Japanese Journal of Clinical Oncology 32:530-535 (2002)
© 2002 Foundation for Promotion of Cancer Research
Artificial Neural Network Analysis for Predicting Pathological Stage of Clinically Localized Prostate Cancer in the Japanese Population
Departments of 1 Urology and 2 Pathology, Kurashiki Central Hospital, Kurashiki, Okayama, Departments of 3 Urology and 4 Pathology, Kitasato University School of Medicine, Sagamihara, Kanagawa and 5 Department of Urology, Tohoku University, School of Medicine, Sendai, Japan
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONCONCLUSIONAcknowledgmentREFERENCES |
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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.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONCONCLUSIONAcknowledgmentREFERENCES |
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Radical prostatectomy is the definitive treatment option in men with clinically localized prostate cancer. Unfortunately, however, 40–50% of men with presumably clinically localized prostate cancer harbor extraprostatic disease and are potentially not curable with surgery alone (1,2). Therefore, accurate staging has been the most important factor for selecting the most appropriate treatment modality for patients with prostate cancer.
Recently, several groups have reported the efficacy of the combined use of PSA, clinical stage and biopsy Gleason score for predicting disease outcome (1,3–7). Among these reports, the ‘Partin Tables’ constructed by Partin and co-workers is one of the most popularized nomograms obtained by the log-linear regression method (1). Such nomograms may also be applied in Japan as a standard tool for predicting the pathological stage before treatment. However, while the nomogram might be applicable to the patient population in which it was developed, extrapolation of the results to other populations may have limited usefulness or validity (8). The difference in the normal range of the serum PSA value and in practice patterns such as prevalence of PSA-based screening programs leading to differences in tumor volumes must affect the differences in overall pathological tumor stages among races and times (8–10).
As prostate cancer has been prevalent in Japan, the necessity for the establishment of a particular predictive method in the Japanese population has increased. Therefore, in this study we examined whether artificial neural network analysis, which is a relatively new diagnostic tool in prostate cancer, can be one of the useful predictive methods in the Japanese population for the first time.
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PATIENTS AND METHODS |
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TOPABSTRACTINTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONCONCLUSIONAcknowledgmentREFERENCES |
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Patient Population
The study population comprised 178 male patients who underwent radical retropubic prostatectomy and bilateral pelvic lymph node dissection without any preoperative hormonal ablation or chemotherapy at Kitasato University Hospital (n = 110) and Kurashiki Central Hospital (n = 68) between October 1992 and May 1999.
Pretreatment Staging and Radical Prostatectomy
Preoperatively all patients underwent serum PSA level examination by Dainapack IMx PSA or Dainapack AxSYM PSA (Dinabot, Tokyo, Japan) and transrectal ultrasound (TRUS)-guided systematic needle biopsy at four or more separate sites. The results of the PSA assays were not interconverted because they were considered virtually the same (11). PSA density was calculated as the serum PSA level divided by the prostate volume which was measured with TRUS using the prolate spheroid equation. All biopsy specimens were examined by a single pathologist. Tumor grade was determined according to the Gleason grading system. The percentage of cores showing tumor traces in all cores and the maximum tumor length in biopsy cores were also recorded. The clinical stage was assigned by two urologists in accordance with the unified tumor node metastasis (TNM) classification (12). Radical prostatectomy was conducted by the retropubic approach by these surgeons or was under their supervision.
Pathological Examination
The radical prostatectomy specimens were examined independently by two different pathologists (S.K. and C.T.) using the whole-organ step-section technique. Briefly, specimens were fixed in 10% formalin and sectioned at 5 mm intervals in a plane perpendicular to the long axis of the gland, from the prostatic apex to the tip of the seminal vesicles. Each section was stained with hematoxylin and eosin. The depth of capsular penetration by the tumor was determined on the basis of criteria specified previously (10). The disease was defined as organ-confined if the cancer was confined within the prostatic capsule.
Statistical Analysis
The non-parametric Mann–Whitney U-test was used between the organ-confined cancer and non-organ-confined cancer groups to compare the mean age, preoperative serum PSA level, PSA density, clinical TNM classification and biopsy Gleason score, the percentage of number of cores showing tumor traces and maximum tumor length in biopsy cores.
Several statistical analyses with several preoperative parameters were compared to determine the most effective method for predicting organ confinement.
Artificial Neural Network Analysis (ANNA)
The neural network used in this application was the Bayesian neural tool of SPSS (Statistical Package for the Social Sciences, SPSS, Chicago, IL) Neural Connection 2.1 software. The Bayesian neural tool was a modified multilayer perceptron (MLP). Multilayer perceptrons have standard feed-forward topology and successive layers of adaptive weights. They typically contain one or two hidden layers of neurons, which are not treated as output units. In the training data set, as training progressed, these weights and biases were modified and converged toward values representing a solution to the prediction problem. The weights were constantly updated to reflect this gradual convergence and to contribute further to the overall reduction in the root mean square error.
