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

Summary

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Hepatocellular carcinoma (HCC) is the most common primary liver cancer 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:

  • The University of Texas MD Anderson Cancer Center, Departments of Imaging Physics, Body Imaging, Gastrointestinal Oncology, Epidemiology, and Interventional Radiology.

  • 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 TypeDownload all or Query/FilterLicense

Images and Segmentations (DICOM, 26.6 GB)


   
(Download requires the NBIA Data Retriever)

CC BY 4.0

Clinical data with description (XLSX, 125 kB)


 

CC BY 4.0

Click the Versions tab for more info about data releases.

Additional Resources for this Dataset

The following external resources have been made available by the data submitters.  These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.

The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.


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.

Citations & Data Usage Policy

Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 4.0 International License under which it has been published. Attribution should include references to the following citations:

Data Citation

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

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 

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.

  1. Moawad, A. W., Morshid, A., Khalaf, A. M., Elmohr, M. M., Hazle, J. D., Fuentes, D., Badawy, M., Kaseb, A. O., Hassan, M., Mahvash, A., Szklaruk, J., Qayyum, A., Abusaif, A., Bennett, W. C., Nolan, T. S., Camp, B., & Elsayes, K. M. (2023). Multimodality annotated hepatocellular carcinoma data set including pre- and post-TACE with imaging segmentation. In Scientific Data (Vol. 10, Issue 1).  https://doi.org/10.1038/s41597-023-01928-3

Version 1 (Current): Updated 2022/08/17

Data TypeDownload all or Query/FilterLicense

Images and Segmentations (DICOM, 26.6 GB)


   

(Download requires the NBIA Data Retriever)

CC BY 4.0

Clinical data with description (XLSX)


 

CC BY 4.0



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