Predictive Modeling Techniques in Prostate Cancer

A number of new predictive modeling techniques have emerged in the past several years. These methods can be used independently or in combination with traditional modeling techniques to produce useful tools for the management of prostate cancer. Investigators should be aware of these techniques and avail themselves of their potentially useful properties. This review outlines selected predictive methods that can be used to develop models that may be useful to patients and clinicians for prostate cancer management.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63147/1/10915360152745812.pd

Racial differences in serum prostate-specific antigen (PSA) doubling time, histopathological variables and long-term PSA recurrence between African-American and white American men undergoing radical prostatectomy for clinically localized prostate cancer

To determine if there are significant differences in biochemical characteristics, biopsy variables, histopathological data, and rates of prostate-specific antigen (PSA) recurrence between African-American (AA) and white American (WA) men undergoing radical prostatectomy (RP), as AA men are twice as likely to die from prostate cancer than their white counterparts. PATIENTS AND METHODS We established a cohort of 1058 patients (402 AA, 646 WA) who had RP and were followed for PSA recurrence. Age, race, serum PSA, biopsy Gleason score, clinical stage, pathological stage, and PSA recurrence data were available for the cohort. The chi-square test of proportions and t -tests were used to assess basic associations with race, and log-rank tests and Cox regression models for time to PSA recurrence. Forward stepwise variable selection was used to assess the effect on the risk of PSA recurrence for race, adjusted by the other variables added one at a time. RESULTS The AA men had higher baseline PSA levels, more high-grade prostatic intraepithelial neoplasia (HGPIN) in the biopsy, and more HGPIN in the pathology specimen than WA men. The AA men also had a shorter mean (sd) PSA doubling time before RP, at 4.2 (4.7) vs 5.2 (5.9) years. However, race was not an independent predictor of PSA recurrence ( P  = 0.225). Important predictors for PSA recurrence in a multivariable model were biopsy HGPIN ( P  < 0.014), unilateral vs bilateral cancer ( P  < 0.006), pathology Gleason score and positive margin status (both P  < 0.001). CONCLUSIONS This study indicates that while there are racial differences in baseline serum PSA and incidence of HGPIN, race is not an independent risk factor for PSA recurrence. Rather, other variables such as pathology Gleason score, bilateral cancers, HGPIN and margin positivity are independently associated with PSA recurrence. The PSA doubling time after recurrence may also be important, leading to the increased mortality of AA men with prostate cancer.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74706/1/j.1464-410X.2005.05561.x.pd

Genetic Adaptive Neural Network to Predict Biochemical Failure After Radical Prostatectomy: A Multi-institutional Study

Background and Purpose: Despite many new procedures, radical prostatectomy remains one of the commonest methods of treating clinically localized prostate cancer. Both from the physician's and the patient's point of view, it is important to have objective estimation of the likelihood of recurrence, which forms the foundation for treatment selection for an individual patient. Currently, it is difficult to predict the probability of biochemical recurrence (rising serum prostate specific antigen [PSA] concentration) in an individual patient, and approximately 30% of the patients do experience recurrence. Tools predicting the recurrence will be of immense practical utility in the treatment selection and planning follow up. We have utilized preoperative parameters through a computer based genetic adaptive neural network model to predict recurrence in such patients, which can help primary care physicians and urologists in making management recommendations. Patients and Methods: Fourteen hundred patients who underwent radical prostatectomy at participating institutions form the subjects of this study. Demographic data such as age, race, preoperative PSA, systemic biopsy based staging and Gleason scores were used to construct a neural network model. This model simulated the functioning of a trained human mind and learned from the database. Once trained, it was used to predict the outcomes in new patients. Results: The patients in this comprehensive database were representative of the average prostate cancer patients as seen in USA. Their mean age was 68.4 years, the mean PSA concentration before surgery was 11.6 ng/mL, and 67% patients had a Gleason sum of 5 to 7. The mean length of follow-up was 41.5 months. Eighty percent of the cancers were clinical stage T2 and 5% T3. In our series, 64% of patients had pathologically organ-confined cancer, 33% positive margins, and 14% had seminal vesicle invasion. Lymph node positive patients were not included in this series. Progression as judged by serum PSA was noted in 30.6%. With entry of a few routinely used parameters, the model could correctly predict recurrence in 76% of the patients in the validation set. The area under the curve was 0.831. The sensitivity was 85%, the specificity 74%, the positive predictive value 77%, and the negative predictive value of 83%. Conclusion: It was possible to predict PSA recurrence with a high accuracy (76%). Physicians desiring objective treatment counseling can use this model, and significant cost savings are anticipated because of appropriate treatment selection and patient-specific follow-up protocols. This technology can be extended to other treatments such as watchful waiting, external-beam radiation, and brachytherapy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63438/1/10915360152745849.pd