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Summary

 Nasopharyngeal carcinoma has a higher incidence in China, and it is more common in the southeast coast. MRI and PET-CT are indispensable imaging modalities that can more accurately assess the stage of tumor and guide the treatment planning and evaluation of normal tissue response. The Department of radiation oncology in our hospital has performed the treatment of nasopharyngeal cancer for many years. Has a wealth of clinical experience and a large number of nasopharyngeal carcinoma patients. Inclusion criteria: 1. All patients are pathologically confirmed nasopharyngeal carcinoma, 2. KPS score is larger than 60 and no other serious cardiovascular disease that could affect the course of treatment. Exclusion criteria: The expected survival time is less than 1 month, and the general condition is poor and radiotherapy cannot be completed. Take a CT, MRI and upload the data in imaging archive at the time before radiotherapy, during 15-20 fraction, 1 month after radiotherapy, 3 months after radiotherapy, 6 months after radiotherapy, 9 months after radiotherapy, 1 year after radiotherapy.

Nasopharyngeal carcinoma has a higher incidence rate in Taizhou city. Taizhou Hospital is the largest general hospital in the local region. It has the largest nasopharyngeal carcinoma resources and can represent the highest level of nasopharyngeal diagnosis and treatment in Taizhou. By analyzing our imaging data, we try to to investigate predictive and prognostic radiomic parameters of treatment and survival outcomes for IMRT treated NPC. Also we are going to correlate the difference in radiomic features between MRI and PET-CT scan in predicting treatment and survival outcomes.

Acknowledgements

 This collection consists of 251 CT scans of Credence Cartridge Radiomic (CCR) phantom. This texture phantom was developed to investigate the feature robustness in the emerging field of radiomics. This phantom dataset was acquired on 4-8 CT scanners using a set of imaging parameters (e.g., reconstruction Field of View, Slice thickness, reconstruction kernels, mAs, and Pitch). A controlled scanning approach was employed to assess the variability in radiomic features due to each imaging parameter. This dataset will be useful to radiomic research community to identify a subset of robust radiomic features and for establishing the ground truths for future clinical investigations.

This Phantom dataset can be used for Feature variability assessment due to CT imaging parameters. These phantom scans can be used to identify a subset of robust radiomic features for future clinical investigations. Using this dataset, the numerical values of radiomic features can be cross-validated by other research groups using their own feature extraction tools.

Acknowledgements

This dataset was submitted by Dr. Eduardo G. Moros and Dr. M Shafiq ul Hassan, USF. Special thanks to Moffitt Cancer Center where data were acquired.

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Localtab
activetrue
titleData Access

Data Access

Choosing the Download option will provide you with a file to launch the TCIA Download Manager to download the entire collection. If you want to browse or filter the data to select only specific scans/studies please use the Search By Collection optionClick the  Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever . Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.

Data TypeDownload all or Query/Filter
Images (DICOM,
GB)Image Removed Image Removed
Tissue Slide Images (web
30.5GB)
Clinical Data (CSV)
Genomics (web)Image Removed


Localtab
titleDetailed Description

Detailed Description

Image Statistics

 


Modalities

CT

, MR, PET, PET-CT

Number of

Patients

Participants

400

251

Number of Studies

2000

251

Number of Series

251

Number of Images

57,839

Images Size (GB)30.5



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Added 9 new subjects of imaging data.

Version 5: Updated 2017/01/31

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Added 6 new subjects of imaging data.

Version 4: Updated 2016/08/31

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Added 20 subjects' imaging data.

Version 3: Updated 2016/05/31

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Added 31 new subjects of imaging data.

Version 2: Updated 2016/01/27

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Extracted latest release of clinical data (TXT) from the GDC Data Portal.

Version 1: Updated 2014/12/09
  1. Shafiq ul Hassan M, Latifi K, Zhang G, Ullah G, Gillies R and Moros E. (2018) Voxel size and gray level normalization of CT radiomic features in lung cancer patients. Scientific Reports.

  2. Shafiq ul Hassan M, Zhang G, Hunt D, Latifi K, Ullah G, Gillies R and Moros E, ‘Accounting for reconstruction kernel-induced variability in CT radiomic features using noise power spectra’, J. Med. Imag. 5(1), 011013 (2017). DOI: 10.1117/1.JMI.5.1.011013

  3. Shafiq ul Hassan M, Zhang G, Latifi K, Ullah G, Hunt D, Balagurunathan Y, Abdullah M, Schabath M, Goldgof D, Mackin D, Court L, Gillies R and Moros E. (2017) Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med. Phys. 44(3), p-1050-1062 .

  4. Paul R, Shafiq ul Hassan M, Moros E, Gillies R, Hall L, Goldgof D. (2018) Stability of deep features across CT scanners and Field Of View (FOV) using a physical phantom. Proc SPIE Medical Imaging Conference, February 2018, Texas, USA

Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

TCGA collections have special publication embargoes which must be followed in addition to our normal data usage policies. See the TCGA section within TCIA's Data Usage Policies and Restrictions for additional details. After the publication embargo period ends these collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:
Public collection license
Info
titleData Citation

Kirk, S., Lee, Y., Lucchesi, F. R., Aredes, N. D., Gruszauskas, N., Catto, J., … Lemmerman, J. (2016). Radiology Data from The Cancer Genome Atlas Urothelial Bladder Carcinoma [TCGA-BLCA] collection. The Cancer Imaging Archive. Shafiq ul Hassan M, Zhang G, Latifi K, Ullah G, Gillies R, Moros E. Credence Cartridge Radiomics Phantom CT Scans with Controlled Scanning Approach. 2018. (DOI: http://doi.org/10.7937/K9/TCIA.2016.8LNG8XDR/TCIA.2019.4l24tz5g


Info
titlePublication Citation

Muhammad Shafiq ul Hassan, Geoffrey Zhang, Kujtim Latifi, Ghanim Ullah, Robert Gillies, Eduardo G. Moros. Computed Tomography Texture Phantom Dataset for Evaluating the Impact of CT Imaging Parameters on Radiomic Features. (link to paper)


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. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)DOI: 10.1007/s10278-013-9622-7

Other Publications Using This Data

TCIA maintains a list of publications which leverage our data. At this time we are not aware of any manuscripts based on this data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

Localtab
titleVersions

Version 6 (Current): Updated

Data TypeDownload all or Query/Filter
Images (DICOM, GB)
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Genomics (web)Image Removed
Data TypeDownload all or Query/Filter
Images (DICOM, 32.9GB)
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Data TypeDownload all or Query/Filter
Images (DICOM, 31.0GB)
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Data TypeDownload all or Query/Filter
Images (DICOM, 25.2GB)
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Data TypeDownload all or Query/Filter
Images (DICOM, 9.4GB)
Clinical Data (TXT)Image Removed
Genomics (web)Image Removed


Localtab
titleVersions

Version 1 (Current): 02-27-2019

Data TypeDownload all or Query/Filter
Images (DICOM,
9
30.
4GB
5GB)
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Clinical Data (TXT)Image Removed
Genomics (web)Image Removed