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.
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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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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:
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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/ |
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| Muhammad Shafiq ul Hassan, Geoffrey Zhang, Kujtim Latifi, Ghanim Ullah, Robert Gillies, Eduardo G. Moros. (2019) Computed Tomography Texture Phantom Dataset for Evaluating the Impact of CT Imaging Parameters on Radiomic Features. (link to attached PDF) |
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| 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. DOI: 10.1007/s10278-013-9622-7 |
Additional Publication Resources:The Collection authors suggest the below will give context to this dataset: 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. 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 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 .
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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
Other Publications Using This DataTCIA maintains a list of publications which leverage our |
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Added 9 new subjects of imaging data.
Version 5: Updated 2017/01/31
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Extracted latest release of clinical data (TXT) from the GDC Data Portal.
Version 1: Updated 2014/12/09
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