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Article

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

1
Department of Electrical Engineering, Stanford University, Stanford, CA, USA
2
Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
3
Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
4
Canary Center for Cancer Early Detection, Stanford University, Stanford, CA, USA
*
Author to whom correspondence should be addressed.
Tomography 2016, 2(4), 283-294; https://doi.org/10.18383/j.tom.2016.00163
Received: 2 September 2016 / Revised: 4 October 2016 / Accepted: 6 November 2016 / Published: 1 December 2016
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.
Keywords: radiomics; segmentation; image processing; medical imaging; quantitative imaging radiomics; segmentation; image processing; medical imaging; quantitative imaging
MDPI and ACS Style

Echegaray, S.; Nair, V.; Kadoch, M.; Leung, A.; Rubin, D.; Gevaert, O.; Napel, S. A Rapid Segmentation-Insensitive “Digital Biopsy” Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non–Small Cell Lung Cancer. Tomography 2016, 2, 283-294. https://doi.org/10.18383/j.tom.2016.00163

AMA Style

Echegaray S, Nair V, Kadoch M, Leung A, Rubin D, Gevaert O, Napel S. A Rapid Segmentation-Insensitive “Digital Biopsy” Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non–Small Cell Lung Cancer. Tomography. 2016; 2(4):283-294. https://doi.org/10.18383/j.tom.2016.00163

Chicago/Turabian Style

Echegaray, Sebastian, Viswam Nair, Michael Kadoch, Ann Leung, Daniel Rubin, Olivier Gevaert, and Sandy Napel. 2016. "A Rapid Segmentation-Insensitive “Digital Biopsy” Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non–Small Cell Lung Cancer" Tomography 2, no. 4: 283-294. https://doi.org/10.18383/j.tom.2016.00163

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