21 August 2017 Noninvasive radiomics signature based on quantitative analysis of computed tomography images as a surrogate for microvascular invasion in hepatocellular carcinoma: a pilot study
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J. of Medical Imaging, 4(4), 041303 (2017). doi:10.1117/1.JMI.4.4.041303
Abstract
We explore noninvasive biomarkers of microvascular invasion (mVI) in patients with hepatocellular carcinoma (HCC) using quantitative and semantic image features extracted from contrast-enhanced, triphasic computed tomography (CT). Under institutional review board approval, we selected 28 treatment-naive HCC patients who underwent surgical resection. Four radiologists independently selected and delineated tumor margins on three axial CT images and extracted computational features capturing tumor shape, image intensities, and texture. We also computed two types of “delta features,” defined as the absolute difference and the ratio computed from all pairs of imaging phases for each feature. 717 arterial, portal-venous, delayed single-phase, and delta-phase features were robust against interreader variability (concordance correlation≥0.8). An enhanced cross-validation analysis showed that combining robust single-phase and delta features in the arterial and venous phases identified mVI (AUC 0.76±0.18). Compared to a previously reported semantic feature signature (AUC 0.47 to 0.58), these features in our cohort showed only slight to moderate agreement (Cohen’s kappa range: 0.03 to 0.59). Though preliminary, quantitative analysis of image features in arterial and venous phases may be potential surrogate biomarkers for mVI in HCC. Further study in a larger cohort is warranted.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Shaimaa H. Bakr, Sebastian Echegaray, Rajesh P. Shah, Aya Kamaya, John Louie, Sandy Napel, Nishita Kothary, Olivier Gevaert, "Noninvasive radiomics signature based on quantitative analysis of computed tomography images as a surrogate for microvascular invasion in hepatocellular carcinoma: a pilot study," Journal of Medical Imaging 4(4), 041303 (21 August 2017). http://dx.doi.org/10.1117/1.JMI.4.4.041303 Submission: Received 27 February 2017; Accepted 25 May 2017
Submission: Received 27 February 2017; Accepted 25 May 2017
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KEYWORDS
Tumors

Computed tomography

Quantitative analysis

Feature extraction

Statistical modeling

Image segmentation

Arteries

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