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CC-RADIOMICS-PHANTOM - The Cancer Imaging Archive (TCIA)
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CC-RADIOMICS-PHANTOM


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The Cancer Imaging Archive

CC-Radiomics-Phantom | Credence Cartridge Radiomics Phantom CT Scans

DOI: 10.7937/K9/TCIA.2017.zuzrml5b | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Lung Phantom Human 17 RTSTRUCT, CT Phantom 1.43GB Image Analyses Public, Complete 2017/07/28

Summary

This collection consists of 17 CT scans of the Credence Cartridge Radiomics (CCR) phantom, which was designed for use in studies of texture feature robustness. The scans were acquired at four medical centers using each center’s chest protocol and were taken using GE (7 scans), Philips (5 scans), Siemens (2 scans), and Toshiba (3 scans) scanners. The CCR phantom has 10 cartridges, each with a unique texture, Fig 1. The first four cartridges are 3D printed ABS plastic with 20%, 30%, 40%, and 50% honeycomb fill, and they provide regular, periodic textures. The next three cartridges provide natural textures: sycamore wood, cork, and extra dense cork. A cartridges of shredded rubber particles provides textures similar to those of non-small cell lung cancer. The ninth cartridge is solid, homogenous acrylic and provides a minimal texture control. Finally, the tenth cartridge is 3D printed plaster with the highest electron density (400 – 600 HU) and is intended to be more similar to bone.

In addition to the DICOM images for the 17 scans, this collection also contains two sets of contours as DICOM RT structure files. The first set provides 8x8x2 cm3 contours for each cartridge in each scan. The second set provides 16 adjacent 2x2x2 cm3 contours for each cartridge in each scan. Researchers studying radiomics will be able to evaluate features for robustness across a variety of scanners. Features can be calculated using the researchers own software or third party software such as IBEX (imaging biomarker explorer).

Related publications:

Mackin, D., Fave, X., Zhang, L., Fried, D., Yang, J., Taylor, B., Rodriguez-Rivera, E., Dodge, C., Jones, A. K., & Court, L. (2015). Measuring Computed Tomography Scanner Variability of Radiomics Features. In Investigative Radiology (Vol. 50, Issue 11, pp. 757–765). Ovid Technologies (Wolters Kluwer Health). https://doi.org/10.1097/rli.0000000000000180          

The following paper was generated on different imaging modalities but the same phantom, this is a related but independent paper with a different set of authors: Fave, X., Mackin, D., Yang, J., Zhang, J., Fried, D., Balter, P., Followill, D., Gomez, D., Kyle Jones, A., Stingo, F., Fontenot, J., & Court, L. (2015). Can radiomics features be reproducibly measured from CBCT images for patients with non‐small cell lung cancer? In Medical Physics (Vol. 42, Issue 12, pp. 6784–6797). Wiley. https://doi.org/10.1118/1.4934826

 

Data Access

Version 1: Updated 2017/07/28

Title Data Type Format Access Points Subjects Studies Series Images License
Images and Radiation Therapy Structures RTSTRUCT, CT DICOM
Download requires NBIA Data Retriever
17 17 51 2,672 CC BY 3.0
Related Datasets
No related Analysis Results found: Submit your proposal! No related Collections found
Legend: Analysis Results| Collections

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

Mackin, D., Ray, X., Zhang, L., Fried, D., Yang, J., Taylor, B., Rodriguez-Rivera, E., Dodge, C., Jones, A., & Court, L. (2017). Data From Credence Cartridge Radiomics Phantom CT Scans (CC-Radiomics-Phantom) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.zuzrml5b 

Detailed Description

Supporting Documentation and Metadata

Acquisition parameters for the phantom scans in this Collection:

Scan

Manufacturer

Model

Kernel

Type

Slice Thickness (mm)

Pixel (mm)

Spiral Pitch Factor

kVp

Effective mAs

CTDIvol (mGy)

CCR1-GE1

GE

Discovery CT750 HD

standard

helical

2.5

0.49

0.98

120

81

6.19

CCR1-GE2

GE

Discovery CT750 HD

standard

axial

2.5

0.70

1.00

120

300

CCR1-GE3

GE

Discovery CT750 HD

standard

helical

2.5

0.78

0.98

120

122

9.3

CCR1-GE4

GE

Discovery ST

standard

helical

2.5

0.98

1.35

120

143

16.3

CCR1-GE5

GE

LightSpeed RT

standard

helical

2.5

0.98

0.75

120

1102

53.6

CCR1-GE6

GE

LightSpeed RT16

standard

helical

2.5

0.98

0.94

120

367

18.8

CCR1-GE7

GE

LightSpeed VCT

standard

helical

2.5

0.74

0.98

120

82

CCR1-P1

Philips

Brilliance Big Bore

B

helical

3.0

0.98

0.94

120

320

17.8

CCR1-P2

Philips

Brilliance Big Bore

C

helical

3.0

0.98

0.94

120

369

15.8

CCR1-P3

Philips

Brilliance Big Bore

B

helical

3.0

1.04

0.81

120

320

19.9

CCR1-P4

Philips

Brilliance Big Bore

B

helical

3.0

1.04

0.81

120

369

19.9

CCR1-P5

Philips

Brilliance 64

B

helical

3.0

0.98

0.67

120

372

16.4

CCR1-S1

Siemens

Sensation Open

B31s

axial

3.0

0.54

1.00

120

26 – 70

1.5

CCR1-S2

Siemens

SOMATOM Definition Flash

[‘I70f’, ‘2’]

helical

2.0

0.52

0.60

120

17 – 28

CCR1-T1

Toshiba

Aquilion

FC18

helical

3.0

0.63

1.11

120

135

4.0

CCR1-T2

Toshiba

Aquilion

FC18

helical

3.0

0.63

1.11

120

135

3.8

CCR1-T3

Toshiba

Aquilion ONE

FC18

helical

3.0

0.98

0.99

120

151

13.5

Acknowledgements

This data set was provided to TCIA by Authors: Mackin, Dennis; Fave, Xenia; Zhang, Lifei; Fried, David; Yang, Jinzhong; Taylor, Brian; Rodriguez-Rivera, Edgardo; Dodge, Cristina; Jones, Aaron Kyle; and Court, Laurence. 

