Predictive Modeling Techniques in Prostate Cancer

Tewari, Ashutosh; Porter, Christopher R.; Peabody, James O.; Crawford, E. David; Demers, Raymond Y.; Johnson, Christine Cole; Wei, John T.; Divine, George; O’Donnell, Colin; Gamito, Eduard J.; Menon, Mani

doi:10.1089/10915360152745812

Cited by 5 publications

(5 citation statements)

References 56 publications

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“…Cancer prediction systems which consider various variables for the prediction of an outcome require computational intelligent methods for efficient prediction outcomes [ 7 ]. Although computational intelligence approaches have been used to predict prostate cancer outcomes, very few models for predicting the pathological stage of prostate cancer exist.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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).

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Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…Other statistical models also are possible 88. Look‐up tables have been well known in the urologic literature since the publication of the so‐called Partin tables in 1997, which helped predict pathologic staging 89.…”

Section: Predictive Models In Prostate Cancer Detectionmentioning

confidence: 99%

“…Although this method has not yet been applied to prostate cancer detection, it has shown promise in other areas of research, including risk assessment in pancreatic cancer 97, 98. There still are other predictive models that have been described, including continuous‐time state transition methods, stochastic microsimulation, classification and regression trees, and group methods of data handling, among others 88, 98‐100. Although these remain promising predictive tools, they have not yet been applied to prostate cancer detection.…”

Section: Predictive Models In Prostate Cancer Detectionmentioning

confidence: 99%

Techniques and predictive models to improve prostate cancer detection

Herman

Dorsey

John

et al. 2009

Cancer

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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. Despite intensive work over the last several decades, prostate cancer continues to be the most common cancer in men and their second leading cause of cancer death. In 2008, it is estimated that 186,320 men received a new diagnosis of this disease, and nearly 29,000 died from it.1 However, there is no universal agreement on screening for prostate cancer. The American Urological Association and the American Cancer Society both recommend using a combination of digital rectal examination (DRE) and serum prostatespecific antigen (PSA) level to screen low-risk white men starting at age 50 years and to screen high-risk populations (including African Americans) starting at age 40 years. Much of the dilemma has to do with limitations of the PSA test itself, which has poor sensitivity and specificity. Various attempts to improve the predictive capacity of PSA likewise have met with only partial success. Because of these deficiencies, statistical and computational models have been created to more accurately predict a patient's risk of prostate cancer at biopsy. This, in turn, helps patients make a more informed decision concerning the choice to proceed with biopsy, a decision that should not be made lightly given the costs involved, the possibility of complications, and the chance of diagnosing clinically insignificant cancer. In this review, we provide a brief summary of PSA and its permutations and examine the methods that generate the predictive models as well as their inherent strengths and weaknesses.Overview of PSA and PSA-specific antigen modifications Catalona et al initially explored the use of PSA as a screening tool. In 2 large studies of healthy men aged !50 years, a higher screening PSA level was correlated with a greater likelihood of cancer. 5,6 Furth...

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“…While the Partin tables have been verified from 2001 to 2011, there are questions about their applicability to current patients following environmental changes [ 11 ]. Thus, a new classification method using machine learning is needed to provide an accurate prediction of the pathology stage [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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

Kim

Choi

Lee

et al. 2018

Journal of Healthcare Engineering

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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.

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Predictive Modeling Techniques in Prostate Cancer

Cited by 5 publications

References 56 publications

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

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

Techniques and predictive models to improve prostate cancer detection

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

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