Datasets:

andreasenz
/

TRUTH-LUNG

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 4 new columns ({'unit', 'result', 'marker', 'value'}) and 16 missing columns ({'EGFR_result', 'TTF1_result', 'ROS1_result', 'label', 'BRAF_result', 'P63_result', 'P40_result', 'valid', 'CK7_result', 'NAPSINA_result', 'ALK_result', 'HER2_result', 'CK5_6_result', 'PDL1_TPS_unit', 'PDL1_TPS', 'KRAS_result'}).

This happened while the csv dataset builder was generating data using

hf://datasets/andreasenz/TRUTH-LUNG/histo_markers_long.csv (at revision aefd6cf7b1d833faf2e6f4266943a6f401d0f67b), ['hf://datasets/andreasenz/TRUTH-LUNG@aefd6cf7b1d833faf2e6f4266943a6f401d0f67b/histo_dataset_wide.csv', 'hf://datasets/andreasenz/TRUTH-LUNG@aefd6cf7b1d833faf2e6f4266943a6f401d0f67b/histo_markers_long.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              id: int64
              marker: string
              result: string
              value: double
              unit: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 805
              to
              {'id': Value('int64'), 'valid': Value('bool'), 'label': Value('string'), 'PDL1_TPS': Value('float64'), 'PDL1_TPS_unit': Value('string'), 'TTF1_result': Value('string'), 'CK7_result': Value('string'), 'CK5_6_result': Value('string'), 'P40_result': Value('string'), 'P63_result': Value('float64'), 'NAPSINA_result': Value('string'), 'ALK_result': Value('float64'), 'ROS1_result': Value('float64'), 'EGFR_result': Value('float64'), 'HER2_result': Value('string'), 'KRAS_result': Value('float64'), 'BRAF_result': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1342, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1889, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 4 new columns ({'unit', 'result', 'marker', 'value'}) and 16 missing columns ({'EGFR_result', 'TTF1_result', 'ROS1_result', 'label', 'BRAF_result', 'P63_result', 'P40_result', 'valid', 'CK7_result', 'NAPSINA_result', 'ALK_result', 'HER2_result', 'CK5_6_result', 'PDL1_TPS_unit', 'PDL1_TPS', 'KRAS_result'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/andreasenz/TRUTH-LUNG/histo_markers_long.csv (at revision aefd6cf7b1d833faf2e6f4266943a6f401d0f67b), ['hf://datasets/andreasenz/TRUTH-LUNG@aefd6cf7b1d833faf2e6f4266943a6f401d0f67b/histo_dataset_wide.csv', 'hf://datasets/andreasenz/TRUTH-LUNG@aefd6cf7b1d833faf2e6f4266943a6f401d0f67b/histo_markers_long.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

id int64	valid bool	label string	PDL1_TPS float64	PDL1_TPS_unit string	TTF1_result string	CK7_result string	CK5_6_result string	P40_result string	P63_result null	NAPSINA_result null	ALK_result null	ROS1_result null	EGFR_result null	HER2_result null	KRAS_result null	BRAF_result null
3,332,610	true	adenocarcinoma	50	%	positive	positive	negative	negative	null	null	null	null	null	null	null	null
3,332,844	true	benign_or_inflammatory	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,333,767	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,338,147	true	benign_or_inflammatory	null	null	positive	positive	null	null	null	null	null	null	null	null	null	null
3,338,169	true	squamous_carcinoma	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,338,301	true	squamous_carcinoma	null	null	positive	positive	positive	positive	null	null	null	null	null	null	null	null
3,338,347	true	adenocarcinoma	50	%	positive	positive	positive	negative	null	null	null	null	null	null	null	null
3,339,821	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,339,822	true	adenocarcinoma	50	%	positive	positive	positive	negative	null	null	null	null	null	null	null	null
3,341,362	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,343,137	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,344,707	true	null	null	null	negative	null	negative	negative	null	null	null	null	null	null	null	null
3,345,400	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,350,015	false	benign_or_inflammatory	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,350,385	true	benign_or_inflammatory	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,350,963	true	squamous_carcinoma	1	%	negative	null	positive	positive	null	null	null	null	null	null	null	null
3,352,180	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,355,627	false	benign_or_inflammatory	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,356,038	true	adenocarcinoma	50	%	positive	positive	null	negative	null	null	null	null	null	null	null	null
3,357,554	true	adenocarcinoma	50	%	null	null	null	null	null	null	null	null	null	null	null	null
3,357,968	true	adenocarcinoma	1	%	positive	positive	null	null	null	null	null	null	null	null	null	null
3,359,577	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,359,678	true	null	null	null	negative	positive	positive	negative	null	null	null	null	null	null	null	null
3,362,859	true	squamous_carcinoma	null	null	negative	positive	positive	positive	null	null	null	null	null	null	null	null
3,363,068	true	adenocarcinoma	null	null	positive	null	null	positive	null	null	null	null	null	null	null	null
3,365,532	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,366,544	true	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,368,239	true	carcinoid_tumor	null	null	positive	null	null	null	null	null	null	null	null	null	null	null
3,369,153	true	null	null	null	null	negative	null	null	null	null	null	null	null	null	null	null
3,369,976	true	adenocarcinoma	null	null	positive	positive	positive	null	null	null	null	null	null	null	null	null
3,372,968	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,373,760	true	adenocarcinoma	50	%	positive	positive	null	null	null	null	null	null	null	null	null	null
3,373,767	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,374,265	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,377,595	true	adenocarcinoma	null	null	negative	negative	null	null	null	null	null	null	null	null	null	null
3,381,105	true	benign_or_inflammatory	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,381,215	true	null	null	null	null	null	negative	negative	null	null	null	null	null	null	null	null
3,383,921	true	squamous_carcinoma	null	null	negative	null	positive	positive	null	null	null	null	null	null	null	null
3,388,132	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,389,412	true	null	null	null	negative	positive	null	negative	null	null	null	null	null	null	null	null
3,389,592	true	squamous_carcinoma	null	null	negative	null	positive	positive	null	null	null	null	null	null	null	null
3,389,695	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,391,619	true	null	null	null	positive	positive	null	null	null	null	null	null	null	null	null	null
3,392,211	true	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,392,678	true	adenocarcinoma	null	null	negative	negative	null	negative	null	null	null	null	null	null	null	null
3,392,883	true	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,397,524	true	squamous_carcinoma	null	null	null	positive	positive	null	null	null	null	null	null	null	null	null
3,403,398	true	adenocarcinoma	1	%	positive	null	null	negative	null	null	null	null	null	null	null	null
3,409,330	true	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,409,849	true	null	null	null	negative	positive	positive	negative	null	null	null	null	null	null	null	null
3,414,061	true	null	null	null	positive	positive	null	null	null	null	null	null	null	null	null	null
3,415,502	true	squamous_carcinoma	50	%	negative	null	positive	positive	null	null	null	null	null	null	null	null
3,415,604	true	adenocarcinoma	null	null	negative	positive	null	null	null	null	null	null	null	null	null	null
3,416,527	true	adenocarcinoma	50	%	positive	positive	positive	negative	null	null	null	null	null	null	null	null
3,418,663	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,419,159	false	benign_or_inflammatory	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,420,749	true	benign_or_inflammatory	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,420,788	true	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,422,563	true	squamous_carcinoma	null	null	null	null	positive	positive	null	null	null	null	null	null	null	null
3,422,566	true	null	null	null	positive	positive	negative	negative	null	null	null	null	null	null	null	null
3,424,127	true	squamous_carcinoma	1	%	null	null	positive	positive	null	null	null	null	null	null	null	null
3,424,388	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,429,369	true	null	1	%	negative	positive	positive	negative	null	null	null	null	null	null	null	null
3,430,472	true	benign_or_inflammatory	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,435,399	true	benign_or_inflammatory	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,437,447	true	benign_or_inflammatory	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,438,237	true	benign_or_inflammatory	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,438,744	true	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,444,716	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,444,963	true	adenocarcinoma	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,449,262	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,452,952	true	squamous_carcinoma	null	null	null	null	positive	positive	null	null	null	null	null	null	null	null
3,453,421	true	metastasis	null	null	negative	negative	positive	null	null	null	null	null	null	null	null	null
3,466,708	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,466,709	true	adenocarcinoma	null	null	positive	positive	null	null	null	null	null	null	null	null	null	null
3,470,803	true	adenocarcinoma	null	null	positive	positive	negative	negative	null	null	null	null	null	null	null	null
3,471,977	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,472,498	true	benign_or_inflammatory	null	null	positive	positive	null	null	null	null	null	null	null	null	null	null
3,478,237	true	null	null	null	null	positive	null	null	null	null	null	null	null	null	null	null
3,486,353	true	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,487,972	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,493,513	true	benign_or_inflammatory	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,497,948	true	adenocarcinoma	null	null	positive	positive	null	positive	null	null	null	null	null	null	null	null
3,501,614	true	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,503,763	true	null	null	null	null	null	positive	null	null	null	null	null	null	null	null	null
3,504,703	true	adenocarcinoma	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,505,040	true	benign_or_inflammatory	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,510,901	true	adenocarcinoma	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,518,420	true	null	null	null	null	positive	null	null	null	null	null	null	null	null	null	null
3,518,426	true	adenocarcinoma	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,519,511	false	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,522,989	true	adenocarcinoma	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,525,774	true	adenocarcinoma	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,526,249	true	squamous_carcinoma	50	%	null	null	null	null	null	null	null	null	null	null	null	null
3,528,207	true	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,528,724	true	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,531,808	true	null	1	%	null	null	null	null	null	null	null	null	null	null	null	null
3,534,966	true	adenocarcinoma	50	%	null	null	null	null	null	null	null	null	null	null	null	null
3,536,529	true	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3,540,199	true	adenocarcinoma	50	%	positive	positive	positive	negative	null	null	null	null	null	null	null	null

End of preview.

CT Lung Nodule Dataset (Case–Control)

Overview

This dataset contains chest CT scans organized in a case–control setting for lung nodule analysis.

Case group: CT scans with lung nodules and corresponding segmentation masks.
Control group: CT scans without lung nodules (no segmentations available).

The dataset is intended for research purposes, including but not limited to:

lung nodule detection and classification,
segmentation,
case–control studies,
radiomics and machine learning experiments.

All data have been anonymized.

Dataset Structure

dataset_root/
│
├── case/
│ ├── <study_id_1>/
│ │ ├── volume.nii.gz
│ │ └── segmentation.nii.gz
│ ├── <study_id_2>/
│ │ ├── volume.nii.gz
│ │ └── segmentation.nii.gz
│ └── ...
│
├── control/
│ ├── <study_id_3>/
│ │ └── volume.nii.gz
│ ├── <study_id_4>/
│ │ └── volume.nii.gz
│ └── ...
│
├── labels.csv
├── pathology_features_1.csv
├── pathology_features_2.csv
└── README.md

Case and Control Folders

`case/`

Contains CT scans with lung nodules.

Each subfolder corresponds to a single imaging study, identified by a unique study_id.

Contents of each case/<study_id>/ folder:

volume.nii.gz
Preprocessed chest CT volume (NIfTI format).
segmentation.nii.gz
Binary segmentation mask of lung nodules, aligned with the CT volume.

`control/`

Contains CT scans without lung nodules.

Each subfolder corresponds to a single imaging study, identified by a unique study_id.

Contents of each control/<study_id>/ folder:

volume.nii.gz
Preprocessed chest CT volume (NIfTI format).

⚠️ Control cases do not include segmentation masks, as no nodules are present.

Study Identifier

Each subfolder name (<study_id>) uniquely identifies one imaging study.
The same study_id is consistently used across:
- folder names,
- labels.csv,
- pathology-related CSV files.

Labels File

`labels.csv`

This file provides the malignancy label for each study.

Format:

study_id	malignancy
XXXXXXX	1
YYYYYYY	0

Label definition:

1 → malignant nodule
0 → benign nodule

Control studies are not included in this file while the label is always malignancy = 0.

Pathology Data

`histo_dataset_wide.csv`

`histo_markers_long.csv`

These files contain anatomical pathology (histopathological) information associated with the studies in the case group.

Each row corresponds to a study_id.
Columns represent pathology-derived variables (e.g., histological findings, grading, molecular or morphological features).
The exact meaning of each column is described in the corresponding data dictionary or publication.

⚠️ Important note:
Control studies do not have pathology data, as no biopsy was performed due to the absence of lung nodules.

Data Modalities Summary

Group	CT Volume	Segmentation	Pathology	Malignancy Label
Case	✔️	✔️	✔️	✔️
Control	✔️	❌	❌	✔️ (benign)

File Formats

CT volumes and segmentations:
- NIfTI (.nii.gz)
Metadata and labels:
- CSV (.csv)

All volumes are stored in a consistent orientation and can be directly loaded using standard medical imaging libraries (e.g., SimpleITK, nibabel).

Notes and Limitations

Control cases did not undergo biopsy, as no lung nodules were detected.
Pathology data are therefore available only for case studies.
The dataset is intended for research use only and not for clinical decision-making.

Citation

If you use this dataset in your research, please cite the associated publication (to be added).

Contact

For questions, clarifications, or additional information, please contact the dataset authors.

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0).

If you use this dataset, please cite:

[Paper / Author / Institution]

License details: https://creativecommons.org/licenses/by/4.0/

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