Evaluation of prostAsure index in the detection of prostate cancer: a preliminary report

Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis

In this paper, the urinary bladder cancer diagnostic method which is based on Multi-Layer Perceptron and Laplacian edge detector is presented. The aim of this paper is to investigate the implementation possibility of a simpler method (Multi-Layer Perceptron) alongside commonly used methods, such as Deep Learning Convolutional Neural Networks, for the urinary bladder cancer detection. The dataset used for this research consisted of 1997 images of bladder cancer and 986 images of non-cancer tissue. The results of the conducted research showed that using Multi-Layer Perceptron trained and tested with images pre-processed with Laplacian edge detector are achieving AUC value up to 0.99. When different image sizes are compared it can be seen that the best results are achieved if 50×50 and 100×100 images were used.

Critical review of prostate cancer predictive tools

Prostate cancer is a very complex disease, and the decision-making process requires the clinician to balance clinical benefits, life expectancy, comorbidities and potential treatment-related side effects. Accurate prediction of clinical outcomes may help in the difficult process of making decisions related to prostate cancer. In this review, we discuss attributes of predictive tools and systematically review those available for prostate cancer. Types of tools include probability formulas, look-up and propensity scoring tables, risk-class stratification prediction tools, classification and regression tree analysis, nomograms and artificial neural networks. Criteria to evaluate tools include discrimination, calibration, generalizability, level of complexity, decision analysis and ability to account for competing risks and conditional probabilities. The available predictive tools and their features, with a focus on nomograms, are described. While some tools are well-calibrated, few have been externally validated or directly compared with other tools. In addition, the clinical consequences of applying predictive tools need thorough assessment. Nevertheless, predictive tools can facilitate medical decision-making by showing patients tailored predictions of their outcomes with various alternatives. Additionally, accurate tools may improve clinical trial design.

Techniques and predictive models to improve prostate cancer detection

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. Cancer 2009;115(13 suppl):3085-99. (c) 2009 American Cancer Society.

Inventory of prostate cancer predictive tools

PURPOSE OF REVIEW

We created an inventory of current predictive tools available for prostate cancer. This review may serve as an initial step toward a comprehensive reference guide for physicians to locate published nomograms that apply to the clinical decision in question. Using MEDLINE a literature search was performed on prostate cancer predictive tools from January 1966 to November 2007. We describe the patient populations to which they apply and the outcomes predicted, and record their individual characteristics.

RECENT FINDINGS

The literature search generated 111 published prediction tools that may be applied to patients in various clinical stages of disease. Of the 111 prediction tools, only 69 had undergone validation. We present an inventory of models with input variables, prediction form, number of patients used to develop the prediction tools, the outcome being predicted, prediction tool-specific features, predictive accuracy, and whether validation was performed.

SUMMARY

Decision rules, such as nomograms, provide evidence-based and at the same time individualized predictions of the outcome of interest. Such predictions have been repeatedly shown to be more accurate than those of clinicians, regardless of their level of expertise. Accurate risk estimates are also required for clinical trial design, to ensure homogeneous high-risk patient groups for whom new cancer therapeutics will be investigated.

The comparability of models for predicting the risk of a positive prostate biopsy with prostate-specific antigen alone: a systematic review

CONTEXT

The sensitivity and specificity profile of measuring levels of prostate-specific antigen (PSA) to select men for prostate biopsy is not optimal. This has prompted the construction of nomograms and artificial neural networks (ANNs) to increase the performance of PSA measurements.

OBJECTIVE

A systematic review of nomograms and ANNs designed to predict the risk of a positive prostate biopsy for cancer was conducted in order to determine their value versus measuring PSA levels alone.

