DICOM converted whole slide hematoxylin and eosin images of rhabdomyosarcoma from Children's Oncology Group trials
Bibliographic record
Abstract
Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma, which primarily occurs in children and young adults. This dataset contains manifests referring to the hematoxylin and eosin (H&E) stained images in Digital Imaging and Communications in Medicine (DICOM) format available from National Cancer Institute Imaging Data Commons (IDC) [1] (also see IDC Portal at https://imaging.datacommons.cancer.gov) as of data release v16. The original images in vendor-specific format were collected on IRB-approved clinical trials or tissue banking studies from Children’s Oncology Group (COG) patients enrolled on ARST0331, ARST0431, D9602, D9803, and D9902 trials, as described in [2]. Those images, augmented with the metadata describing their content, were provided to the IDC team for the purposes of archival, and were converted into DICOM Whole Slide Microscopy (SM) representation [3], [4] using custom open source scripts and tools available and described here [5]. The resulting converted images were released in IDC in the RMS-Mutation-Prediction collection with the data release v16. To conveniently explore the data available for this dataset, please use this dashboard: https://lookerstudio.google.com/reporting/7f267400-8774-42e1-b5d1-ca11863c52a9. Notebooks demonstrating how to use this data are available here: https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/collections_demos/rms_mutation_prediction. Clinical data accompanying the images is available via SQL interface in IDC BigQuery tables, see details on accessing IDC clinical data in the respective tutorial (https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/clinical_data_intro.ipynb). The images referred to by the accompanying manifests can be explored and visualized using IDC Portal here: https://portal.imaging.datacommons.cancer.gov/explore/. Direct link to open the collection is https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=rms_mutation_prediction. The GCP and AWS manifests provided with this dataset record can be used to download the corresponding files from the IDC Google Cloud Storage (GCS) or Amazon S3 (AWS) buckets free of charge following the instructions available in IDC documentation here: https://learn.canceridc.dev/data/downloading-data. Specifically, you will need to install the s5cmd command line tool on your computer (see instructions at https://github.com/peak/s5cmd#installation), and follow the manifest-specific download instructions accompanying the file list below. If you use the files referenced in the attached manifests, we ask you to please cite this dataset, as well as the publication describing the original dataset [2] and the publication acknowledging IDC [1]. Specific files included in the record are: rms_mutation_prediction_gcs.s5cmd: GCS-based manifest (to download the files described in the manifest, execute this command: s5cmd --no-sign-request --endpoint-url https://storage.googleapis.com run rms_mutation_prediction_gcs.s5cmd) rms_mutation_prediction_aws.s5cmd: AWS-based manifest (to download the files described in the manifest, execute this command: s5cmd --no-sign-request --endpoint-url https://s3.amazonaws.com run rms_mutation_prediction_aws.s5cmd) rms_mutation_prediction_dcf.csv: Gen3-based manifest (see details in https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids). References [1] A. Fedorov et al., "NCI Imaging Data Commons," Cancer Res., vol. 81, no. 16, pp. 4188–4193, Aug. 2021, doi: 10.1158/0008-5472.CAN-21-0950. [2] D. Milewski et al., "Predicting molecular subtype and survival of rhabdomyosarcoma patients using deep learning of H&E images: A report from the Children's Oncology Group," Clin. Cancer Res., vol. 29, no. 2, pp. 364–378, Jan. 2023, doi: 10.1158/1078-0432.CCR-22-1663. [3] National Electrical Manufacturers Association (NEMA), "DICOM PS3.3 - Information Object Definitions: A.32.8 VL Whole Slide Microscopy Image IOD." Accessed: Aug. 11, 2023. [Online]. Available: https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8 [4] M. D. Herrmann et al., "Implementing the DICOM standard for digital pathology," J. Pathol. Inform., vol. 9, no. 1, p. 37, Jan. 2018, doi: 10.4103/jpi.jpi_42_18. [5] D. Clunie, A. Fedorov, and M. D. Herrmann, ImagingDataCommons/idc-wsi-conversion: Initial release. Zenodo, 2023. doi: 10.5281/zenodo.8240154.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.047 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".