ABSTRACT
OBJECTIVE: The purpose of this study was to facilitate interpretation of (99m)Tc-mercaptoacetyltriglycine (MAG3) diuretic scans by identifying key interpretative variables and developing a predictive model for computer-assisted diagnosis. MATERIALS AND METHODS: Ninety-seven studies were randomly selected from an archived database of MAG3 baseline and furosemide acquisitions and scan interpretations (obstruction, equivocal finding, or no obstruction) derived from a consensus of three experts. Sixty-one studies (120 kidneys) were randomly chosen to build a predictive model for diagnosing or excluding obstruction. The other 36 studies (71 kidneys) composed the validation group. The probability of normal drainage (no obstruction) at the baseline acquisition and the probability of no obstruction, equivocal finding, or obstruction after furosemide administration were determined by logistic regression analysis and proportional odds modeling of MAG3 renographic data. RESULTS: The single most important baseline variable for excluding obstruction was the ratio of postvoid counts to maximum counts. Renal counts in the last minute of furosemide acquisition divided by the maximum baseline acquisition renal counts and time to half-maximum counts after furosemide administration in a pelvic region of interest were the critical variables for determining obstruction. The area under the receiver operating characteristic curve (AUC) for predicting normal drainage in the validation sample was 0.93 (standard error, 0.02); sensitivity, 85%; specificity, 93%. The AUC for the diagnosis of obstruction after furosemide administration was 0.84 (standard error, 0.06); sensitivity, 82%; specificity, 83%. CONCLUSION: A predictive system has been developed that provides a promising computer-assisted diagnosis approach to the interpretation of MAG3 diuretic renal scans; this system has also identified the key variables required for scan interpretation.
