Evidence map›Paper›PMID 29888982›Full record

ArticleThe British journal of radiology2018

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

Bino A Varghese, Frank Chen, Darryl H Hwang, Steven Y Cen, Inderbir S Gill, Vinay A Duddalwar

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Article in The British journal of radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Reliability of CT-based texture features: Phantom study.Journal of applied clinical medical physics · 2019
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  5. Article
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Bino A Varghese1 Department of Radiology, University of Southern California , Los Angeles, CA , USA.
Frank Chen1 Department of Radiology, University of Southern California , Los Angeles, CA , USA.
Darryl H Hwang1 Department of Radiology, University of Southern California , Los Angeles, CA , USA.
Steven Y Cen1 Department of Radiology, University of Southern California , Los Angeles, CA , USA.
Inderbir S Gill2 Institute of Urology, University of Southern California , Los Angeles, CA , USA.
Vinay A Duddalwar1 Department of Radiology, University of Southern California , Los Angeles, CA , USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo 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.

methodsIn 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.

resultsA 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.

conclusionIn 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.

Indexed as

Fourier AnalysisTomography, X-Ray ComputedAdenoma, OxyphilicAdipose TissueAngiomyolipomaCase-Control StudiesDiagnosis, DifferentialFeasibility StudiesHumansKidney NeoplasmsNeoplasms, Adipose TissueRetrospective StudiesStatistics, Nonparametric

Identifiers

PMID29888982
PMCPMC6223167

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.