Comparison of logistic regression and neural net modeling for prediction of prostate cancer pathologic stage.

@article{Veltri2002ComparisonOL,
  title={Comparison of logistic regression and neural net modeling for prediction of prostate cancer pathologic stage.},
  author={Robert Veltri and Manisha H Chaudhari and Matthew C Miller and Edward C Poole and Gerard J O'dowd and Alan W. Partin},
  journal={Clinical chemistry},
  year={2002},
  volume={48 10},
  pages={1828-34}
}
BACKGROUND Prostate cancer (PCa) pathologic staging remains a challenge for the physician using individual pretreatment variables. We have previously reported that UroScore, a logistic regression (LR)-derived algorithm, can correctly predict organ-confined (OC) disease state with >90% accuracy. This study compares statistical and neural network (NN) approaches to predict PCa stage. METHODS A subset (756 of 817) of radical prostatectomy patients was assessed: 434 with OC disease, 173 with… CONTINUE READING