Related Publications

Publications by the Dataset Authors

The authors recommended this paper as the best source of additional information about this dataset:

No publications by dataset authors were found.

Research Community Publications

The Collection authors suggest the below will give context to this dataset:

  1. Mackin, D., Fave, X., Zhang, L., Fried, D., Yang, J., Taylor, B., Rodriguez-Rivera, E., Dodge, C., Jones, A. K., & Court, L. (2015). Measuring Computed Tomography Scanner Variability of Radiomics Features. In Investigative Radiology (Vol. 50, Issue 11, pp. 757–765). Ovid Technologies (Wolters Kluwer Health). https://doi.org/10.1097/rli.0000000000000180
  2. The following paper was generated on different imaging modalities but the same phantom, this is a related but independent paper with a different set of authors:
    Fave, X., Mackin, D., Yang, J., Zhang, J., Fried, D., Balter, P., Followill, D., Gomez, D., Kyle Jones, A., Stingo, F., Fontenot, J., & Court, L. (2015). Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? In Medical Physics (Vol. 42, Issue 12, pp. 6784–6797). Wiley. https://doi.org/10.1118/1.4934826

 

TCIA maintains a list of publications which leverage our data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

  • Gu, J., Li, B., Shu, H., Zhu, J., Qiu, Q., & Bai, T. (2022). Development and verification of radiomics framework for computed tomography image segmentation. Medical Physics, 1-32. doi:10.1002/mp.15904.
  • Ibrahim, A., Barufaldi, B., Refaee, T., Silva Filho, T. M., Acciavatti, R. J., Salahuddin, Z., . . . Lambin, P. (2022). MaasPenn radiomics reproducibility score: A novel quantitative measure for evaluating the reproducibility of CT-based handcrafted radiomic features. Cancers, 14(7), 1599. doi:10.3390/cancers14071599.
  • Ibrahim, A., Refaee, T., Leijenaar, R. T., Primakov, S., Hustinx, R., Mottaghy, F. M., . . . Lambin, P. (2021). The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset. PLoS One, 16(5), e0251147. doi:10.1371/journal.pone.0251147.
  • Kalendralis, P. (2022). Artificial intelligence applications in radiotherapy: The role of the FAIR data principles. (Ph.D. Dissertation). Maastricht University ,The Netherlands, https://doi.org/10.26481/dis.20221010pk
  • Refaee, T., Salahuddin, Z., Widaatalla, Y., Primakov, S., Woodruff, H. C., Hustinx, R., . . . Lambin, P. (2022). CT Reconstruction Kernels and the Effect of Pre- and Post-Processing on the Reproducibility of Handcrafted Radiomic Features. J Pers Med, 12(4). doi:10.3390/jpm12040553.

Other Publications Using this Data

The Collection authors suggest the below will give context to this dataset:

  1. Mackin, D., Fave, X., Zhang, L., Fried, D., Yang, J., Taylor, B., Rodriguez-Rivera, E., Dodge, C., Jones, A. K., & Court, L. (2015). Measuring Computed Tomography Scanner Variability of Radiomics Features. In Investigative Radiology (Vol. 50, Issue 11, pp. 757–765). Ovid Technologies (Wolters Kluwer Health). https://doi.org/10.1097/rli.0000000000000180
  2. The following paper was generated on different imaging modalities but the same phantom, this is a related but independent paper with a different set of authors:
    Fave, X., Mackin, D., Yang, J., Zhang, J., Fried, D., Balter, P., Followill, D., Gomez, D., Kyle Jones, A., Stingo, F., Fontenot, J., & Court, L. (2015). Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? In Medical Physics (Vol. 42, Issue 12, pp. 6784–6797). Wiley. https://doi.org/10.1118/1.4934826

 

TCIA maintains a list of publications which leverage our data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

  • Gu, J., Li, B., Shu, H., Zhu, J., Qiu, Q., & Bai, T. (2022). Development and verification of radiomics framework for computed tomography image segmentation. Medical Physics, 1-32. doi:10.1002/mp.15904.
  • Ibrahim, A., Barufaldi, B., Refaee, T., Silva Filho, T. M., Acciavatti, R. J., Salahuddin, Z., . . . Lambin, P. (2022). MaasPenn radiomics reproducibility score: A novel quantitative measure for evaluating the reproducibility of CT-based handcrafted radiomic features. Cancers, 14(7), 1599. doi:10.3390/cancers14071599.
  • Ibrahim, A., Refaee, T., Leijenaar, R. T., Primakov, S., Hustinx, R., Mottaghy, F. M., . . . Lambin, P. (2021). The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset. PLoS One, 16(5), e0251147. doi:10.1371/journal.pone.0251147.
  • Kalendralis, P. (2022). Artificial intelligence applications in radiotherapy: The role of the FAIR data principles. (Ph.D. Dissertation). Maastricht University ,The Netherlands, https://doi.org/10.26481/dis.20221010pk
  • Refaee, T., Salahuddin, Z., Widaatalla, Y., Primakov, S., Woodruff, H. C., Hustinx, R., . . . Lambin, P. (2022). CT Reconstruction Kernels and the Effect of Pre- and Post-Processing on the Reproducibility of Handcrafted Radiomic Features. J Pers Med, 12(4). doi:10.3390/jpm12040553.