, in get_dataset_config_info
                  builder = load_dataset_builder(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1325, in load_dataset_builder
                  dataset_module = dataset_module_factory(
                      path,
                  ...<5 lines>...
                      cache_dir=cache_dir,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1217, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1192, in dataset_module_factory
                  ).get_module()
                    ~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 648, in get_module
                  patterns = get_data_patterns(base_path, download_config=self.download_config)
                File "/usr/local/lib/python3.14/site-packages/datasets/data_files.py", line 493, in get_data_patterns
                  return _get_data_files_patterns(resolver)
                File "/usr/local/lib/python3.14/site-packages/datasets/data_files.py", line 290, in _get_data_files_patterns
                  data_files = pattern_resolver(pattern)
                File "/usr/local/lib/python3.14/site-packages/datasets/data_files.py", line 372, in resolve_pattern
                  for filepath, info in fs.glob(fs_pattern, detail=True, **glob_kwargs).items():
                                        ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_file_system.py", line 728, in glob
                  return super().glob(path, maxdepth=maxdepth, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/fsspec/spec.py", line 604, in glob
                  allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_file_system.py", line 767, in find
                  out = self._ls_tree(path, recursive=True, refresh=refresh, maxdepth=maxdepth, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_file_system.py", line 586, in _ls_tree
                  self._ls_tree(
                  ~~~~~~~~~~~~~^
                      common_path,
                      ^^^^^^^^^^^^
                  ...<4 lines>...
                      maxdepth=maxdepth,
                      ^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_file_system.py", line 612, in _ls_tree
                  for path_info in tree:
                                   ^^^^
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_api.py", line 3922, in list_repo_tree
                  for path_info in paginate(path=tree_url, headers=headers, params={"recursive": recursive, "expand": expand}):
                                   ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/utils/_pagination.py", line 36, in paginate
                  hf_raise_for_status(r)
                  ~~~~~~~~~~~~~~~~~~~^^^
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/utils/_http.py", line 877, in hf_raise_for_status
                  raise _format(HfHubHTTPError, str(e), response) from e
              huggingface_hub.errors.HfHubHTTPError: Server error '504 Gateway Time-out' for url 'https://huggingface.co/api/datasets/MedOtter/NSCLC-Radiogenomics/tree/cf7cb183ff59b3f7f7d634d9232ce34c1b7e3bf4?recursive=true&expand=false'
              For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/504

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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

Citation 

@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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