Journal article
Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images
Tomography (Ann Arbor), Vol.6(2), pp.65-76
06/2020
DOI: 10.18383/j.tom.2020.00004
PMID: 32548282
Abstract
Quantitative imaging biomarkers (QIBs) provide medical image–derived intensity, texture, shape, and size features that may help characterize cancerous tumors and predict clinical outcomes. Successful clinical translation of QIBs depends on the robustness of their measurements. Biomarkers derived from positron emission tomography images are prone to measurement errors owing to differences in image processing factors such as the tumor segmentation method used to define volumes of interest over which to calculate QIBs. We illustrate a new Bayesian statistical approach to characterize the robustness of QIBs to different processing factors. Study data consist of 22 QIBs measured on 47 head and neck tumors in 10 positron emission tomography/computed tomography scans segmented manually and with semiautomated methods used by 7 institutional members of the NCI Quantitative Imaging Network. QIB performance is estimated and compared across institutions with respect to measurement errors and power to recover statistical associations with clinical outcomes. Analysis findings summarize the performance impact of different segmentation methods used by Quantitative Imaging Network members. Robustness of some advanced biomarkers was found to be similar to conventional markers, such as maximum standardized uptake value. Such similarities support current pursuits to better characterize disease and predict outcomes by developing QIBs that use more imaging information and are robust to different processing factors. Nevertheless, to ensure reproducibility of QIB measurements and measures of association with clinical outcomes, errors owing to segmentation methods need to be reduced.
Details
- Title: Subtitle
- Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images
- Creators
- Brian J Smith - Departments of Biostatistics.John M Buatti - Radiation Oncology; and.Christian Bauer - Electrical and Computer EngineeringEthan J Ulrich - University of IowaPayam Ahmadvand - Simon Fraser UniversityMikalai M Budzevich - Moffitt Cancer CenterRobert J Gillies - Moffitt Cancer CenterDmitry Goldgof - University of South FloridaMilan Grkovski - Memorial Sloan Kettering Cancer CenterGhassan Hamarneh - Simon Fraser UniversityPaul E Kinahan - University of Washington Medical CenterJohn P Muzi - University of Washington Medical CenterMark Muzi - University of Washington Medical CenterCharles M Laymon - University of PittsburghJames M Mountz - University of PittsburghSadek Nehmeh - Cornell UniversityMatthew J Oborski - University of PittsburghBinsheng Zhao - Columbia University Irving Medical CenterJohn J Sunderland - University of IowaReinhard R Beichel - Electrical and Computer Engineering
- Resource Type
- Journal article
- Publication Details
- Tomography (Ann Arbor), Vol.6(2), pp.65-76
- DOI
- 10.18383/j.tom.2020.00004
- PMID
- 32548282
- NLM abbreviation
- Tomography
- ISSN
- 2379-1381
- eISSN
- 2379-139X
- Publisher
- Grapho Publications, LLC
- Language
- English
- Date published
- 06/2020
- Academic Unit
- Radiology; Electrical and Computer Engineering; Biostatistics; Physics and Astronomy; Radiation Oncology; Neurosurgery; Otolaryngology; Holden Comprehensive Cancer Center
- Record Identifier
- 9984197248702771
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