CT imaging findings of renal epithelioid lipid-poor angiomyolipoma

被引:4
作者
Wang, Di [1 ,2 ]
Gong, Guanghui [3 ]
Fu, Yan [1 ,2 ]
Zhu, Liping [1 ,2 ]
Yin, Hongling [3 ]
Liu, Longfei [4 ]
Zhu, Zhiming [1 ]
Zhou, Gaofeng [1 ]
Yan, Ang [1 ,5 ]
Lei, Guangwu [1 ]
Chen, Changyong [1 ]
Pang, Peipei [6 ]
Yi, Xiaoping [1 ,2 ,7 ]
Kuang, Yehong [7 ,8 ,9 ,10 ]
Chen, Bihong T. [11 ]
机构
[1] Cent South Univ, Xiangya Hosp, Dept Radiol, Changsha 410008, Hunan, Peoples R China
[2] Cent South Univ, Natl Clin Res Ctr Geriatr Disorders, Xiangya Hosp, Changsha 410008, Hunan, Peoples R China
[3] Cent South Univ, Xiangya Hosp, Dept Pathol, Changsha 410008, Hunan, Peoples R China
[4] Cent South Univ, Xiangya Hosp, Dept Urol, Changsha 410008, Hunan, Peoples R China
[5] Cent South Univ, Xiangya Hosp, Dept Med Equipment, Changsha 410008, Hunan, Peoples R China
[6] GE Healthcare, Hangzhou 310000, Peoples R China
[7] Hunan Key Lab Skin Canc & Psoriasis, Changsha, Hunan, Peoples R China
[8] Natl Clin Res Ctr Geriatr Disorders, Changsha, Hunan, Peoples R China
[9] Hunan Engn Res Ctr Skin Hlth & Dis, Changsha, Hunan, Peoples R China
[10] Cent South Univ, Xiangya Hosp, Dept Dermatol, Changsha 410008, Hunan, Peoples R China
[11] City Hope Natl Med Ctr, Dept Diagnost Radiol, 1500 E Duarte Rd, Duarte, CA 91010 USA
基金
中国国家自然科学基金;
关键词
Angiomyolipoma; Computed tomography; Diagnosis; Pathology; COLLECTING SYSTEM; TEXTURE ANALYSIS; MASSES; DIFFERENTIATION; PARAMETERS; CARCINOMA; BENIGN; CANCER; FAT;
D O I
10.1007/s00330-021-08528-y
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Objectives To identify specific imaging and clinicopathological features of a rare potentially malignant epithelioid variant of renal lipid-poor angiomyolipoma (E-lpAML). Methods A total of 20 patients with E-lpAML and 43 patients with other lpAML were retrospectively included. Multiphase computed tomography (CT) imaging features and clinicopathological findings were recorded. Independent predictors for E-lpAML were identified using multivariate logistic regression and were used to construct a diagnostic score for differentiation of E-lpAML from other lpAML. Results The E-lpAML group consisted of 6 men and 14 women (age median +/- SD: 39.45 +/- 15.70, range: 16.0-68.0 years). E-lpAML tended to appear as hyperdense mass lesions located at the renal sinus (n = 8, 40%) or at the renal cortex (n = 12, 60%), with a "fast-in and slow-out" enhancement pattern (n = 20, 100%), cystic degeneration (n = 18, 90%), "eyeball" sign (n = 11, 55%), and tumor neo-vasculature (n = 15, 75%) on CT. Multivariate logistic regression analysis showed that the independent predictors for diagnosing E-lpAML were cystic degeneration on CT imaging and CT value of the tumor in corticomedullary phase of enhancement. A predictive model was built with the two predictors, achieving an area under the curve (AUC) of 93.5% (95% confidence interval (95%CI): 84.3-98.2%) with a sensitivity of 95.0% (95%CI: 75.1-99.9%) and a specificity of 83.72% (95%CI: 69.3-93.2%). Conclusion We identified specific CT imaging features and predictors that could contribute to the correct diagnosis of E-lpAML. Our findings should be helpful for clinical management of E-lpAML which could potentially be malignant and may require nephron-sparing surgery while other lpAML tumors which are benign require no intervention.