EVIDENCE ACQUISITION

Medical Literature Analysis and Retrieval System Online (U.S. National Library of Medicine's life science database; MEDLINE) was searched using the terms "nomogram" "artificial neural network" and "prostate cancer" for dates up to and including July 2007 and was supplemented by manual searches of reference lists. Included studies used an assessment tool to examine the risk of a positive prostate biopsy in a man without a known cancer diagnosis. Intramodel comparisons with evaluation of PSA levels alone, and intermodel comparisons of area under the curve (AUC) from receiver operating characteristic (ROC) curves were conducted. Individual case examples were also used for comparisons.

EVIDENCE SYNTHESIS

Twenty-three studies examining 36 models were included. With the exception of two studies, all the models had AUC values of 0.70 or greater, with eight reporting an AUC of >/=0.80 and four (all ANNs) reporting an AUC >/=0.85, with variable validation status. Fourteen studies compared the AUC with PSA levels alone: all showed a benefit from using AUCs which varied from 0.02 to 0.26. Of the 16 external validation comparisons, in 13 the AUC was lower in the external population than in the model population.

CONCLUSIONS

Nomograms and ANNs produce improvements in AUC over measurement of PSA levels alone, but many lack external validation. Where this is available, the benefits are often diminished, although most remain significantly better than with evaluation of PSA levels alone. In men without additional risk factors, PSA cutoff values alone provide a relatively precise risk estimate, but if additional risk factors are known, PSA values alone are less accurate.

Application of artificial intelligence to the management of urological cancer

PURPOSE

Artificial intelligence techniques, such as artificial neural networks, Bayesian belief networks and neuro-fuzzy modeling systems, are complex mathematical models based on the human neuronal structure and thinking. Such tools are capable of generating data driven models of biological systems without making assumptions based on statistical distributions. A large amount of study has been reported of the use of artificial intelligence in urology. We reviewed the basic concepts behind artificial intelligence techniques and explored the applications of this new dynamic technology in various aspects of urological cancer management.

MATERIALS AND METHODS

A detailed and systematic review of the literature was performed using the MEDLINE and Inspec databases to discover reports using artificial intelligence in urological cancer.

RESULTS

The characteristics of machine learning and their implementation were described and reports of artificial intelligence use in urological cancer were reviewed. While most researchers in this field were found to focus on artificial neural networks to improve the diagnosis, staging and prognostic prediction of urological cancers, some groups are exploring other techniques, such as expert systems and neuro-fuzzy modeling systems.

CONCLUSIONS

Compared to traditional regression statistics artificial intelligence methods appear to be accurate and more explorative for analyzing large data cohorts. Furthermore, they allow individualized prediction of disease behavior. Each artificial intelligence method has characteristics that make it suitable for different tasks. The lack of transparency of artificial neural networks hinders global scientific community acceptance of this method but this can be overcome by neuro-fuzzy modeling systems.

Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for bladder cancer: a comparison with logistic regression analysis

OBJECTIVE

To compare the prognostic performance of an artificial neural network (ANN) with that of standard logistic regression (LR), in patients undergoing radical cystectomy for bladder cancer.

PATIENTS AND METHODS

From February 1982 to February 1994, 369 evaluable patients with non-metastatic bladder cancer had pelvic lymph node dissection and radical cystectomy for either stage Ta-T1 (any grade) tumour not responding to intravesical therapy, with or with no carcinoma in situ, or stage T2-T4 tumour. LR analysis based on 12 variables was used to identify predictors of overall 5-year survival, and the ANN model was developed to predict the same outcome. The LR analysis, based on statistically significant predictors, and the ANN model were the compared for their accuracy in predicting survival.

RESULTS

The median age of the patients was 63 years, and overall 201 of them died. The tumour stage and nodal involvement (both P<0.001) were the only statistically independent predictors of overall 5-year survival on LR analysis. Based on these variables, LR had a sensitivity and specificity for predicting survival of 68.4% and 82.8%, respectively; corresponding values for the ANN were 62.7% and 86.1%. For LR and ANN, the positive predictive values were 78.6% and 76.2%, and the negative predictive values were 73.9% and 76.5%, respectively. The index of diagnostic accuracy was 75.9% for LR and 76.4% for ANN.

