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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.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the TCGA Bladder Phenotype Research Group.

Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection:

  • University of North Carolina- Special thanks to J. Keith Smith, M.D., Ph.D. and Shanah Kirk from the Department of Radiology. 
  • Barretos Cancer Hospital, Barretos, São Paulo, Brazil – Special Thanks to Fabiano Rubião Lucchesi, MD and Natália Del Angelo Aredes
  • University of Chicago- Special thanks to Nicholas Gruszauskas, Ph.D.
  • University of Sheffield - Special thanks to James Catto, MB, ChB, PhD, FRCS from the Department of Oncology.
  • Memorial Sloan-Kettering Cancer Center, New York, NY - Special thanks to Hebert A. Vargas Alvarez, MD and Pierre Elnajjar.
  • Lahey Hospital & Medical Center, Burlington, MA - Special thanks to John Lemmerman, RT and Kimberly Reiger-Christ, PhD, Cancer Research, Sophia Gordon Cancer Center.
  • University of Southern California- Special thanks to Siamak Daneshmand, MD, from the Department of Urology and Vinay Duddalwar, MD, FRCR from the Department of Radiology.

 

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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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
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Localtab
titleDetailed Description

Detailed Description

Image Statistics

 


Modalities

CT

, CR, MR, PT

Number of

Patients

Participants

106

251

Number of Studies

135

251

Number of Series

827

251

Number of Images

78

57,

429

839

Images Size (GB)
40
30.
1

GDC Data Portal - Clinical and Genomic Data

The GDC Data Portal has extensive clinical and genomic data, which can be matched to the patient identifiers on the images here in TCIA.  Below is a snapshot of clinical data extracted on 1/27/2016.

Explanations of the clinical data can be found on the Biospecimen Core Resource Clinical Data Forms linked below:

A Note about TCIA and TCGA Subject Identifiers and Dates

Subject Identifiers: a subject with radiology images stored in TCIA is identified with a Patient ID that is identical to the Patient ID of the same subject with demographic, clinical, pathological, and/or genomic data stored in TCGA. For each TCGA case, the baseline TCGA imaging studies found on TCIA are pre-surgical. 

Dates: TCIA and TCGA handle dates differently, and there are no immediate plans to reconcile:

  • TCIA Dates: dates (be they birth dates, imaging study dates, etc.) in the Digital Imaging and Communications in Medicine (DICOM) headers of TCIA radiology images have been offset by a random number of days. The offset is a number of days between 3 and 10 years prior to the real date that is consistent for each TCIA image-submitting site and collection, but that varies among sites and among collections from the same site. Thus, the number of days between a subject’s longitudinal imaging studies are accurately preserved when more than one study has been archived while still meeting HIPAA requirements.
  • TCGA Dates: the patient demographic and clinical event dates are all the number of days from the index date, which is the actual date of pathologic diagnosis. So all the dates in the data are relative negative or positive integers, except for the “days_to_pathologic_diagnosis” value, which is 0 – the index date. The years of birth and diagnosis are maintained in the distributed clinical data file. The NCI retains a copy of the data with complete dates, but those data are not made available.With regard to other TCGA dates, if a date comes from a HIPAA “covered entity’s” medical record, it is turned into the relative day count from the index date. Dates like the date TCGA received the specimen or when the TCGA case report form was filled out are not such covered dates, and they will appear as real dates (month, day, and year).
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Added 6 new subjects of imaging data.

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Added 31 new subjects of imaging data.

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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.20162019.8LNG8XDR4l24tz5g


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 2017/10/30

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Localtab
titleVersions

Version 1 (Current): 02-27-2019

Data TypeDownload all or Query/Filter
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5GB)
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