Summary
Hepatocellular carcinoma (HCC) is the most common primary liver with incidences doubled over the past two decades due to increasing risk factors. Despite surveillance, the majority of HCC cases are diagnosed at advanced stages that can be treated only using (Transarterial chemoembolization) TACE, or systemic therapy. TACE failure can occur to 60% of patients receiving the procedure, with subsequent financial and emotional burden. Radiomics have emerged as a new tool capable of predicting tumor response to TACE from pre-procedural CT study.This retrospectively acquired data collection includes pre- and post-procedure CT imaging studies of 105 confirmed HCC patients who underwent TACE between 2002 and 2012 with an available treatment outcome, in the form of time-to-progression and overall survival. Baseline imaging includes multiphasic contrast-enhanced CT with no image artifacts (e.g. surgical clip) and was obtained 1-12 weeks (average 3 weeks) prior to the first TACE session. Semiautomatic segmentation of liver, tumor, and blood vessels created using AMIRA was manually clinically curated. These segmentations of each pre-procedural CT study were done for the purpose of algorithm training for prediction and automatic liver tumor segmentation, and are provided here (NIfTI converted to DICOM-SEG format).
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.
Harmonization of the components of this dataset, including into standard DICOM representation, was supported in part by the NCI Imaging Data Commons consortium. NCI Imaging Data Commons consortium is supported by the contract number 19X037Q from Leidos Biomedical Research under Task Order HHSN26100071 from NCI.
Data Access
Data Type | Download all or Query/Filter |
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Images and Segmentations (DICOM, XX.X GB) | (Download requires the NBIA Data Retriever) |
Clinical data with description (XLSX) | |
Software/Source Code (External weblink to github) |
Click the Versions tab for more info about data releases.
Please contact help@cancerimagingarchive.net with any questions regarding usage.
Detailed Description
Image Statistics | |
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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.
Citations & Data Usage Policy
Data 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. https://doi.org/10.7937/tcia.5fna-0924
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 |
Publication 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
Acknowledgement
Only if they ask for special acknowledgments like funding sources, grant numbers, etc in their proposal.
TCIA 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.
Version X (Current): Updated yyyy/mm/dd
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Images (DICOM, xx.x GB) | (Requires NBIA Data Retriever.) |
Clinical Data (CSV) | Link |
Software/Source Code (web) |
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