DICOM converted Slide Microscopy images for the HTAN-VANDERBILT collection

<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=htan_vanderbilt">HTAN-VANDERBILT</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> <p>The Human Tumor Atlas Network (HTAN) [2], part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types.&nbsp;</p> <p>Colorectal cancer (CRC) is among the top three most prevalent cancers in global incidence and mortality. Most of these cancers develop from pre-cancerous adenomas. There is an unmet need to develop new preventive strategies and risk stratification models to decrease incidence, improve early detection, and prevent deaths from CRC.</p> <p>We believe that the ability to provide the most effective precision diagnostics and preventive strategies can only be achieved with single-cell analysis. As such, we will map spatial relationships across the spectrum of normal colon, early polyps, and late adenomas, including their unique stromal and microbial microenvironments to identify unique molecular phenotypes.</p> <p>Our goal will be accomplished through prospective, standardized collection and analysis of colorectal tissue, associated biospecimens, and related clinical and epidemiological data from participants undergoing colonoscopy or surgical resection. The biospecimens from these participants will be used for single-cell RNA sequencing, whole exome sequencing, multiplex immunofluorescence, species-specific bacterial fluorescence in situ hybridization, and other approaches. Finally, the information from these approaches will be integrated to develop a single-cell pre-cancer atlas with defined molecular phenotypes for dissemination to the broader scientific community.</p> <p>Please see the <a href="https://humantumoratlas.org/hta11/"> HTAN-Vanderbilt <em> </em> </a> information page to learn more about the images and to obtain any supporting metadata for this collection.</p> <p>Citation guidelines can be found on the <a href="https://docs.google.com/document/d/1seSp9yo8mqMK5sFXd4LEf_t_4SEofZi-1p-M75R1lhk/edit#heading=h.ybiv33wzts40"> HTAN Publication Policy <em> </em> </a> information page.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>htan_vanderbilt-idc_v15-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>htan_vanderbilt-idc_v15-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>htan_vanderbilt-idc_v15-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Collection of the images that were converted by IDC was supported through the Human Tumor Atlas Network, grants 1U2CCA233291-01 "Integrative Single-Cell Atlas of Host and Microenvironment in Colorectal Neoplastic Transformation" and 1U24CA233243-01 "Human Tumor Atlas Network: Data Coordinating Center" from National Cancer Institute.</p> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. &amp; Kikinis, R. <em>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</em>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p> <p>[2] Rozenblatt-Rosen, O., Regev, A., Oberdoerffer, P., Nawy, T., Hupalowska, A., Rood, J. E., Ashenberg, O., Cerami, E., Coffey, R. J., Demir, E., Ding, L., Esplin, E. D., Ford, J. M., Goecks, J., Ghosh, S., Gray, J. W., Guinney, J., Hanlon, S. E., Hughes, S. K., Hwang, E. S., Iacobuzio-Donahue, C. A., Jan&eacute;-Valbuena, J., Johnson, B. E., Lau, K. S., Lively, T., Mazzilli, S. A., Pe&rsquo;er, D., Santagata, S., Shalek, A. K., Schapiro, D., Snyder, M. P., Sorger, P. K., Spira, A. E., Srivastava, S., Tan, K., West, R. B., Williams, E. H. &amp; Human Tumor Atlas Network. <em>The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution</em>. Cell<strong> 181,</strong> 236&ndash;249 (2020). <a href="http://dx.doi.org/10.1016/j.cell.2020.03.053">http://dx.doi.org/10.1016/j.cell.2020.03.053</a></p>