The disadvantage of MLP had been a tendency to be prone to overfitting without an extensive validation data set. However, the Bayesian neural tool could automatically decrease overfitting and produce a generalized model even in a limited data set.
Input variables included preoperative pathological and clinical parameters. In the first analysis (ANNA model 1), three input parameters, preoperative serum PSA level, clinical TNM classification and biopsy Gleason score, all of which were used in Partin Tables, were used. In the second analysis (ANNA model 2), the percentage of the number of cores showing tumor traces and maximum tumor length in biopsy cores were added to the variables. In the third analysis (ANNA model 3), PSA density was added to the previous five parameters. In the final analysis (ANNA model 4), patient’s age was further added to the previous six parameters. The output variable was organ confinement status.
In this application, 124 (70%) and 18 (10%) of the 178 patients were selected randomly for training and validation and the remaining 36 patients (20%) were used for testing of the predictive ability of each analysis.
Logistic Regression Analysis
Compared with ANNA, logistic regression analysis with the likelihood ratio test was performed in the training and validation data set of ANNA to define the correlation between each preoperative variable and organ confinement status of prostate cancer. In one logistic regression model (LR model 1), preoperative serum PSA level, clinical TNM classification and biopsy Gleason score were used as preoperative variables. In another model (LR model 2), the percentage of cores showing tumor traces and maximum tumor length in biopsy cores were added to the variables. PSA density was excluded from analysis because of a highly significant association with the serum PSA level. The Peason 
2 goodness-of-fit test was used to test the internal validity of these models. Logistic regression analysis gave a score (A) with A = a + b1 x 1 + b2 x 2 + ... as a linear combination of predictors (x1, x2, ...). Probability was determined as P = 1/(1 + e–A) The predictive ability was checked in the same test data in ANNA.
Logistic regression analysis gave equations to be generated for predicting organ confinement of prostate cancer with both three and five parameters (Fig. 1).
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Partin Tables
Partin Tables were used as one of the most popular ready-made nomograms for predicting pathological stage.
For the comparison of predictive ability, the area under receiver operating characteristic (ROC) curves was used. ROC curves, which chart test sensitivity versus specificity along a range of cutoff values, were constructed for output variables in testing the data set of each analysis. The area under the curve was calculated and compared as described by Hanley and McNeil (13,14).
The limit of significance for all tests was P < 0.05.
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RESULTS |
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TOPABSTRACTINTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONCONCLUSIONAcknowledgmentREFERENCES |
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Patients’ Characteristics and Outcome of Surgery
The median age of the 178 patients at radical prostatectomy was 65.0 years (range, 49–80 years; mean, 64.9 years ). The median preoperative serum PSA was 7.9 ng/ml (range, 1.2–136.0 ng/ml; mean, 13.0 ng/ml). The proportion of patients with PSA
4.0, 4.1–10.0, 10.1–20.0 and 
20.1 ng/ml were 14.0% (25 patients), 49.4 % (88 patients), 20.2% (36 patients) and 16.3% (29 patients), respectively. The 178 patients were classified clinically as stage T1c (92 patients), T2a (22 patients), T2b (35 patients), T2c (13 patients), T3a (three patients), T3b (three patients) and T3c (10 patients). Sextant biopsies were obtained in 54.5% (97/178) of the patients. The remaining patients had fewer than six (3.4%, 6/178) or more than six (up to 12) biopsies (42.1%, 75/178). Biopsy tumor grade was classified as Gleason score 2–4, 5 and 6 in 21 (11.8%), 24 (13.5%) and 33 (18.5%) patients, respectively. One hundred patients had poorly differentiated tumors, with Gleason scores of 7 in 62 patients (34.8%) and of 8–10 in 38 patients (21.3%). Ninety-seven patients (54.5%) had organ-confined disease but 81 patients (45.5%) had locally advanced disease on final pathological examination; extracapsular extension was seen in 75 patients (42.1%), seminal vesicle involvement in 35 (19.7%) and microscopic nodal involvement in 11 (6.2%). The distribution of preoperative parameters between organ-confined cancer (OCD) and non-organ-confined cancer (non-OCD) patients are shown in Table 1.
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The Predictability of Each Analysis
The area under the ROC curves and the comparison with area of the Partin Tables are summarized in Table 2. With three parameters, the area under the ROC curve of ANNA model 1 (0.825 ± 0.071) was larger than those of LR model 1 (0.782 ± 0.079) and Partin Tables (0.756 ± 0.087) but it was not significant (P = 0.690 and 0.541, Fig. 2A). With an additional two and three parameters, the area under the ROC of ANNA model 2 and ANNA model 3 had tendencies to become larger (0.876 ± 0.068 and 0.899 ± 0.053). However, the area under the ROC of ANNA model 4 decreased to 0.870 and the patient’s age did not contribute to an increase in predictability in this study. Table 3 shows the cutoff points for the parameters used to generate ANNA model 3 (best ANNA model). Despite the expansion of the area under the ROC curve, the difference between the area of ANNA model 3 (0.899 ± 0.053) and LR model 2 (0.873 ± 0.065) was 0.026, which was not statistically significant (P = 0.756, Fig. 2B). The difference between the best ANNA model 3 and Partin Tables almost doubled from 0.069 to 0.143, but did not attain statistical significance probably owing to the limited data set (P = 0.157) (Fig. 2C).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONCONCLUSIONAcknowledgmentREFERENCES |
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ANNA is a complex computer system designed to replicate human decision making by emulating the human neuron. ANNA can be trained to recognize patterns derived from input variables and their associated outcomes and then to apply these patterns to new cases, different from conventional statistics that processes information step by step according to given sets of rules.
