Summary
PURPOSE: To determine the variability of lesion size measurements in computed tomography data sets of patients imaged under a “no change” (“coffee break”) condition and to determine the impact of two reading paradigms on measurement variability.METHOD AND MATERIALS: Using data sets from 32 RIDER Lung CT patients and 8 RIDER Pilot patients scanned twice within 15 minutes (“no change”), measurements were performed by five radiologists in two phases: (1) independent reading of each computed tomography dataset (timepoint): (2) a locked, sequential reading of datasets. Readers performed measurements using several sizing methods, including one-dimensional (1D) longest in-slice dimension and 3D semi-automated segmented volume. Change in size was estimated by comparing measurements performed on both timepoints for the same lesion, for each reader and each measurement method. For each reading paradigm, results were pooled across lesions, across readers, and across both readers and lesions, for each measurement method.
For additional information please see https://qibawiki.rsna.org/index.php/VolCT_-_Group_1B and the Release Notes from which the following may be specially useful: "Results are described in DICOM SR files, which in turn reference DICOM segmentation files that encode the region as a 3D raster, and presentation states that record the zoom, pan and window levels at the time of measurement" and "Readers are identified by number (from 1 through 5) ... and their actual identity recorded in the SR tree in observer context and worklist descriptions has been removed."
Acknowledgements
- CoreLab Partners, Inc conducted the reader study component of this investigation. They provided the reading facility, review workstations, software, and logistical support. CoreLab Partners radiologists also participated as readers. Therefore, we acknowledge CoreLab Partners for their support and specifically acknowledge CoreLab Partners radiologists Kevin Byrne, Steven Kaplan, Julie Barudin, Joyce Sherman, Kathy Slazak, George Edeburn, and J. Michael O'Neal for participating as readers in this study.
- We acknowledge financial support from the RSNA Quantitative Imaging Biomarker Alliance (QIBA) provided by National Institute of Biomedical Imaging and Bioengineering American Recovery and Reinvestment Act of 2009 funds.
Data Access
Data Type | Download all or Query/Filter | License |
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Segmentations and Reports (DICOM, 348 MB) |
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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.
- Spreadsheet of locations or reader results https://qibawiki.rsna.org/index.php/VolCT_-_Group_1B#Downloadable_Images_and_Data_Sets
Collections Used in this Analysis
Below is a list of the Collections used in these analyses:
Source Data Type | Download or Query/Filter | License |
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Original corresponding images from RIDER Pilot (DICOM, 10.4 GB) |
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Original corresponding images from RIDER Lung CT (DICOM,7.55 GB) | (Download requires the NBIA Data Retriever) |
Detailed Description
Image Statistics | Radiology Image Statistics |
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Modalities | SR, SEG, PR |
Number of Patients | 40 |
Number of Studies | 323 |
Number of Series | 1,508 |
Number of Images | 1508 |
Images Size (GB) | 0.348 |
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Citations & Data Usage Policy
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
Data Citation
McNitt-Gray, M., Kim, H., Zhao, B., Schwartz, L. H., Clunie, D., Cohen, K., PETRICK, N., Fenimore, C., Lu, Z. Q. J., & Buckler, A. (2020). QIBA VolCT Group 1B Round 2 No Change Size Measurements (QIBA-VolCT-1B) [Data set]. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/tcia.2020.1c3h-vp70
Publication Citation
McNitt-Gray M. F., Hyun Kim G., Zhao B., Schwartz L.H., Clunie D., Cohen K., Petrick N., Fenimore C., Lu Z.Q.J., Buckler A.J. (2015) Determining the Variability of Lesion Size Measurements from CT Patient Data Sets Acquired under “No Change” Conditions. Translational Oncology 8(1):55-64. https://doi.org/10.1016/j.tranon.2015.01.001
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. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7 PMCID: PMC3824915
Other Publications Using This Data
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Version 1 (Current): Updated 2023/05/24
Data Type | Download all or Query/Filter | License |
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Segmentations and Reports (DICOM, 348 MB) |
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