Abstract
Objective
To evaluate the ability of Aorta-Lesion-Attenuation Difference (ALAD) to differentiate
malignant renal tumors from renal oncocytomas.
Methods
A retrospective review of preoperative computed tomography (CT) scans and surgical
pathology was performed on patients undergoing partial nephrectomy for small, solid
renal masses. ALAD was calculated by measuring the difference in Hounsfield units
(HU) between the aorta and the lesion of interest on the same image slice on preoperative
CT scan. The discriminative ability of ALAD to differentiate malignant pathology from
oncocytoma was evaluated by sensitivity, specificity, positive predictive value (PPV),
negative predictive value (NPV), and area under curve (AUC) using receiver operating
characteristic analysis.
Results
A total of 227 preoperative CT scans and corresponding pathology reports were reviewed.
ALAD values were calculated during the excretory and nephrographic phases. Nephrographic
ALAD was able to differentiate malignant pathology from oncocytoma using a HU threshold
of 24 with a sensitivity of 84%, specificity of 86%, PPV of 98%, and NPV of 33%. The
AUC for malignant pathology vs oncocytoma was 0.86 (95% confidence intervals 0.77-0.96).
Nephrographic ALAD was able to differentiate chromophobe renal cell carcinoma (RCC)
from oncocytoma using a HU threshold of 24 with a sensitivity of 100%, specificity
of 86%, PPV of 75%, and NPV of 100%. The AUC for chromophobe RCC vs oncocytoma was
0.98 (95% confidence intervals 0.91-1.00).
Conclusion
ALAD discriminates well between chromophobe RCC and oncocytoma, which may aid in the
management of patients with indeterminate diagnoses of oncocytic neoplasm on diagnostic
needle biopsy. Further validation of ALAD will be necessary prior to routine use in
clinical practice.
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Article info
Publication history
Published online: December 12, 2018
Accepted:
November 30,
2018
Received:
October 9,
2018
Footnotes
Financial Disclosure: The authors declare that they have no relevant financial interests.
Identification
Copyright
© 2018 Elsevier Inc. All rights reserved.
