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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

https://data.kitware.com/

1.2.276.0.7230010.3.1.2.296485376.1.1665793212.499772

2.25.269859997690759739055099378767846712697

1.3.6.1.4.1.14519.5.2.1.5099.8010.217836670708542506360829799868

1.3.6.1.4.1.14519.5.2.1.4792.2001.232252967813565730694525674696

1.3.6.1.4.1.14519.5.2.1.4792.2001.105216574054253895819671475627

1.3.6.1.4.1.5962.99.1.1117.5035.1620319789811.1.2.1

1.3.6.1.4.1.5962.99.1.1123.9231.1620326176300.1.2.1

1.3.6.1.4.1.5962.99.1.1126.3483.1620329455972.1.2.1

https://github.com/ImagingInformatics/hackathon-images

2.16.124.113543.6004.101.103.20021117.162333.1

2.16.124.113543.6004.101.103.20021117.190619.1

2.16.124.113543.6004.101.103.20021117.123455.1

2.16.124.113543.6004.101.103.20021117.061159.1

https://www.aapm.org/

1.2.840.113619.2.30.1.1762295590.1623.978668949.886

1.2.276.0.7230010.3.1.2.447481088.1.1669202398.851612

Custom data SPECT, specifically I123-FP-CIT (DaTSCAN) SPECT, evaluates the dopaminergic system to diagnose Parkinson's disease, especially when tremor symptoms are unclear. It helps distinguish Parkinson's disease from treatment-related tremor.

1.3.6.1.4.1.9328.50.1.54652

https://www.cancerimagingarchive.net/collection/rider-pilot/

Lung Image Database Consortium (LIDC). (2023) RIDER Pilot [Data set]. The Cancer Imaging Archive (TCIA). https://doi.org/10.7937/m87f-mz83

1.3.6.1.4.1.14519.5.2.1.331759366792756327296606233801322964986

Mayr, N., Yuh, W. T. C., Bowen, S., Harkenrider, M., Knopp, M. V., Lee, E. Y.-P., Leung, E., Lo, S. S., Small Jr., W., & Wolfson, A. H. (2023). Cervical Cancer – Tumor Heterogeneity: Serial Functional and Molecular Imaging Across the Radiation Therapy Course in Advanced Cervical Cancer (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/ERZ5-QZ59

https://www.cancerimagingarchive.net/collection/cc-tumor-heterogeneity/

1.3.6.1.4.1.14519.5.2.1.297577087050970310787702792940607009472

Eslick, E. M., Kipritidis, J., Gradinscak, D., Stevens, M. J., Bailey, D. L., Harris, B., Booth, J. T., & Keall, P. J. (2022). CT Ventilation as a functional imaging modality for lung cancer radiotherapy (CT-vs-PET-Ventilation-Imaging) (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/3ppx-7s22

https://www.cancerimagingarchive.net/collection/ct-vs-pet-ventilation-imaging/

1.3.6.1.4.1.14519.5.2.1.2103.7010.634114621738943599785009586807

1.3.6.1.4.1.14519.5.2.1.2103.7010.135953723682765205394176991681

Huang, W., Tudorica, A., Chui, S., Kemmer, K., Naik, A., Troxell, M., Oh, K., Roy, N., Afzal, A., & Holtorf, M. (2014). Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge (QIN Breast DCE-MRI) (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/k9/tcia.2014.a2n1ixox

https://www.cancerimagingarchive.net/collection/qin-breast-dce-mri/

1.3.6.1.4.1.14519.5.2.1.1.24766180081901755714059656629507905556

Cancer Moonshot Biobank. (2023). Cancer Moonshoot Biobank – Acute Myeloid Leukemia (CMB-AML) (Version 4) [Dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/PCTE-6M66

https://www.cancerimagingarchive.net/collection/cmb-aml/

1.3.6.1.4.1.14519.5.2.1.3098.5025.285242291560760827564488897577

https://www.cancerimagingarchive.net/collection/anti-pd-1_lung/

Madhavi, P., Patel, S., & Tsao, A. S. (2019). Data from Anti-PD-1 Immunotherapy Lung [Data set]. The Cancer Imaging Archive. DOI: 10.7937/tcia.2019.zjjwb9ip

