150 lines
9.8 KiB
Markdown
150 lines
9.8 KiB
Markdown
# OHIF public demo data sets
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The OHIF Viewer's public demo page, available at https://viewer.ohif.org/, uses publicly anonymized demo datasets.
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These datasets were mostly obtained from the [NIH NCI Imaging Data Commons](https://datacommons.cancer.gov/repository/imaging-data-commons)
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and [NIH NCI TCIA](https://www.cancerimagingarchive.net/). Before listing the datasets,
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we would like to extend a special thank you to all groups who have made their datasets publicly available.
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Without them, we would not have been able to create this demo page.
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Please find below the list of datasets used on the demo page, along with their respective citations.
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## Platforms
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### NIH NCI IDC
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- 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.
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### NIH NCI TCIA
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- 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
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## Datasets
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Below you can find the StudyInstanceUID of the studies that are used in the demo page along with their citations.
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### 1.3.6.1.4.1.14519.5.2.1.267424821384663813780850856506829388886
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Segmentation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Open Annotated Dataset and Baseline Algorithm (Vestibular-Schwannoma-SEG)
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- 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
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- 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
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### 1.3.6.1.4.1.14519.5.2.1.7009.2403.334240657131972136850343327463
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### 1.3.6.1.4.1.14519.5.2.1.7009.2403.871108593056125491804754960339
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ACRIN-NSCLC-FDG-PET (ACRIN 6668)
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- 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
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- 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
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### 2.25.103659964951665749659160840573802789777
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The Cancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM)
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- 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
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### 1.3.6.1.4.1.14519.5.2.1.256467663913010332776401703474716742458
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Abdominal or pelvic enhanced CT images within 10 days before surgery of 230 patients with stage II colorectal cancer (StageII-Colorectal-CT)
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- 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
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- 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
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### 1.3.6.1.4.1.14519.5.2.1.3023.4024.215308722288168917637555384485
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The Cancer Genome Atlas Sarcoma Collection (TCGA-SARC)
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- Roche, C., Bonaccio, E., & Filippini, J. (2016). The Cancer Genome Atlas Sarcoma Collection (TCGA-SARC) (Version 3) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.CX6YLSUX
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### 1.3.6.1.4.1.14519.5.2.1.4792.2001.105216574054253895819671475627
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BREAST-DIAGNOSIS
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- 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
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### 1.3.6.1.4.1.14519.5.2.1.1706.8374.643249677828306008300337414785
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Multimodality annotated HCC cases with and without advanced imaging segmentation (HCC-TACE-Seg)
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- 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
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- 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
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### 1.3.6.1.4.1.14519.5.2.1.1188.2803.137585363493444318569098508293
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Ultrasound data of a variety of liver masses (B-mode-and-CEUS-Liver)
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- Eisenbrey, J., Lyshchik, A., & Wessner, C. (2021). Ultrasound data of a variety of liver masses [Data set]. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/TCIA.2021.v4z7-tc39
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### 1.3.6.1.4.1.32722.99.99.62087908186665265759322018723889952421
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NSCLC-Radiomics
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- 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
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- 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)
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### 1.3.6.1.4.1.14519.5.2.1.3671.4754.298665348758363466150039312520
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QIN-PROSTATE-Repeatability
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- 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
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- 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
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- 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:
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### 2.25.141277760791347900862109212450152067508
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The Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma Collection (CPTAC-CCRCC)
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- 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
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- 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).”
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### 2.25.275741864483510678566144889372061815320
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National Lung Screening Trial
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- 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
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- 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
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### 1.3.6.1.4.1.14519.5.2.1.99.1071.26968527900428638961173806140069
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Stony Brook University COVID-19 Positive Cases (COVID-19-NY-SBU)
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- 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
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### 2.16.840.1.114362.1.11972228.22789312658.616067305.306.2
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https://data.kitware.com/
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