Properly trained neural networks have repeatedly been reported to demonstrate superior predictive accuracy to other predictive technologies including logistic regression in various biomedical fields (15,16), although some investigators have reported similarities of these predictive abilities (17–19).
Regarding the use of the ANNA method in prostate cancer, several authors have reported its superiority in predicting pathological stage since the first report by Snow et al. in 1994 (20–22). Many authors have demonstrated its superiority over the recognized predictive methods of traditional statistics only by comparing the accuracy, sensitivity, specificity and predictive value analyses at certain cutoff points. However, to determine true superiority between two different methods, ROC curves should be used as a comparative diagnostic method, as mentioned by Borque et al. (23). They reported a comparison of ANNA and traditional logistic regression models in the Spanish population using ROC curves. They used three input variables, preoperative PSA, clinical stage and biopsy Gleason score, and concluded that these two methods had almost the same ability to predict organ confinement of prostate cancer.
In our study, although the ANNA model showed better predictability than Partin Tables, it did not attain statistical significance. This may be influenced by the limited training, and further studies should be performed to determine the effect of the possibly biased clinical staging result in which the rate of organ-confined disease of cT2a cancer seems to be unexpectedly high (82%, 18/22 patients).
Accounting for the difference between the races, we have previously constructed a nomogram based on logistic regression in the Japanese population (24). It may be more easily used than ANNA because of its simplicity. However, the nomogram is a static predictive method and it cannot keep up with the variations in which the background population may be changed as a result of the developments in diagnostic methods. However, ANNA is a dynamic predictive method which can continue to improve its predictability by expanding the training data set over time. This is one of the advantages of ANNA.
Another reason why the difference in predictability between ANNA and Partin Tables did not reach statistical significance may be the small number of pretreatment parameters which had a relatively linear relationship to the pathological findings (25). Therefore, we added other pretreatment parameters to the input variables to improve the accuracy of ANNA in this data set. As additional parameters, first we used the percentage of cores with cancer and maximum tumor length, which had improved predictability of extracapsular extension of prostate cancer in the Japanese population in logistic regression analysis in a previous report (26). While PSA density had multi-collinearity to serum PSA, it could furthermore be added to the ANNA model with no assumption about a relationship to other variables, which is also one of the advantages of ANNA. With six pretreatment parameters, the ANNA model certainly showed excellent predictability (0.899: area under ROC of ANNA3), but the logistic regression model with five parameters also showed favorable accuracy. With these limited training data, these methods showed no remarkable difference in predictability.
Although the advantage of ANNA over logistic regression can be demonstrated particularly when many input variables that are related in a non-linear way are involved, the balance between the number of input variables and the training data set is very important. In this limited training data set, adding other parameters to the input variables gave rise to overfitting and as a result decreased the predictability (we added patients’ age to the input variables but it reduced the area under the ROC curve to 0.870). The usefulness of ANNA will be recognized when additional parameters can be used without overfitting with a larger training data set.
In this study, we have shown the possible favorable role of ANNA as a predictive method. More modeling optimization is necessary to improve the predictive accuracy and generalizability of ANNA in the future. However, the development of new predictive variables and accumulation in training and validation data sets can continue to evolve the learning ability of ANNA and has the possibility to make it an excellent diagnostic tool for predicting the pathological stage of prostate cancer in the Japanese population.
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CONCLUSION |
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TOPABSTRACTINTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONCONCLUSIONAcknowledgmentREFERENCES |
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We examined the usefulness of ANNA in predicting the organ confinement of prostate cancer in the Japanese population. Although the advantages of neural networks cannot be fully demonstrated with the currently limited data set, the technique can continue to evolve. There is the possibility that this new predictive method will improve the clinical staging of prostate cancer and assist in making suitable treatment decisions.
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Acknowledgment |
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TOPABSTRACTINTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONCONCLUSIONAcknowledgmentREFERENCES |
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This study was supported in part by a Grant-in-Aid from the Japanese Ministry of Health and Welfare.
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FOOTNOTES |
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+ For reprints and all correspondence: Yoshiyuki Matsui, Department of Urology, Kurashiki Central Hospital, 1–1–1 Miwa, Kurashiki 710, Japan. E-mail: ym7856@kchnet.or.jp.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONCONCLUSIONAcknowledgmentREFERENCES |
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Received July 5, 2002; accepted September 24, 2002
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