1.3.6.1.4.1.14519.5.2.1.1.84416332615988066829602832830236187384

https://www.cancerimagingarchive.net/collection/cmb-pca/

Cancer Moonshot Biobank. (2022). Cancer Moonshot Biobank – Prostate Cancer Collection (CMB-PCA) (Version 7) [Dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/25T7-6Y12

1.3.6.1.4.1.32722.99.99.239341353911714368772597187099978969331

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. (2014). Data From NSCLC-Radiomics (version 4) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI

https://www.cancerimagingarchive.net/collection/nsclc-radiomics/

1.3.6.1.4.1.14519.5.2.1.7085.2626.494695569589117268722281491772

https://www.cancerimagingarchive.net/collection/cptac-ucec/

National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). (2019). The Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma Collection (CPTAC-UCEC) (Version 12) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.3R3JUISW

1.3.6.1.4.1.14519.5.2.1.207544490797667703011829289839681390478

https://www.cancerimagingarchive.net/collection/remind/

Juvekar, P., Dorent, R., Kögl, F., Torio, E., Barr, C., Rigolo, L., Galvin, C., Jowkar, N., Kazi, A., Haouchine, N., Cheema, H., Navab, N., Pieper, S., Wells, W. M., Bi, W. L., Golby, A., Frisken, S., & Kapur, T. (2023). The Brain Resection Multimodal Imaging Database (ReMIND) (Version 1) [dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/3RAG-D070

1.3.12.2.1107.5.1.4.60175.30000008042114404745300000010

Gavrielides, M. A., Kinnard, L. M., Myers, K. J., Peregoy, J., Pritchard, W. F., Zeng, R., Esparza, J., Karanian, J., & Petrick, N. (2015). Data From Phantom FDA [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/k9/TCIA.2015.orbjkmux

https://www.cancerimagingarchive.net/collection/phantom-fda/

1.3.6.1.4.1.14519.5.2.1.6834.5010.992793141464713669479982159310

https://www.cancerimagingarchive.net/collection/4d-lung/

Hugo, G. D., Weiss, E., Sleeman, W. C., Balik, S., Keall, P. J., Lu, J., & Williamson, J. F. (2016). Data from 4D Lung Imaging of NSCLC Patients (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.ELN8YGLE

1.3.6.1.4.1.9328.50.17.15423521354819720574322014551955370036

https://www.cancerimagingarchive.net/collection/rider-lung-pet-ct/

Muzi P, Wanner M, & Kinahan P. (2015). Data From RIDER Lung PET-CT. The Cancer Imaging Archive. https://doi.org/10.7937/k9/tcia.2015.ofip7tvm

1.3.6.1.4.1.14519.5.2.1.9823.1001.134394060407147891170882809392

https://www.cancerimagingarchive.net/collection/prostate-mri/

Choyke P, Turkbey B, Pinto P, Merino M, Wood B. (2016). Data From PROSTATE-MRI. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2016.6046GUDv

1.3.6.1.4.1.14519.5.2.1.191696062987463500085282581898315738844

https://www.cancerimagingarchive.net/collection/upenn-gbm/

Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., Rudie, J. D., Flores Santamaria, N., Fathi Kazerooni, A., Pati, S., Rathore, S., Mamourian, E., Ha, S. M., Parker, W., Doshi, J., Baid, U., Bergman, M., Binder, Z. A., Verma, R., … Davatzikos, C. (2021). Multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the University of Pennsylvania Health System (UPENN-GBM) (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.709X-DN49

1.3.6.1.4.1.14519.5.2.1.4792.2001.921758700577562664959693695481

https://www.cancerimagingarchive.net/collection/breast-diagnosis/

Bloch, B. Nicolas, Jain, Ashali, & Jaffe, C. Carl. (2015). BREAST-DIAGNOSIS [Data set]. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2015.SDNRQXXR

1.3.6.1.4.1.14519.5.2.1.1620.1225.189514895974227080410265976065

Comstock, C. E., Gatsonis, C., Newstead, G. M., Snyder, B. S., Gareen, I. F., Bergin, J. T., Rahbar, H., Sung, J. S., Jacobs, C., Harvey, J. A., Nicholson, M. H., Ward, R. C., Holt, J., Prather, A., Miller, K. D., Schnall, M. D., & Kuhl, C. K. (2023). Abbreviated Breast MRI and Digital Tomosynthesis Mammography in Screening Women With Dense Breasts (EA1141) (Version 1) [dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/2BAS-HR33

https://www.cancerimagingarchive.net/collection/ea1141/