Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

The accurate prediction of pathologic stage in cancer 594management is important in choosing an adequate treatment plan. Accurate staging is critical for prostate cancer because the majority of men diagnosed with prostate cancer are older adults with other co-morbidities (1). However, cancer staging performed by a physician may be subjective, and therefore depends heavily on experience, skill, and knowledge. Within this background, various clinical decision support systems (CDSSs) derived from computerized machine learning techniques have been introduced as unbiased and objective supporters for decision-making.

There have been several studies to improve the prostate cancer detection rate with the use of prediction models based on logistic regression analysis and an artificial neural network (ANN) (2-11). The use of a support vector machine (SVM) approach has been introduced as a potential alternative to conventional ANNs (2, 12). SVMs have attracted attention as a useful tool for image recognition and bioinformatics by defining the optimal separating hyperplane with the maximal margin.

Many predictive models to predict pathologic stage have been developed on the basis of different parameters of transrectal ultrasound (TRUS)-guided prostate biopsy (13). However, no report is available on the pre-operative prediction of advanced prostate cancer based on prostate biopsy by applying CDSSs, such as SVM and ANN models, in spite of the advancement in solving classification problems. Moreover, a comparison study between a SVM and other established CDSSs, such as an ANN, on the accurate prediction of advanced prostate cancer with parameters acquired from prostate biopsy is needed.

The purpose of this study was to develop SVM and ANN models for the pre-operative prediction of advanced prostate cancer with the parameters of TRUS-guided prostate biopsies and to compare the accuracies of each model.

Based on the concept that prostate cancer volume is correlated strongly with local invasiveness, metastatic potential, and a loss of histologic differentiation, various quantitative parameters from needle biopsy results have been assessed to predict pathologic stage in radical retropubic prostatectomy (17). The current study demonstrated that multiple logistic regression analysis of our quantitative parameters for predicting stage indicated that maximum cancer length is a unique independent predictor of organ-confined disease. ROC curve analysis found that "length-parameters", such as maximum cancer length, total linear cancer length, and percentage cancer length, were significantly superior to "number-parameters", including number of cores positive and percentage of cores positive. Furthermore, ROC curve analysis also revealed that percentage cancer length had a similar performance for predicting pathologic stage to total linear cancer length (13).

The combined use of the serum PSA level, the biopsy Gleason score, and biopsy parameters is advocated to improve the pre-operative predictability of final pathologic stage. Park et al. (13) reported that the maximum cancer length in combination with the PSA level and Gleason score improves pathologic stage predictability by ROC curve analysis (18-21). In our study, biopsy parameters and various clinical parameters were used as input variables to construct the SVM and ANN models.

Although many models or nomograms are available to the clinician to predict the pathologic stage and freedom from biochemical failure after treatment, few models are designed to predict the pathologic stage of prostate cancer with the outcome of TRUS-guided prostate biopsy (2). Because prostate cancer is a disease that requires more than one option for the physician to be able to determine diagnosis, staging, and treatment, it provides a good example of the need for a CDSS. We tried to construct CDSSs using the SVM and ANN models and to compare the performances of these models based on readily available parameters, including biopsy parameters.

ANNs are computer programs that simulate some of the higher-level functions of the architecture of the human brain (11). In ANNs simple processing units (nodes) which simulate neurons are linked via weighted interconnections. The interconnection weights function as multipliers that simulate the connection strengths between neurons in an analogous biological model. The nodes are commonly arranged in three or more layers (input, output, and hidden layers). The input layer accepts the values of the predictor variables presented to the neural network, while one or more output nodes represent the predicted outputs (prediction of cancer or disease stages). One or more hidden layers of nodes link the input and output layers (2, 22).

There are several reports in which ANNs were used for the early detection of prostate cancer, a prostate rebiopsy, prostate cancer staging, or for predicting biochemical failure. Zlotta et al. (23) reported that the ANN outperformed logistic regression analysis and correctly predicted pathologic stage in > 90% of the validation patients with serum PSA levels < 10 ng/mL based on clinical, biochemical, and biopsy data. Babaian et al. (24) compared the sensitivity, specificity, and negative and positive predictive values between the use of a neural network algorithm and other PSA parameters showed that the ANN was significantly better in terms of specificity when sensitivity was constantly held at 92%.

The use of SVM developed by Vapnik (25) has resulted in an advance to solve or classify pattern recognition problems. The aim of the SVM is to devise a computationally-efficient way of learning separating hyperplanes in a high-dimensional feature space. The SVM can map the input vectors into a high-dimensional feature space through some non-linear mapping. In this space, an optimal separating hyperplane is constructed. The process of training an SVM classifier is equivalent to finding this optimal hyperplane in a way that minimizes the error on the training dataset and maximizes the perpendicular distance between the decision boundary and the closest data points in classes (2, 26-29). The advantage of the SVM is that an SVM classifier depends only on the support vectors, and the classifier function is not influenced by the entire data set, as is the case for many neural network systems, including ANN. Another characteristic of the SVM is the possibility to deal efficiently with a very large number of features because of the exploitation of kernel functions (2, 30).

It has been shown that the empirical performance of SVMs is generally as good as the best ANN solutions, and it has been proposed that the good performance of SVMs results from fewer model parameters to optimize in the SVM approach, reducing the possibility of over-fitting the training data and thus increasing the actual performance. Chang et al. (31) reported that the classification ability of the SVM is nearly equal to that of the neural network model in imaging diagnosis of breast malignancy. According to their study, the training and diagnosis procedure of the SVM is faster and more stable than other neural network models.

In our study the CDSSs, such as a SVM or an ANN, could provide the probability that the patient has advanced prostate cancer (more than pT3a) with inputs of various clinical and biopsy parameters. From our results, the AUC for the SVM and the ANN were 0.805 and 0.719, respectively. There were some false-positive and -negative cases. The false-positive cases were due to an overestimate of the pre-operative stage and the false-negative cases were due to an underestimate of the pre-operative stage. The false-positive cases might occur when the selected biopsy areas had a concentration of poorly differentiated cancer cells. Conversely, the false-negative cases might occur when the selected biopsy areas had little distribution of the poorly differentiated cancer cells. In the pre-operative prediction of advanced prostate cancer, the performance of the SVM was statistically superior to ANN. Based on our results, the CDSSs, such as a SVM or an ANN derived from computerized machine learning techniques, make it possible for the clinician to predict the pathologic stage more objectively without biases and to determine adequate management planning for biopsy-proven prostate cancer patients. It is expected that the SVM aids the physician in choosing the best treatment option.

Our study had several limitations. First, each process to acquire the clinical and biopsy parameters was performed by one analyst. This might be influenced by individual experience, skill, and knowledge. Moreover, the techniques used by the same expert might undergo subtle changes with time. Second, any predictive model based on maximum cancer length should be further validated and modified because the quantity of the tissue core invaded by a tumor depends on several other factors, including tumor and prostate volume, tumor distribution, and the biopsy procedure (13, 32).

Despite the limitations to the current study, our CDSSs for pre-operative prediction of advanced prostate cancer with input data of TRUS-guided prostate biopsy and clinical parameters can offer the physician and the patient information regarding tumor staging. Based on our results, with respect to the pre-operative prediction of advanced prostate cancer, the performance of the SVM was superior to ANN.