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Localtab Group


Localtab
activetrue
titleData Access

Data Access

Data TypeDownload all or Query/Filter

Images and Segmentations (DICOM, XX.X GB)

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(Download requires the NBIA Data Retriever)

Clinical data with description (XLSX)

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Software/Source Code (External weblink to github)

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urlhttps://github.com/fuentesdt/livermask

Click the Versions tab for more info about data releases.

Please contact help@cancerimagingarchive.net  with any questions regarding usage.


Localtab
titleDetailed Description

Detailed Description

Image Statistics


Modalities

CT, SEG

Number of Patients

105

Number of Studies

214

Number of Series

677

Number of Images

51,968

Images Size (GB)26.6

These SEG were originally created as NIfTI format files (Amira Software, ThermoFisher 2019) , and converted to DICOM.

Github link for the NN code: https://github.com/fuentesdt/livermask

Note - the mask on Patient ID HCC_001 (SEG file Series UID 1.2.276.0.7230010.3.1.3.8323329.719.1600928570.399942) has a slightly different dimension than the CT (Series UI 1.3.6.1.4.1.14519.5.2.1.1706.8374.302065206690360709343725942120) . This difference is is far from the interesting features and the masks, so clinical interpretation should be unaffected by this discrepancy.


Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy

Tcia license 4 noncommercial

Info
titleData Citation

DOI goes here. Create using Datacite with information from Collection Approval form

Moawad A, Fuentes D, Elsayes K.  Multimodality annotated HCC cases with and without advanced imaging segmentation. 

Ahmed W. Moawad, MD

David Fuentes, PhD   

Ali Morshid, MD

Ahmed M. Khalaf

Mohab M. Elmohr

Abdelrahman abusaif, MD

John D. Hazle, PhD

Ahmed O. Kaseb, MD

Manal Hassan, MD

Armeen Mahvash, MD

Janio Szklaruk, MD

Aliyya Qayyom, MD

Khaled M. Elsayes, MD



Info
titlePublication Citation

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


Info
titleAcknowledgement

Only if they ask for special acknowledgments like funding sources, grant numbers, etc in their proposal.


Info
titleTCIA Citation

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

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.


Localtab
titleVersions

Version X (Current): Updated yyyy/mm/dd

Data TypeDownload all or Query/Filter
Images (DICOM, xx.x GB)

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(Requires NBIA Data Retriever.)

Clinical Data (CSV)Link
Software/Source Code (web)

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<< One or two sentences about what you changed since last version.  No note required for version 1. >> 


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