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OHIF public demo data sets

The OHIF Viewer's public demo page, available at https://viewer.ohif.org/, uses publicly anonymized demo datasets. These datasets were mostly obtained from the NIH NCI Imaging Data Commons and NIH NCI TCIA. Before listing the datasets, we would like to extend a special thank you to all groups who have made their datasets publicly available. Without them, we would not have been able to create this demo page.

Please find below the list of datasets used on the demo page, along with their respective citations.

Platforms

NIH NCI IDC

  • Fedorov, A., Longabaugh, W.J., Pot, D., Clunie, D.A., Pieper, S., Aerts, H.J., Homeyer, A., Lewis, R., Akbarzadeh, A., Bontempi, D. and Clifford, W., 2021. NCI imaging data commons. Cancer research, 81(16), p.4188.

NIH NCI TCIA

  • Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7

Datasets

Below you can find the StudyInstanceUID of the studies that are used in the demo page along with their citations.

1.3.6.1.4.1.14519.5.2.1.267424821384663813780850856506829388886

Segmentation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Open Annotated Dataset and Baseline Algorithm (Vestibular-Schwannoma-SEG)

  • Shapey, J., Kujawa, A., Dorent, R., Wang, G., Bisdas, S., Dimitriadis, A., Grishchuck, D., Paddick, I., Kitchen, N., Bradford, R., Saeed, S., Ourselin, S., & Vercauteren, T. (2021). Segmentation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Open Annotated Dataset and Baseline Algorithm [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.9YTJ-5Q73

  • Shapey, J., Kujawa, A., Dorent, R., Wang, G., Dimitriadis, A., Grishchuk, D., Paddick, I., Kitchen, N., Bradford, R., Saeed, S. R., Bisdas, S., Ourselin, S., & Vercauteren, T. (2021). Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm. In Scientific Data (Vol. 8, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41597-021-01064-w

1.3.6.1.4.1.14519.5.2.1.7009.2403.334240657131972136850343327463

1.3.6.1.4.1.14519.5.2.1.7009.2403.871108593056125491804754960339

ACRIN-NSCLC-FDG-PET (ACRIN 6668)

  • Kinahan, P., Muzi, M., Bialecki, B., Herman, B., & Coombs, L. (2019). Data from the ACRIN 6668 Trial NSCLC-FDG-PET (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.30ilqfcl

  • Machtay, M., Duan, F., Siegel, B. A., Snyder, B. S., Gorelick, J. J., Reddin, J. S., Munden, R., Johnson, D. W., Wilf, L. H., DeNittis, A., Sherwin, N., Cho, K. H., Kim, S., Videtic, G., Neumann, D. R., Komaki, R., Macapinlac, H., Bradley, J. D., & Alavi, A. (2013). Prediction of Survival by [18F]Fluorodeoxyglucose Positron Emission Tomography in Patients With Locally Advanced Non–Small-Cell Lung Cancer Undergoing Definitive Chemoradiation Therapy: Results of the ACRIN 6668/RTOG 0235 Trial. In Journal of Clinical Oncology (Vol. 31, Issue 30, pp. 3823–3830). American Society of Clinical Oncology (ASCO). https://doi.org/10.1200/jco.2012.47.5947

2.25.103659964951665749659160840573802789777

The Cancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM)

  • Scarpace, L., Mikkelsen, T., Cha, S., Rao, S., Tekchandani, S., Gutman, D., Saltz, J. H., Erickson, B. J., Pedano, N., Flanders, A. E., Barnholtz-Sloan, J., Ostrom, Q., Barboriak, D., & Pierce, L. J. (2016). The Cancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM) (Version 4) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.RNYFUYE9

1.3.6.1.4.1.14519.5.2.1.256467663913010332776401703474716742458

Abdominal or pelvic enhanced CT images within 10 days before surgery of 230 patients with stage II colorectal cancer (StageII-Colorectal-CT)

  • Tong T., Li M. (2022) Abdominal or pelvic enhanced CT images within 10 days before surgery of 230 patients with stage II colorectal cancer (StageII-Colorectal-CT) [Dataset]. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/p5k5-tg43

