D Hamilton1, U J Miola, D Mousa. 1. Department of Medical Physics, Armed Forces Hospital, Riyadh, Kingdom of Saudi Arabia.
Abstract
UNLABELLED: Severe renal artery stenosis (RAS) is a relatively uncommon complication after renal transplantation but is a curable cause of hypertension, which demands reliable early diagnosis to reduce morbidity, mortality and graft loss. Captopril renography has been used for a number of years as a method of detecting RAS but controversy still exists as to the diagnostic accuracy of this test and as to the most appropriate interpretation criteria with which to establish a positive result. METHODS: This report presents the results of using artificial neural networks to impartially assess these interpretation criteria. Data comprised 31 99mTc-MAG3 captopril renography investigations undertaken on hypertensive renal transplant patients with a suspected diagnosis of RAS. Each renogram study was correlated with an arteriogram as the "gold standard". Training of the network was performed using the round-robin technique. RESULTS: An accuracy of 95% could be achieved by considering perfusion index, time-to-peak activity, accumulation index and excretion index for both pre- and post-challenge studies. This varied as the parameters were either included or excluded. CONCLUSION: Artificial neural network analysis is a useful technique to evaluate the most appropriate criteria for interpreting captopril transplant renography investigations.
UNLABELLED: Severe renal artery stenosis (RAS) is a relatively uncommon complication after renal transplantation but is a curable cause of hypertension, which demands reliable early diagnosis to reduce morbidity, mortality and graft loss. Captopril renography has been used for a number of years as a method of detecting RAS but controversy still exists as to the diagnostic accuracy of this test and as to the most appropriate interpretation criteria with which to establish a positive result. METHODS: This report presents the results of using artificial neural networks to impartially assess these interpretation criteria. Data comprised 31 99mTc-MAG3 captopril renography investigations undertaken on hypertensive renal transplantpatients with a suspected diagnosis of RAS. Each renogram study was correlated with an arteriogram as the "gold standard". Training of the network was performed using the round-robin technique. RESULTS: An accuracy of 95% could be achieved by considering perfusion index, time-to-peak activity, accumulation index and excretion index for both pre- and post-challenge studies. This varied as the parameters were either included or excluded. CONCLUSION: Artificial neural network analysis is a useful technique to evaluate the most appropriate criteria for interpreting captopril transplant renography investigations.