20 October 2017 Interobserver variability in tumor contouring affects the use of radiomics to predict mutational status
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J. of Medical Imaging, 5(1), 011005 (2017). doi:10.1117/1.JMI.5.1.011005
Abstract
Radiomic features characterize tumor imaging phenotype. Nonsmall cell lung cancer (NSCLC) tumors are known for their complexity in shape and wide range in density. We explored the effects of variable tumor contouring on the prediction of epidermal growth factor receptor (EGFR) mutation status by radiomics in NSCLC patients treated with a targeted therapy (Gefitinib). Forty-six early stage NSCLC patients (EGFR mutant:wildtype = 20:26) were included. Three experienced radiologists independently delineated the tumors using a semiautomated segmentation software on a noncontrast-enhanced baseline and three-week post-therapy CT scan images that were reconstructed using 1.25-mm slice thickness and lung kernel. Eighty-nine radiomic features were computed on both scans and their changes (radiomic delta-features) were calculated. The highest area under the curves (AUCs) were 0.87, 0.85, and 0.80 for the three radiologists and the number of significant features ( AUC > 0.8 ) was 3, 5, and 0, respectively. The AUCs of a single feature significantly varied among radiologists (e.g., 0.88, 0.75, and 0.73 for run-length primitive length uniformity). We conclude that a three-week change in tumor imaging phenotype allows identifying the EGFR mutational status of NSCLC. However, interobserver variability in tumor contouring translates into a significant variability in radiomic metrics accuracy.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Qiao Huang, Lin Lu, Laurent Dercle, Philip Lichtenstein, Yajun Li, Qian Yin, Min Zong, Lawrence Schwartz, Binsheng Zhao, "Interobserver variability in tumor contouring affects the use of radiomics to predict mutational status," Journal of Medical Imaging 5(1), 011005 (20 October 2017). http://dx.doi.org/10.1117/1.JMI.5.1.011005 Submission: Received 30 June 2017; Accepted 21 September 2017
Submission: Received 30 June 2017; Accepted 21 September 2017
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KEYWORDS
Tumors

Computed tomography

Image segmentation

Lung

Lung cancer

Tissues

Receptors

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