  • Li, M., Gong, J., Bao, Y., Huang, D., Peng, J., & Tong, T. (2022). Special issue “The advance of solid tumor research in China”: Prognosis prediction for stage II colorectal cancer by fusing computed tomography radiomics and deep‐learning features of primary lesions and peripheral lymph nodes. In International Journal of Cancer. Wiley. https://doi.org/10.1002/ijc.34053

1.3.6.1.4.1.14519.5.2.1.3023.4024.215308722288168917637555384485

The Cancer Genome Atlas Sarcoma Collection (TCGA-SARC)

1.3.6.1.4.1.14519.5.2.1.4792.2001.105216574054253895819671475627

BREAST-DIAGNOSIS

1.3.6.1.4.1.14519.5.2.1.1706.8374.643249677828306008300337414785

Multimodality annotated HCC cases with and without advanced imaging segmentation (HCC-TACE-Seg)

  • Moawad, A. W., Fuentes, D., Morshid, A., Khalaf, A. M., Elmohr, M. M., Abusaif, A., Hazle, J. D., Kaseb, A. O., Hassan, M., Mahvash, A., Szklaruk, J., Qayyom, A., & Elsayes, K. (2021). Multimodality annotated HCC cases with and without advanced imaging segmentation [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.5FNA-0924

  • Morshid, A., Elsayes, K. M., Khalaf, A. M., Elmohr, M. M., Yu, J., Kaseb, A. O., Hassan, M., Mahvash, A., Wang, Z., Hazle, J. D., & Fuentes, D. (2019). A Machine Learning Model to Predict Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization. Radiology: Artificial Intelligence, 1(5), e180021. https://doi.org/10.1148/ryai.2019180021

1.3.6.1.4.1.14519.5.2.1.1188.2803.137585363493444318569098508293

Ultrasound data of a variety of liver masses (B-mode-and-CEUS-Liver)

1.3.6.1.4.1.32722.99.99.62087908186665265759322018723889952421

NSCLC-Radiomics

  • Aerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., Lambin, P. (2019). Data From NSCLC-Radiomics (version 4) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI

  • Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., Lambin, P. (2014, June 3). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/ncomms5006 (link)

1.3.6.1.4.1.14519.5.2.1.3671.4754.298665348758363466150039312520

QIN-PROSTATE-Repeatability

  • Fedorov, A; Schwier, M; Clunie, D; Herz, C; Pieper, S; Kikinis, R; Tempany, C; Fennessy, F. (2018). Data From QIN-PROSTATE-Repeatability. The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2018.MR1CKGND

  • Fedorov A, Vangel MG, Tempany CM, Fennessy FM. Multiparametric Magnetic Resonance Imaging of the Prostate: Repeatability of Volume and Apparent Diffusion Coefficient Quantification. Investigative Radiology. 52, 538–546 (2017). DOI: 10.1097/RLI.0000000000000382

  • Fedorov, A., Schwier, M., Clunie, D., Herz, C., Pieper, S., Kikinis,R., Tempany, C. & Fennessy, F. An annotated test-retest collection of prostate multiparametric MRI. Scientific Data 5, 180281 (2018). DOI:

2.25.141277760791347900862109212450152067508

The Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma Collection (CPTAC-CCRCC)

  • National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). (2018). The Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma Collection (CPTAC-CCRCC) (Version 10) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.OBLAMN27

  • The CPTAC program requests that publications using data from this program include the following statement: “Data used in this publication were generated by the National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC).”

2.25.275741864483510678566144889372061815320

National Lung Screening Trial

  • National Lung Screening Trial Research Team. (2013). Data from the National Lung Screening Trial (NLST) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.HMQ8-J677

  • National Lung Screening Trial Research Team*; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD (2011). Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. New England Journal of Medicine, 365(5), 395–409. https://doi.org/10.1056/nejmoa1102873

1.3.6.1.4.1.14519.5.2.1.99.1071.26968527900428638961173806140069

Stony Brook University COVID-19 Positive Cases (COVID-19-NY-SBU)

  • Saltz, J., Saltz, M., Prasanna, P., Moffitt, R., Hajagos, J., Bremer, E., Balsamo, J., & Kurc, T. (2021). Stony Brook University COVID-19 Positive Cases [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.BBAG-2923

2.16.840.1.114362.1.11972228.22789312658.616067305.306.2

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