[Problems of the preoperative prediction of the pathological stage in prostate cancer].
The pathological stage of the tumor is the most influential prognostic factor for progression after radical prostatectomy. However, as many as 50% of men undergoing radical prostatectomy are found to have extraprostatic disease in the pathological specimen. Accurate identification of the risks of disease extension and of disease recurrence prior to radical prostatectomy would thus be useful in counseling men presenting with clinically localized prostate cancer. Nomograms may help patients and physicians make more informed treatment decisions based on the probability of pathological stage. Partin and co-workers popularized the use of a pretreatment nomogram based on PSA (prostate specific antigen), clinical stage (TNM stage) and biopsy Gleason score to predict the pathological stage of localized prostate cancer. However, it may not be directly applicable to Japanese males, and the interpretation and comparison of data sets should be done with caution and careful consideration. Although attempts have been made to establish a nomogram for Japanese patients, been tried, it is still based on the data for a small number of patients. More data from a greater number of patients and validation analysis are essential. Recently, artificial neural networks (ANN) have been shown to be effective in predicting pathologic stage in men with clinically localized prostate cancer. The use of ANNs is a relatively new concept and the data is based on Western people; thus, the data analysis for Japanese patients is necessary. The present paper mainly outlines the usefulness and problems for the preoperative prediction of the pathological stage in prostate cancer by nomograms and artificial neural networks.
Konomoto
Tatsuo
T
Dept. of Urology, Graduate School of Medical Sciences, Kyushu University.
Naito
Seiji
S
D014408:Biomarkers, Tumor / Q000097:blood; D006801:Humans; D008207:Lymphatic Metastasis; D008297:Male; D009367:Neoplasm Staging / Q000379:methods*; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D017430:Prostate-Specific Antigen / Q000097:blood; D011471:Prostatic Neoplasms / Q000473:pathology*
D014408:Biomarkers, Tumor; D017430:Prostate-Specific Antigen