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Artificial neural network analysis for predicting pathological stage of clinically localized prostate cancer in the
Yoshiyuki Matsui1, Shin Egawa, Chotatsu Tsukayama
1Department of Urology, Kurashiki Central Hospital, Okayama, Japan. ym7856@kchnet.or.jp
Background:
Although prostate cancer has been prevalent in Japan, there has been no particular model for predicting the pathological stage in the Japanese population. We examined whether artificial neural network analysis (ANNA), which is a relatively new diagnostic tool in prostate cancer, can be one of the predictive methods for predicting organ confinement, compared with the traditional logistic regression model, in the Japanese population for the first time.
Methods:
The study population comprised 178 men who underwent radical prostatectomy at our institutions between October 1992 and May 1999. As additional pretreatment parameters to the preoperative serum PSA level, clinical TNM classification and biopsy Gleason score, the percentage of number of cores exhibiting traces of tumor, maximum tumor length in biopsy cores, PSA density and patient age were used. The predictive ability of ANNA with several parameters for a set of 36 randomly selected test data was compared with those of logistic regression analysis and 'Partin Tables' by area under the receiver operating characteristics (ROC) curve analysis.
Results:
Of 178 patients, 97 (54.5%) had organ-confined disease but 81 (45.5%) had locally advanced disease. With three parameters, the area under the ROC curve of ANNA (0.825 +/- 0.071) was larger than those for logistic regression (0.782 +/- 0.079) and Partin Tables (0.756 +/- 0.087), but not to a significant extent (P = 0.690 and 0.541). Although the expansion of the parameters did not increase the difference in area under the ROC curve between the best ANNA and logistic regression (0.899 +/- 0.053 and 0.873 +/- 0.065, respectively), the difference between the best ANNA and Partin Tables did not reach but approached statistical significance (P = 0.157).
Conclusion:
Although more modeling optimization is necessary to improve the predictive accuracy and generalizability of ANNA, we suggest that there is the possibility for this new predictive method to evolve in the analysis of clinical staging of prostate cancer.
