Eduard J. Gamito

Enrollment of racial and ethnic minorities in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.

BACKGROUND Minority populations in the United States, especially blacks and Hispanics, are generally underrepresented among participants in clinical trials. Here, we report the experience of enrolling ethnic minorities in a large cancer screening trial. METHODS The Prostate, Colorectal, Lung and Ovarian (PLCO) Cancer Screening Trial is a multicenter randomized trial designed to evaluate the effectiveness of screening for the PLCO cancers. Subjects were recruited at 10 U.S. centers between 1993 and 2001. One screening center had a major special recruitment effort for blacks and another center had a major special recruitment effort for Hispanics. RESULTS Among almost 155,000 subjects enrolled in PLCO, minority enrollment was as follows: black (5.0%), Hispanic (1.8%) and Asian (3.6%). This compares to an age-eligible population in the combined catchment areas of the PLCO centers that was 14.0% black, 2.9% Hispanic and 5.4% Asian, and an age-eligible population across the U.S. that was 9.5% black, 6.5% Hispanic and 3.0% Asian. About half (45%) of Hispanics were recruited at the center with the special Hispanic recruitment effort. Seventy percent of blacks were recruited at two centers; the one with the major special recruitment effort and a center in Detroit whose catchment area was 20% black among age-eligibles. Blacks, Hispanics and (non-Hispanic) whites were all more highly educated, less likely to currently smoke and more likely to get regular exercise than their counterparts in the general population. CONCLUSION Significant efforts were made to recruit racial/ ethnic minorities into PLCO, and these efforts resulted in enrollment levels that were comparable to those seen in many recent cancer screening or prevention trials. Blacks and Hispanics were nonetheless underrepresented in PLCO compared to their levels among age-eligibles in the overall U.S. population or in the aggregate PLCO catchment areas.

Factors contributing to the racial differences in prostate cancer mortality.

To analyse, in a retrospective cohort study, differences in rates of surgical treatment for prostate cancer between African‐Americans and White Americans, and to evaluate the extent to which these differences are associated with disparities in survival rates between these groups.

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 physicians and the patients 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.

Predicting the outcome of prostate biopsy in a racially diverse population: a prospective study

OBJECTIVES To develop a mathematical model to predict prostate biopsy outcome using readily available clinical variables. METHODS A total of 319 men (78% African American) undergoing transrectal ultrasound-guided prostate biopsy were prospectively studied. The parameters collected included age, race, prostate-specific antigen (PSA) level, PSA density (PSAD), digital rectal examination findings, biopsy history, prostate volume (by transrectal ultrasound), and ultrasound findings. Models were constructed using multivariate logistic regression (LR) analysis and back-propagation artificial neural networks (ANNs). Patient data were randomly split into five cross-validation sets and used to develop and validate the LR and ANN models. RESULTS Of the 319 men, 39% had a positive biopsy. The mean patient age was 65.1 +/- 8.3 years, with a mean PSA level of 12.6 +/- 24.9 ng/mL and a mean PSAD of 0.31 +/- 0.66 ng/mL/cm(3). Univariate analysis indicated a significant difference in age, PSA level, PSAD, free PSA, digital rectal examination findings, TRUS lesion, and biopsy history between the positive and negative biopsy groups (P <0.01). The mean area under the receiver operating characteristic curve (AUROC) for the five LR models was 0.76 +/- 0.04 (range 0.71 to 0.81). The median LR AUROC was 0.76, with a corresponding specificity of 0.13 at a sensitivity of 0.95. The mean AUROC for the five ANN models was 0.76 +/- 0.04 (range 0.71 to 0.83). The median ANN AUROC was 0.76, with a corresponding specificity of 0.21 at a sensitivity of 0.95. CONCLUSIONS Two models (LR and ANN) that predict outcome with high efficiency (AUROC = 0.76) were constructed from a contemporary, prospective database. Such models may be useful to patients and physicians alike when assessing the diagnostic strategies available to detect prostate cancer.

Use Of Artificial Neural Networks In Prostate Cancer

Artificial neural networks (ANNs) are a type of artificial intelligence software inspired by biological neuronal systems that can be used for nonlinear statistical modeling. In recent years, these applications have played an increasing role in predictive and classification modeling in medical research. We review the basic concepts behind ANNs and examine the role of this technology in selected applications in prostate cancer research.

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.