Are There Useful CT Features to Differentiate Renal Cell Carcinoma From Lipid-Poor Renal Angiomyolipoma?

Yang, Chin‐Wei; Shen, Shu-Huei; Chang, Yen‐Hwa; Chung, Hsiao-Jen; Wang, Jia-Hwia; Lin, Alex Tong‐Long; Chen, Kuang-Kuo

doi:10.2214/ajr.12.10204

Cited by 109 publications

(81 citation statements)

References 34 publications

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“…The fp-AMLs were found to have a significantly greater rate of unenhanced attenuation of >38.5 HU and enhanced homogeneity. 17 Still other findings have been associated with fp-AML. Renal masses, which have been described as having either an angular interface with the adjacent parenchyma or having a shape resembling an ''icecream cone'' or ''mushroom, '' 17,23 are more likely to be fp-AML (Fig.…”

Section: Discussionmentioning

confidence: 99%

Computed Tomography and Magnetic Resonance Findings of Fat-Poor Angiomyolipomas

Potretzke

Bauman

et al. 2017

Journal of Endourology

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"Computed tomography and magnetic resonance findings of fat-poor angiomyolipomas." Journal of Endourology.31,2. 119-128. (2017 Results: A total of 26 fp-AMLs were identified. Thirteen lesions had available unenhanced CT images, of which eight (62%) were hyperdense compared to the adjacent renal parenchyma, while five (38%) were isodense. Twenty lesions had enhanced CT images: 14 (70%) and 6 (30%) with homogeneous and heterogeneous enhancement, respectively. Of the nine lesions with enhanced MR sequences, five (56%) were homogeneously enhancing, and four (44%) were heterogeneously enhancing. Eight of nine (89%) lesions had hypointense signal intensity (SI) on T2-weighted MR sequences, while one (11%) had hyperintense SI. None of the eight lesions displayed a decrease in signal on fat-suppressed sequences. Conclusions: In this study, we confirmed common imaging features of fp-AML: high attenuation on unenhanced CT sequences, homogeneous enhancement on CT, and hypointensity on T2-weighted MR. When these features are present, a renal mass biopsy may be prudent.

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Section: Discussionmentioning

confidence: 99%

Computed Tomography and Magnetic Resonance Findings of Fat-Poor Angiomyolipomas

Potretzke

Bauman

et al. 2017

Journal of Endourology

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“…However, no fat can be visualized on CT in approximately 4.5% of AMLs. These tumors are referred to as lp-AMLs [25] . Our data showed that it is very difficult even for an experienced radiologist to diagnose lp-AMLs accurately.…”

Section: Discussionmentioning

confidence: 99%

Does Computed Tomography Still Have Limitations to Distinguish Benign from Malignant Renal Tumors for Radiologists?

Shin¹,

Duddalwar

Ukimura³

et al. 2017

Urol Int

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Objectives: To evaluate the current accuracy of CT for diagnosing benign renal tumors. Materials and Methods: We retrospectively reviewed 905 patients who underwent preoperative CT followed by surgical resection. The final pathology was benign in 156 patients (17%). After exclusions, 140 patients with 163 benign tumors were included and 3 sets of the CT interpretations by radiologists with varying levels of experience were analyzed. Results: The histological breakdown was as follows: oncocytomas (54.6%), angiomyolipomas (AMLs; 30.7%), renal cysts (8.0%), other miscellaneous benign tumors (6.7%). The sensitivities of diagnosing oncocytomas were 3.4, 9.0, and 13.5% in primary radiological reports, second blinded reviews, and third non-blinded reviews, respectively (p = 0.055). The sensitivities of diagnosing AMLs were 46.0, 58.0, and 62.0% in the 3-sets of CT interpretations, respectively (p = 0.246). As for renal cysts, the sensitivities were 69.2, 92.3, and 100% in the 3-sets of CT interpretations, respectively (p = 0.051). In primary reports, the positive predictive values were 95.8% in lipid poor (lp)-AMLs, 60.0% in oncocytomas, 69.2% in renal cysts, respectively (p < 0.05). Conclusions: Current conventional CT imaging still has limitations in differentiating oncocytomas and lp-AMLs from renal cell carcinomas, even when images were re-examined by experienced radiologists.

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“…These results imply that tumor attenuation on unenhanced scans may vary, reason why high tumor attenuation on unenhanced scans should not be used as a definitive finding for AML with minimal fat. Yang et al categorized lipid-poor AML based on homogeneity with a homogeneous enhancement pattern for fat poor AML and a heterogeneous enhancement pattern for clear-cell RCC [3] nevertheless the majority of chromophobe RCC and papillary RCC tumors revealed a homogeneous pattern [2,6] . Calcification was also another criteria used to differentiate fat poor AML from RCC in non-contrast CT as intra-tumoral calcification is a common finding in RCCs and may be seen in about 30% of cases [11] , whereas it is rare in AML [5,24] .…”

Section: Iso-attenuating Aml:-mentioning

confidence: 99%

“…AML characteristically expresses melanocytic markers including HMB-45 and Melan-A. The availability of melanocytic markers to diagnose AML produced more precise categorization of tumors of exceptional histology that previously may have been miscategorized [3] . Monophasic AMLs contrast histologically from triphasic AMLs in that they are made of predominantly or completely of 1 component some of which are smooth muscle or fat.…”

Section: …………………………………………………………………………………………………… Introduction:-mentioning

confidence: 99%

Differentiation of Renal Aml (Angiomyolipoma) With Minimal Fat From RCC (Renal Cell Carcinoma) by Analysis on Ct Images

Champion¹,

Deng²,

Limbu³

et al. 2018

IJAR

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Are There Useful CT Features to Differentiate Renal Cell Carcinoma From Lipid-Poor Renal Angiomyolipoma?

Abstract: Specific CT features can potentially be used to differentiate lipid-poor renal angiomyolipoma from renal cell carcinoma.

Cited by 109 publications

References 34 publications

Computed Tomography and Magnetic Resonance Findings of Fat-Poor Angiomyolipomas

Computed Tomography and Magnetic Resonance Findings of Fat-Poor Angiomyolipomas

Does Computed Tomography Still Have Limitations to Distinguish Benign from Malignant Renal Tumors for Radiologists?

Differentiation of Renal Aml (Angiomyolipoma) With Minimal Fat From RCC (Renal Cell Carcinoma) by Analysis on Ct Images

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