Research Dataset Catalog and ShareScore Ratings | ScienceDex

zenodo48/100

DICOM converted whole slide hematoxylin and eosin images of rhabdomyosarcoma from Children's Oncology Group trials

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 <a href="https://imaging.datacommons.cancer.gov">https://imaging.datacommons.cancer.gov</a>) 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: <a href="https://lookerstudio.google.com/reporting/7f267400-8774-42e1-b5d1-ca11863c52a9">https://lookerstudio.google.com/reporting/7f267400-8774-42e1-b5d1-ca11863c52a9</a>. Notebooks demonstrating how to use this data are available here: <a href="https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/collections_demos/rms_mutation_prediction">https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/collections_demos/rms_mutation_prediction</a>. 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 (<a href="https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/clinical_data_intro.ipynb">https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/clinical_data_intro.ipynb</a>). The images referred to by the accompanying manifests can be explored and visualized using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/">https://portal.imaging.datacommons.cancer.gov/explore/</a>. Direct link to open the collection is <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=rms_mutation_prediction">https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=rms_mutation_prediction</a>. 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: <a href="https://learn.canceridc.dev/data/downloading-data">https://learn.canceridc.dev/data/downloading-data</a>. Specifically, you will need to install the s5cmd command line tool on your computer (see instructions at <a href="https://github.com/peak/s5cmd#installation">https://github.com/peak/s5cmd#installation</a>), 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: <ol> <li> <code>rms_mutation_prediction_gcs.s5cmd</code>: GCS-based manifest (to download the files described in the manifest, execute this command: <code>s5cmd --no-sign-request --endpoint-url https://storage.googleapis.com run rms_mutation_prediction_gcs.s5cmd</code>) </li> <li> <code>rms_mutation_prediction_aws.s5cmd</code>: AWS-based manifest (to download the files described in the manifest, execute this command: <code>s5cmd --no-sign-request --endpoint-url https://s3.amazonaws.com run rms_mutation_prediction_aws.s5cmd</code>) </li> <li> <code>rms_mutation_prediction_dcf.csv</code>: Gen3-based manifest (see details in <a href="https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>). </li> </ol> References [1] A. Fedorov et al., "NCI Imaging Data Commons," Cancer Res., vol. 81, no. 16, pp. 4188–4193, Aug. 2021, doi: <a href="https://dx.doi.org/10.1158/0008-5472.CAN-21-0950">10.1158/0008-5472.CAN-21-0950</a>.  [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: <a href="https://dx.doi.org/10.1158/1078-0432.CCR-22-1663">10.1158/1078-0432.CCR-22-1663</a>. [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: <a href="https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8">https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8</a> [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: <a href="https://dx.doi.org/10.4103/jpi.jpi_42_18">10.4103/jpi.jpi_42_18</a>.  [5] D. Clunie, A. Fedorov, and M. D. Herrmann, ImagingDataCommons/idc-wsi-conversion: Initial release. Zenodo, 2023. doi: <a href="https://dx.doi.org/10.5281/zenodo.8240154">10.5281/zenodo.8240154</a>.

opencc-by-4.0Aug 2023View details →

zenodo44/100

A Single-Cell Tumor Immune Atlas for Precision Oncology

Publication version of the Single-Cell Tumor Immune Atlas This upload contains: <ul> <li>TICAtlas.rds: an rds file containing a Seurat object with the whole Atlas</li> <li>TICAtlas.h5ad: an h5ad file with the whole Atlas</li> <li>TICAtlas_downsampled.rds: an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li>TICAtlas_downsampled.h5ad: an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li>TICAtlas_metadata.csv: a comma-separated text file with the metadata for each of the cells</li> </ul> All the files contain the following patient/sample metadata variables: <ul> <li>patient: assigned patient identifiers</li> <li>nCountRNA and nFeatureRNA: number of UMIs and genes per cell</li> <li>percent.mt: percentage of mitochondrial genes</li> <li>gender: the patient's gender (male/female/unknown)</li> <li>source: dataset of origin</li> <li>subtype: cancer type (abbreviations as indicated in the preprint)</li> <li>kmeans_cluster: patients clusters, NA if filtered out before clustering</li> <li>lv1 and lv2: annotated cell type for each of the cells, two level annotation (lv2 has more cell types)</li> </ul> <pre> </pre> If you have any issues with the metadata (i.e. unexpected factors, NA values...) you can use the TICAtlas_metadata.csv file. For more information, <a href="https://genome.cshlp.org/content/early/2021/09/21/gr.273300.120.">read our paper</a>, <a href="https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/Tumor-Immune-Cell-Atlas">check our GitHub</a> and our <a href="https://singlecellgenomics-cnag-crg.shinyapps.io/TICA/">ShinyApp</a>. h5ad files can be read with Python using <a href="https://scanpy.readthedocs.io/en/stable/">Scanpy</a>, rds files can be read in R using <a href="https://satijalab.org/seurat/">Seurat</a>. For format conversion between AnnData and Seurat we recommend <a href="https://mojaveazure.github.io/seurat-disk/">SeuratDisk</a>. For other single-cell data formats you can use <a href="https://github.com/cellgeni/sceasy">sceasy</a>.

opencc-by-4.0Sep 2020View details →

dryad40/100

Randomized controlled oncology trials with tumor stage inclusion criteria

Background: Extracting inclusion and exclusion criteria in a structured, automated fashion remains a challenge to developing better search functionalities or automating systematic reviews of randomized controlled trials in oncology. The question "Did this trial enroll patients with localized disease, metastatic disease, or both?" could be used to narrow down the number of potentially relevant trials when conducting a search. Dataset collection: 600 randomized controlled trials from high-impact medical journals were classified depending on whether they allowed for the inclusion of patients with localized and/or metastatic disease. The dataset was randomly split into a training/validation and a test set of 500 and 100 trials respectively. However, the sets could be merged to allow for different splits. Data properties: Each trial is a row in the csv file. For each trial there is a doi, a publication date, a title, an abstract, the abstract sections (introduction, methods, results, conclusion), several tags associated with the annotation process (text, _input_hash, _task_hash, options, _view_id, config, accept, answer, _timestamp, _annotator_id,_session_id), and the assigned labels (answer).