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

Abstract

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).

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

“…Our proposed DBN-DS achieved an AUC of 0.777, compared to 0.620 for the Partin tables. This result is similar to that reported by Cosma et al [ 4 ], although different data sets were used for each study; however, they show a high consistency with the results of the present study.…”

“…In a recent pathological staging methodology study, Cosma et al [ 4 ] use a neuro-fuzzy model, with an approach similar to ours. The results also indicated that the neural network-fuzzy-based computational intelligence learning approach is suitable for prostate cancer staging and exceeds the performance of the Partin tables.…”

“…The clinical stage evaluation is based on data gathered from clinical tests that are available prior to treatment or the surgical removal of the tumor. Cancer staging evaluation occurs both before and after the tumor is removed: the clinical and pathological stages, respectively [ 4 ]. Pathologic staging is determined after the removal of the tumor tissue and after surgery.…”

Breast Cancer Diagnosis Using a Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information

“…Firstly, the General Discriminant Analysis (GDA) method was used to reduce the dimensionality of the feature space to eight dimensions, and then least square support vector machine (LS-SVM) was used in classification stage. Cosma et al [7] proposed a neuro-fuzzy model for predicting the pathological stage in patients with prostate cancer. Their results revealed that the neuro-fuzzy system outperformed a statistical nomogram commonly adopted by clinicians to predict cancer stage prior at the pre-operative stage.…”

A survey on computational intelligence approaches for predictive modeling in prostate cancer

“…This will reveal whether computational approaches are superior to nomograms when trained and tested on small datasets. Such a comparison is described in Cosma et al (2016). …”