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 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.
RESULTS: The mean percent difference (± SD) when pooled across both readers and lesions for 1D and 3D measurements extracted from contours was 2.8 ± 22.2% and 23.4 ± 105.0%, respectively, for the independent reads. For the locked, sequential reads, the mean percent differences (± SD) reduced to 2.52 ± 14.2% and 7.4 ± 44.2% for the 1D and 3D measurements, respectively.
CONCLUSION: Even under a “no change” condition between scans, there is variation in lesion size measurements due to repeat scans and variations in reader, lesion, and measurement method. This variation is reduced when using a locked, sequential reading paradigm compared to an independent reading paradigm.
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, XX.X GB) |
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Additional 8 Participants' DICOM not in RIDER Lung CT but rather in RIDER Pilot data (DICOM, XX.X GB) | (Download requires the NBIA Data Retriever) |
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Collections Used in this Analysis
Below is a list of the Collections used in these analyses:
- RIDER Lung CT
- RIDER Pilot
Please contact help@cancerimagingarchive.net with any questions regarding usage.
Detailed Description
Image Statistics | |
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Modalities | SR, SEG, PR |
Number of Patients | 32 or 40 |
Number of Studies | |
Number of Series | 1,508 ? |
Number of Images | |
Images Size (GB) | ~350 MB |
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."
Citations & Data Usage Policy
Data Citation
DOI goes here. Create using Datacite with information from Collection Approval form
https://doi.org/10.7937/tcia.2020.1c3h-vp70 is in draft mode , that is, coming soon.
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. 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
Other Publications Using This Data
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Version 1 (Current): Updated 2020/mm/dd
Data Type | Download all or Query/Filter |
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Images (DICOM, xx.x GB) | (Requires NBIA Data Retriever.) |