引用
收藏
页码:4919 / 4930
页数:12
相关论文
共 32 条
[1]   Depression in cancer: The many biobehavioral pathways driving tumor progression [J].
Bortolato, Beatrice ;
Hyphantis, Thomas N. ;
Valpione, Sara ;
Perini, Giulia ;
Maes, Michael ;
Morris, Gerwyn ;
Kubera, Marta ;
Kohler, Cristiano A. ;
Fernandes, Brisa S. ;
Stubbs, Brendon ;
Pavlidis, Nicholas ;
Carvalho, Andre F. .
CANCER TREATMENT REVIEWS, 2017, 52 :58-70
[2]   Fat poor angiomyolipoma differentiation from renal cell carcinoma at 320-slice dynamic volume CT perfusion [J].
Chen, Chao ;
Kang, Qinqin ;
Xu, Bing ;
Shi, Zhang ;
Guo, Hairuo ;
Wei, Qiang ;
Lu, Yayun ;
Wu, Xinhuai .
ABDOMINAL RADIOLOGY, 2018, 43 (05) :1223-1230
[3]   Small Renal Masses in Close Proximity to the Collecting System and Renal Sinus Are Enriched for Malignancy and High Fuhrman Grade and Should Be Considered for Early Intervention [J].
Correa, Andres F. ;
Toussi, Amir ;
Amin, Milon ;
Hrebinko, Ronald L. ;
Gayed, Bishoy A. ;
Parwani, Anil, V ;
Maranchie, Jodi K. .
CLINICAL GENITOURINARY CANCER, 2018, 16 (04) :E729-E733
[4]   CD8+ cytotoxic T lymphocytes in cancer immunotherapy: A review [J].
Farhood, Bagher ;
Najafi, Masoud ;
Mortezaee, Keywan .
JOURNAL OF CELLULAR PHYSIOLOGY, 2019, 234 (06) :8509-8521
[5]   Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma [J].
Feng, Zhichao ;
Rong, Pengfei ;
Cao, Peng ;
Zhou, Qingyu ;
Zhu, Wenwei ;
Yan, Zhimin ;
Liu, Qianyun ;
Wang, Wei .
EUROPEAN RADIOLOGY, 2018, 28 (04) :1625-1633
[6]   Renal Epithelioid Angiomyolipoma: Imaging Characteristics in Nine Cases With Radiologic-Pathologic Correlation and Review of the Literature [J].
Froemming, Adam T. ;
Boland, Jennifer ;
Cheville, John ;
Takahashi, Naoki ;
Kawashima, Akira .
AMERICAN JOURNAL OF ROENTGENOLOGY, 2013, 200 (02) :W178-W186
[7]   Non-Small Cell Lung Cancer: Histopathologic Correlates for Texture Parameters at CT [J].
Ganeshan, Balaji ;
Goh, Vicky ;
Mandeville, Henry C. ;
Quan Sing Ng ;
Hoskin, Peter J. ;
Miles, Kenneth A. .
RADIOLOGY, 2013, 266 (01) :326-336
[8]   Tumor angiogenesis-related parameters in multi-phase enhanced CT correlated with outcomes of hepatocellular carcinoma patients after radical hepatectomy [J].
Gao, S. -Y. ;
Tang, L. ;
Cui, Y. ;
Li, X. -T. ;
Zhang, X. -Y. ;
Shan, J. ;
Sun, Y. -S. ;
Zhang, X. -P. .
EJSO, 2016, 42 (04) :538-544
[9]   Management of the Incidental Renal Mass on CT: A White Paper of the ACR Incidental Findings Committee [J].
Herts, Brian R. ;
Silverman, Stuart G. ;
Hindman, Nicole M. ;
Uzzo, Robert G. ;
Hartman, Robert P. ;
Israel, Gary M. ;
Baumgarten, Deborah A. ;
Berland, Lincoln L. ;
Pandharipande, Pari V. .
JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY, 2018, 15 (02) :264-273
[10]   Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? [J].
Hodgdon, Taryn ;
McInnes, Matthew D. F. ;
Schieda, Nicola ;
Flood, Trevor A. ;
Lamb, Leslie ;
Thornhill, Rebecca E. .
RADIOLOGY, 2015, 276 (03) :787-796