Retrospective Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jun 26, 2020; 8(12): 2502-2509
Published online Jun 26, 2020. doi: 10.12998/wjcc.v8.i12.2502
Magnetic resonance imaging features of minimal-fat angiomyolipoma and causes of preoperative misdiagnosis
Xiao-Long Li, Li-Xin Shi, Qi-Cong Du, Wei Wang, Li-Wei Shao, Ying-Wei Wang
Xiao-Long Li, Qi-Cong Du, Wei Wang, Li-Wei Shao, Ying-Wei Wang, Department of Radiology, First Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100853, China
Li-Xin Shi, Department of Urology Surgery, First Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100853, China
Author contributions: Li XL and Shi LX contributed equally to this article and should be considered as co-first authors; Li XL and Du QC performed the operation; Li XL and Shi LX designed this retrospective study; Du QC and Wang W wrote this paper; Wang W and Shao LW were responsible for sorting the data.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Chinese People’s Liberation Army General Hospital.
Informed consent statement: Informed consent was obtained from the patients.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Ying-Wei Wang, PhD, Attending Doctor, Department of Radiology, First Medical Center of Chinese People’s Liberation Army General Hospital, 28 Fuxing Road, Beijing 100853, China. wangyw301@163.com
Received: February 24, 2020
Peer-review started: February 24, 2020
First decision: March 27, 2020
Revised: April 10, 2020
Accepted: May 19, 2020
Article in press: May 19, 2020
Published online: June 26, 2020
ARTICLE HIGHLIGHTS
Research background

Minimal-fat angiomyolipoma (mf-AML) is often misdiagnosed as renal cell carcinoma before operation, which leads to unnecessary operation. Improving the rate of preoperative diagnosis is helpful to reduce unnecessary surgical treatment.

Research motivation

The magnetic resonance imaging (MRI) features of mf-AML are different from those of typical AML. Summarizing and analyzing the imaging features of mf-AML are helpful to improve the understanding of the disease and avoid unnecessary surgery.

Research objectives

To summarize the MRI features of mf-AML in order to improve the rate of preoperative diagnosis of mf-AML.

Research methods

The MRI features of mf-AML confirmed by operation and pathology were retrospectively analyzed, including morphological features, lipids, capsule, washout and so on. These lesions were diagnosed as renal cell carcinoma before operation or could not be diagnosed clearly.

Research results

A retrospective analysis of the results of ten cases of AML revealed a circular-like mass in 4/10 (40%) patients, an oval mass in 6/10 (60%), a mass with a capsule in 9/10 (90%), and a mass with a lipid component in 7/10 (70%). But it still needs studies with a larger sample size to prove it.

Research conclusions

An oval morphological characteristic is strong evidence for the diagnosis of mf-AML, while a capsule and lipids are atypical manifestations of mf-AML.

Research perspectives

The imaging features of mf-AML are not typical, morphological features are very important for the diagnosis of renal tumors, and lipids and capsules can also be MRI findings of mf-AML. Some imaging features of mf-AML overlap with renal cell carcinoma, so it is necessary to comprehensively analyze its imaging features to improve the rate of preoperative diagnosis.