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)

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology.

BackgroundTesting a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified.MethodsThe PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system.ResultsThe search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible.ConclusionES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research

Diagnosis of Prostate Cancer using Soft Computing Paradigms

The process of diagnosing of prostate cancer using traditional methods is cumbersome because of the similarity of symptoms that are present in other diseases. Soft Computing (SC) paradigms which mimic human imprecise data manipulation and learning capabilities have been reviewed and harnessed for diagnosis and classification of prostate cancer. SC technique based on Adaptive Neuro-Fuzzy Inference System (ANFIS) facilitated symptoms analysis, diagnosis and prostate cancer classification. Age of Patient (AP), Pains in Urination (PU), Frequent Urination (FU), Blood in Semen (BS) and Pains in Pelvic (PP) served as input attributes while Prostate Risk (PR) served as output. Matrix laboratory provided the programming tools for system implementation. The practical function of the system was assessed using prostate cancer data collected from the University of Uyo Teaching Hospital. A 95% harmony observed between the computed and the expected output in the ANFIS model, showed the superiority of the ANFIS model over the fuzzy model. The system is poised to assist medical professionals in the domain of diagnosis and classification of prostate cancer for the promotion of management and treatment decisions

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

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

Breast cancer diagnosis using a hybrid genetic algorithm for feature selection based on mutual information

Feature Selection is the process of selecting a subset of relevant features (i.e. predictors) for use in the construction of predictive models. This paper proposes a hybrid feature selection approach to breast cancer diagnosis which combines a Genetic Algorithm (GA) with Mutual Information (MI) for selecting the best combination of cancer predictors, with maximal discriminative capability. The selected features are then input into a classifier to predict whether a patient has breast cancer. Using a publicly available breast cancer dataset, experiments were performed to evaluate the performance of the Genetic Algorithm based on the Mutual Information approach with two different machine learning classifiers, namely the k-Nearest Neighbor (KNN), and Support vector machine (SVM), each tuned using different distance measures and kernel functions, respectively. The results revealed that the proposed hybrid approach is highly accurate for predicting breast cancer, and it is very promising for predicting other cancers using clinical data

Improving the diagnostic yield of prostate cancer

Although the introduction of PSA as a biomarker for detecting prostate cancer has enabled the disease to be diagnosed at an earlier stage, the primary drawback to PSA as a biomarker and basis for prostate cancer screening and diagnosis is its lack of specificity. To address this issue, PSA density, velocity and percent free PSA have all been assessed as potential approaches for improving the diagnosis of prostate cancer, albeit with variable and limited success. These challenges and the desire to improve the management and treatment of patients with prostate cancer prompted studies that investigated the capacity of measuring pro- PSA levels to improve diagnostic yield over that of PSA alone. Our study confirmed that combining pro-PSA, total and percent free PSA improved the specificity of prostate cancer detection from that of 23% for PSA alone to 44%. To further improve the utility of pro-PSA, we subsequently determined that the ratio of pro-PSA and benign-PSA can identify the presence of prostate cancer with greater accuracy when the percent free PSA is below 15%. Traditionally, prostate cancer has been definitively diagnosed by performing transrectal ultrasound (TRUS) guided prostate biopsies. However, such a biopsy technique has a cancer detection rate of less than 30% in a benign feeling prostate. In addition, when TRUS biopsies are repeated due to rising PSA, the cancer detection rate significantly reduces to below 10% for men undergoing a third set of such biopsies. We therefore undertook a study to assess the diagnostic strength of the transperineal template prostate biopsies (TPTPB) in this group of men and demonstrated that the cancer detection rate of the TPTPB is significantly better than that of the TRUS biopsy (52%-68%). We subsequently directly compared TRUS against TPTPB in biopsy naïve men and revealed that TPTPB significantly outperforms TRUS in prostate cancer detection (60% versus 32%). Based on the established concept that there is a reciprocal relationship between cancer and the immune system, and the proposition that the presence of cancer will influence the phenotype of immune cells in the blood which can be detected by profiling peripheral blood mononuclear cells, we subsequently investigated whether profiling the phenotype of immune cells in the blood could further improve our ability not only to detect prostate cancer, but clinically significant prostate cancer. For this, flow cytometric immune profiling of the peripheral blood from men with benign prostate disease and patients with confirmed prostate cancer identified phenotypic features 'fingerprints' within the lymphocyte populations which, when incorporated into machine learning based algorithms, can be used to distinguish between the presence of benign prostate disease and prostate cancer. Furthermore, we identified a panel of eight natural killer (NK) cell phenotypic features which can be used to very accurately differentiate between high risk and low/intermediate risk prostate cancer when incorporated into machine learning algorithms. In summary, over the past two decades, my work has not only resulted in an improvement in the utility of PSA as a biomarker for the detection of prostate, but I also demonstrated that performing transperineal prostate biopsies significantly improves prostate cancer detection. Furthermore, we have recently revealed that our immune system can aid us to differentiate between indolent and clinically significant prostate cancer. Taken together, this programme of work has greatly impacted clinical practice

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements