Datasets:

waticlems
/

tcga-prad-bcr

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Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 246, in _split_generators
                  raise ValueError(
                      "`file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files"
                  )
              ValueError: `file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files
              
              The above exception was the direct cause of the following exception:
              
              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 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

TCGA-PRAD Biochemical Recurrence Dataset

This dataset was curated as part of a research project aimed at predicting biochemical recurrence (BCR) in prostate cancer directly from H&E-stained prostatectomy specimens.
Based on the TCGA Prostate Adenocarcinoma (TCGA-PRAD) cohort, we provides curated labels for biochemical recurrence along with packed diagnostic whole-slide images and corresponding tissue masks.

📂 Repository Structure

The dataset is organized into the following folders and files:

whole-slide-images/

Packed WSIs for the 298 TCGA-PRAD patients included in our study in .tif format

tissue-masks/

Corresponding binary tissue segmentation masks, also in .tif format (0 = background, 1 = tissue)

biotab-xml/

Original case-specific biotab XML files for 403 TCGA-PRAD patients with at least one diagnostic slide.
Used for extracting BCR status and metadata.

metadata.csv

metadata prased from biotab XML files for all the 403 TCGA-PRAD patients with diagnostic slides.

bcr.csv

Curated biochemical recurrence labels for the 298 TCGA-PRAD patients included in our study
- case_id: TCGA case ID
- event: 1 if biochemical recurrence occurred, 0 otherwise
- follow_up_y: Time to event (if event=1) or time to last follow-up (if event=0), in years

🧬 Label Curation Process

Biochemical recurrence labels were curated from the biotab XML files, based on the following logic:

Event = 1 (Biochemical Recurrence):
- biochemical_recurrence_indicator = YES, using days_to_biochemical_recurrence_first as time to event.
- OR new_tumor_event_type = "Biochemical evidence of disease" with available new_tumor_event_dx_days_to.
Event = 0 (No Recurrence):
- biochemical_recurrence_indicator = NO and no tumor event indicating recurrence.
- Time to last follow-up taken from last_contact_days_to.

Patients with incomplete or contradictory information were excluded (105 patients).

🧾 Citation

If you use this dataset in your research, please cite:

@article{grisi2026bcr,
      title={Deep Learning From Routine Histology Improves Risk Stratification for Biochemical Recurrence in Prostate Cancer}, 
      author={Clément Grisi and Khrystyna Faryna and Nefise Uysal and Vittorio Agosti and Enrico Munari and Solène-Florence Kammerer-Jacquet and Paulo Guilherme de Oliveira Salles and Yuri Tolkach and Reinhard Büttner and Sofiya Semko and Maksym Pikul and Axel Heidenreich and Jeroen van der Laak and Geert Litjens},
      year={2026},
      eprint={2603.14187},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.14187},
}

🔒 License

This dataset is derived from TCGA, which is distributed under the TCGA Data Use Certification.

Redistribution of image data is permitted for academic and non-commercial research purposes only.

tags: - medical - histology - tcga size_categories: - n<1K

Downloads last month: 588

Total file size:

317 GB

Paper for waticlems/tcga-prad-bcr

Deep Learning From Routine Histology Improves Risk Stratification for Biochemical Recurrence in Prostate Cancer

Paper • 2603.14187 • Published Mar 15