Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT.

Abstract

OBJECTIVE:

To test the feasibility of two-dimensional fast Fourier transforms (FFT)-based imaging metrics in differentiating solid, non-macroscopic fat containing, enhancing renal masses using contrast-enhanced CT images. We quantify image-based intratumoral textural variations (indicator of tumor heterogeneity) using frequency-based (FFT) imaging metrics.

METHODS:

In this Institutional Review Board approved, Health Insurance Portability and Accountability Act -compliant, retrospective case-control study, we evaluated 156 patients with predominantly solid, non-macroscopic fat containing, enhancing renal masses identified between June 2009 and June 2016. 110 cases (70%) were malignant RCC, including clear cell, papillary and chromophobe subtypes and, 46 cases (30%) were benign renal masses: oncocytoma and lipid-poor angiomyolipoma. Whole lesions were manually segmented using Synapse 3D (Fujifilm, CT) and co-registered from the multiphase CT acquisitions for each tumor. Pathological diagnosis of all tumors was obtained following surgical resection. Matlab function, FFT2 was used to perform the image to frequency transformation.

RESULTS:

A Wilcoxon rank sum test showed that FFT-based metrics were significantly (p < 0.005) different between 1. benign vs malignant renal masses, 2. oncocytoma vs clear cell renal cell carcinoma and 3. oncocytoma vs lipid-poor angiomyolipoma. Receiver operator characteristics analysis revealed reasonable discrimination (area under the curve >0.7, p < 0.05) within these three groups of comparisons.

CONCLUSION:

In combination with other metrics, FFT-metrics may improve patient management and potentially help differentiate other renal tumors. Advances in knowledge: We report for the first time that FFT-based metrics can differentiate between some solid, non-macroscopic fat containing, enhancing renal masses using their contrast-enhanced CT data.

• Authors: Bino A Varghese, Frank Chen, Darryl H Hwang, Steven Y Cen, Inderbir S Gill, Vinay A Duddalwar
• Journal: The British journal of radiology (Br J Radiol) Vol. 91 Issue 1089 Pg. 20170789 (Sep 2018) ISSN: 1748-880X [Electronic] England
• PMID: 29888982 (Publication Type: Journal Article)

Topics

• Adenoma, Oxyphilic (diagnostic imaging)
• Adipose Tissue (diagnostic imaging)
• Angiomyolipoma (diagnostic imaging)
• Case-Control Studies
• Diagnosis, Differential
• Feasibility Studies
• Fourier Analysis
• Humans
• Kidney Neoplasms (diagnostic imaging)
• Neoplasms, Adipose Tissue (diagnostic imaging)
• Retrospective Studies
• Statistics, Nonparametric
• Tomography, X-Ray Computed (methods)