Predictive modeling techniques in prostate cancer

A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer

Object

Pathologic prediction of prostate cancer can be made by predicting the patient's prostate metastasis prior to surgery based on biopsy information. Because biopsy variables associated with pathology have uncertainty regarding individual patient differences, a method for classification according to these variables is needed.

Method

We propose a deep belief network and Dempster-Shafer- (DBN-DS-) based multiclassifier for the pathologic prediction of prostate cancer. The DBN-DS learns prostate-specific antigen (PSA), Gleason score, and clinical T stage variable information using three DBNs. Uncertainty regarding the predicted output was removed from the DBN and combined with information from DS to make a correct decision.

Result

The new method was validated on pathology data from 6342 patients with prostate cancer. The pathology stages consisted of organ-confined disease (OCD; 3892 patients) and non-organ-confined disease (NOCD; 2453 patients). The results showed that the accuracy of the proposed DBN-DS was 81.27%, which is higher than the 64.14% of the Partin table.

Conclusion

The proposed DBN-DS is more effective than other methods in predicting pathology stage. The performance is high because of the linear combination using the results of pathology-related features. The proposed method may be effective in decision support for prostate cancer treatment.

Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model

The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582).

Techniques and predictive models to improve prostate cancer detection

The use of prostate-specific antigen (PSA) as a screening test remains controversial. There have been several attempts to refine PSA measurements to improve its predictive value. These modifications, including PSA density, PSA kinetics, and the measurement of PSA isoforms, have met with limited success. Therefore, complex statistical and computational models have been created to assess an individual's risk of prostate cancer more accurately. In this review, the authors examined the methods used to modify PSA as well as various predictive models used in prostate cancer detection. They described the mathematical underpinnings of these techniques along with their intrinsic strengths and weaknesses, and they assessed the accuracy of these methods, which have been shown to be better than physicians' judgment at predicting a man's risk of cancer. Without understanding the design and limitations of these methods, they can be applied inappropriately, leading to incorrect conclusions. These models are important components in counseling patients on their risk of prostate cancer and also help in the design of clinical trials by stratifying patients into different risk categories. Thus, it is incumbent on both clinicians and researchers to become familiar with these tools. Cancer 2009;115(13 suppl):3085-99. (c) 2009 American Cancer Society.