Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma.

Yang Lv,Xinquan Gu,Yuhan Zhang,Xu Li

doi:10.18383/j.tom.2020.00039

Abstract

The diagnosis of patients with suspected angiomyolipoma relies on the detection of abundant macroscopic intralesional fat, which is always of no use to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma and diagnosis of fp-AML excessively depends on individual experience. Texture analysis was proven to be a potentially useful biomarker for distinguishing between benign and malignant tumors because of its capability of providing objective and quantitative assessment of lesions by analyzing features that are not visible to the human eye. This review aimed to summarize the literature on the use of texture analysis to diagnose patients with fat-poor angiomyolipoma vs those with renal cell carcinoma and to evaluate its current application, limitations, and future challenges in order to avoid unnecessary surgical resection.

Highlights

Renal angiomyolipoma (AML) containing smooth muscle cells, dysmorphic blood vessels, and adipose tissues [1, 2], is the most common benign solid renal tumor observed in clinical practice [3], most of which can be diagnosed by means of conventional computed tomography (CT) and magnetic resonance imaging (MRI) that can detect abundant macroscopic intralesional fat [4,5,6]
Hodgdon et al conducted research on unenhanced CT images and claimed that renal cell carcinoma (RCC) can be characterized by a higher level of entropy than fat-poor angiomyolipoma (fp-AML) (P .01) [5]
Previous studies suggested that higher lesion entropy was a strong predictor of RCC, and greater entropy was consistently observed in RCC compared with fp-AML

Summary

Introduction

Renal angiomyolipoma (AML) containing smooth muscle cells, dysmorphic blood vessels, and adipose tissues [1, 2], is the most common benign solid renal tumor observed in clinical practice [3], most of which can be diagnosed by means of conventional computed tomography (CT) and magnetic resonance imaging (MRI) that can detect abundant macroscopic intralesional fat [4,5,6]. A variety of methods were proposed to differentiate fp-AML from RCC, such as angular interface, high attenuation of lesions at unenhanced CT, and strongly prolonged enhancement [12,13,14]. These imaging findings showed insufficient specificity, inconsistent reproducibility, or inadequate prospective reliability [3, 15]. The differentiation between benign and malignant tumors is of essential importance for the decision of proper treatment, but the diagnosis of fp-AML is challenging, time-consuming, and dependent much on the experience of individual radiologists

Objectives

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Discussion

Conclusion