Estimating the diagnostic yields resulting from renography and deconvolution parameters: a logistic regression analysis.
METHODS
Seventy patients with established diagnoses of normal, parenchymally insufficient or acutely obstructed kidneys were subjected to gamma camera renography. Deconvolution was then performed using three main techniques subdivided into six variants. Parameters from time-activity curves as well as retention curves were calculated. Logistic regression analysis was performed to assess the ability of renography and deconvolution methods to differentiate between kidney groups.


RESULTS
Discrimination between the groups was achieved by standard renography using six of 17 tested renogram parameters. Based on a set of six curve parameters, the correct classification rates ranged 86%-100%. Five of the six variants of the deconvolution technique used produced similar results. None, however, produced results which were as robust as those from renography. The sixth deconvolution method was consistently worse than the others.


CONCLUSIONS
Standard renography was consistently better than any of the deconvolution techniques used in the separation of the kidney groups. Conceptually, the results of a logistic regression analysis of renogram parameters may raise possibilities in the field of computer-aided diagnosis.
Kempi
V
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Department of Radiophysics, Sjukhuset, Ostersund, Sweden.
Sutton
D G
DG
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007668:Kidney / Q000000981:diagnostic imaging; D016018:Least-Squares Analysis; D008297:Male; D008875:Middle Aged; D011866:Radioisotope Renography; D012016:Reference Values; D012044:Regression Analysis; D051437:Renal Insufficiency / Q000000981:diagnostic imaging*; D014517:Ureteral Obstruction / Q000000981:diagnostic imaging*