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TOMOGRAPHY, December 2016, Volume 2, Issue 4: 283-294
DOI: 10.18383/j.tom.2016.00163

A Rapid Segmentation-Insensitive 'Digital Biopsy' Method for Radiomic Feature Extraction; Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer

Sebastian Echegaray1, Viswam Nair2,3,4, Michael Kadoch2, Ann Leung2, Daniel Rubin2,3, Olivier Gevaert3, and Sandy Napel2

1Department of Electrical Engineering, Stanford University, Stanford, California; 2Department of Radiology, Stanford University School of Medicine, Stanford, California; 3Department of Medicine, Stanford University School of Medicine, Stanford, California; and 4Canary Center for Cancer Early Detection, Stanford University, Stanford, California


Quantitative imaging approaches compute features within images’ regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non–small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of 0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required.


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