| 摘要 |
The subject invention provides a method for diagnosing prostate cancer and determining preoperatively the pathological stage in patients with prostate cancer. The methods described herein can be used for prediction of margin positivity, seminal vesicle (S.V.) involvement, and lymph nodal (L.N.) involvement in patients with clinically localized prostate cancer. The method includes use of a neural network which provides prostate cancer stage information for a patient based upon input data which includes the patient's preoperative serum PSA, biopsy Gleason score, and systemic biopsy-based information. Its positive predictive value (PPV), negative predictive value (NPV), and accuracy are superior to that of current nomograms in use. Use of this method can result in enormous cost savings by accurately diagnosing patients with prostate cancer and by avoiding multiple imaging tests and expensive surgery in unindicated patients. |
