Category: Imaging & Image Guided Therapy: New Therapies

MP4-14 - Using Aorta-Lesion-Attenuation-Difference (ALAD) on Preoperative Contrast-Enhanced CT Scan to Differentiate Between Malignant and Benign Renal Tumors

Fri, Sep 21
10:00 AM - 12:00 PM

Introduction & Objective : Small renal masses (<4cm) are benign 20% of the time, and pre-treatment biopsy cannot always reliably differentiate benign pathology from certain types of malignany renal cell carcinomas (RCC).  Aorta-Lesion-Attenuation-Difference (ALAD) measurement is a technique which has been shown to have the ability to discriminate between these benign and malignant histologic subtypes.  Our objective was to evaluate the ability of ALAD to differentiate malignant renal tumors from renal oncocytomas. 


Methods : A retrospective review of preoperative CT scans and surgical pathology from 218 robotic assisted partial nephrectomy specimens obtained by a single surgeon was performed.  ALAD was calculated by measuring the difference in Hounsfield units (HU) between the aorta and the lesion of interest on the same image slice in the nephrographic phase 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 ROC analysis. 


Results : A total of 218 preoperative CT scans and corresponding pathology reports were reviewed.  Pathology review revealed 22 oncocytomas (10.1%), 11 chromophobe RCC (5%), 37 papillary RCC (17%), and 148 clear cell RCC (67.9%).  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 versus oncocytoma was 0.86 (95% CI 0.77−0.96).  Subgroup analysis showed that ALAD was able to differentiate chromophobe RCC from oncocytoma using a HU threshold of 24 with a sensitivity of 100%, specificity of 86%, PPV of 75%, and a NPV of 100%.  The AUC for chromophobe RCC versus oncocytoma was 0.98 (95% CI 0.91−1.00). 


Conclusions : ALAD measurement based upon preoperative CT scans provides good discrimination between chromophobe RCC and oncocytoma, and an ALAD of ≤24 predicted non-chromophobe RCC with 100% probability. This information may aid in the management of patients with indeterminate diagnoses of oncocytic neoplasm on biopsy. 
ALAD also discriminates well between oncocytoma and malignant RCC in general, and an ALAD of >24 predicted malignant RCC histology with 98% probability. 
Further validation of ALAD will be necessary prior to routine use in clinical practice. 

Li-Ming Su

David A. Cofrin Professor of Urologic Oncology; Chairman, Department of Urology
University of Florida College of Medicine, Department of Urology
Gainesville, Florida

Dr. Su is the David A. Cofrin Professor of Urologic Oncology and Chairman of the Department of Urology at the University of Florida College of Medicine. He completed his urology residency at the New York Presbyterian Hospital-Weil Cornell Medical College in 2000 and a fellowship in robotics and laparoscopic surgery in 2001 at Johns Hopkins Hospital. He served eight years on faculty at the James Buchanan Brady Urological Institute at Johns Hopkins before coming to the University of Florida in 2008 as the Chief of the Division of Robotic and Minimally Invasive Urologic Surgery. His clinical interests are in minimally invasive surgical therapies including robotic surgery for prostate and kidney cancer. Dr. Su’s research focuses on exploring image-guided surgery for prostate and kidney cancer as well as optical imaging for renal tumors, robotic simulation and virtual reality. He has authored over 90 peer reviewed manuscripts and multiple book chapters including chapters in both Campbell’s Urology and Smith’s Textbook of Endourology. He is the editor of two editions of the textbook Atlas of Robotic Urologic Surgery, a surgical atlas that provides step-by-step schematic and video-based instructions and is designed to enhance the learning of novice robotic urologic surgeons. Dr. Su has been a member of the American Board of Urology Written Examination Committee and has received national awards including Newsweek’s Top Cancer Doctors (2015) and Castle Connelly’s America’s Top Doctors (2011-2018). Lastly, he has received first place audio-visual and manuscript awards for topics of laparoscopic prostate and kidney surgery.

Joseph Grajo

Assistant Professor of Radiology
University of Florida College of Medicine, Department of Radiology
Gainesville, Florida

Russell S. Terry

Endourology Fellow
Duke University Medical Center, Division of Urology
Durham, North Carolina

Fellow in Endourology, Metabolic Stone Disease, Laparoscopy, and Robotic Surgery
Division of Urologic Surgery
Duke University Medical Center

Justin Ruoss

Radiology Resident
University of Florida College of Medicine, Department of Radiology
Gainesville, Florida

Blake Noennig

Urology Resident
University of Florida College of Medicine, Department of Urology
Gainesville, Florida

Jonathan Pavlinec

Urology Resident
University of Florida College of Medicine, Department of Urology
Gainesville, Florida

Shahab Bozorgmehri

University of Florida College of Medicine, Department of Epidemiology
Gainesville, Florida

Paul Crispen

Associate Professor of Radiology
University of Florida College of Medicine, Department of Urology
Gainesville, Florida