Evaluation of a computerized Bayesian model for diagnosis of renal cyst vs. tumor vs. normal variant from urogram information.

The diagnostic problem of cyst/tumor/normal variant raised on an excretory urogram leads to a decision to do needle aspiration or renal arteriography. This decision depends critically upon the probability distribution for the three diagnoses. A computerized Bayesian model of a uroradiologist's diagnostic process in solving the problem was developed. The model was based on subjective probabilities supplied by an experienced uroradiologist. The model was evaluated in terms of its ability to decrease the cost of further diagnosis regarding aspiration versus arteriography. The model's output was compared with decisions made by unaided radiologists viewing the same panel of 50 urogram test cases. Results indicate that the model does not improve upon the decisions made by a radiologist highly experienced with this diagnostic problem. However, the decisions made by unaided, less experienced radiologists result in greater cost than those of the model.