CONCLUSIONS

The ANN accurately predicted the survival of patients undergoing radical cystectomy for bladder cancer and had a prognostic performance comparable with that of LR. As ANNs are based on easy-to-use software that can identify nonlinear interactions between variables, they might become the preferred tool for predicting outcome.

Computer technology in detection and staging of prostate carcinoma: A review

After two decades of increasing interest and research activity, computer-assisted diagnostic approaches are reaching the stage where more routine deployment in clinical practice is becoming a possibility [Kruppinski, E.A., 2004. Computer-aided detection in clinical environment: Benefits and challenges for radiologists. Radiology 231, 7-9]. This is particularly the case in the analysis of mammographic images [Helvie, M.A., Hadjiiski, L., Makariou, E., Chan, H.P., Petrick, N., Sahiner, B., Lo, S.C., Freedman, M., Adler, D., Bailey, J., Blane, C., Hoff, D., Hunt, K., Joynt, L., Klein, K., Paramagul, C., Patterson, S.K., Roubidoux, M.A., 2004. Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial. Radiology 231, 208-214] and in the detection of pulmonary nodules [Reeves, A.P., Kostis, W.J., 2000. Computer-aided diagnosis for lung cancer. Radiol. Clin. North Am. 38, 497-509]. However, similar approaches can be applied more widely with the promise of increasing clinical utility in other areas. We review how computer-aided approaches may be applied in the diagnosis and staging of prostatic cancer. The current status of computer technology is reviewed, covering artificial neural networks for detection and staging, computerised biopsy simulation and computer-assisted analysis of ultrasound and magnetic resonance images.

Effects of False-Positive Prostate Cancer Screening Results on Subsequent Prostate Cancer Screening Behavior

Objectives: Little is known about screening behavior following a false-positive prostate cancer screening result, which we have defined as a screening result with “abnormal/suspicious” labeling that did not result in a prostate cancer diagnosis within 14 months. The purpose of this analysis was to examine whether age, race, education, or previous false-positive prostate cancer screening results via prostate-specific antigen or digital rectal exam predict decision to obtain subsequent prostate cancer screening.

Methods: Data were drawn from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. The study sample consisted of 2,290 older men (mean age, 62.8 years; range, 55-75 years) who had false-positive (n = 318) or negative (n = 1,972) prostate-specific antigen or digital rectal exam baseline prostate cancer screening results. Multivariable logistic regression was used to assess the effect of false-positive results on subsequent prostate cancer screening behavior, adjusting for all covariates.

Results: The multivariable model showed that being African American (P = 0.016), and having a high school education or less (P = 0.007), having a previous false-positive prostate cancer screening result (P &lt; 0.001), were predictive of not returning for prostate cancer screening in the following screening trial year.

Conclusion: The study results highlight the importance of shared decision making between patients and their providers regarding the risks and benefits of prostate cancer screening, and follow-up options for abnormal prostate cancer screening results. Shared decision making may be especially important for African American men, whom prostate cancer disproportionately affects.

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.

Performance of a neural network in detecting prostate cancer in the prostate-specific antigen reflex range of 2.5 to 4.0 ng/mL

OBJECTIVES

To explore the potential role of a neural network-derived algorithm in enhancing the specificity of prostate cancer detection compared with the determination of prostate-specific antigen (PSA) and free PSA (fPSA) while maintaining a 90% detection rate. Recent information suggests that the incidence of detectable prostate cancer is similar in men whose PSA values range from 2.5 to 4.0 ng/mL and from 4.0 to 10.0 ng/mL. If the PSA threshold triggering a prostate biopsy is lowered to 2.5 ng/mL, approximately 13% of men older than 50 would be added to the patient biopsy pool.

METHODS

One hundred fifty-one men were enrolled in a prospective, Institutional Review Board-approved protocol to evaluate the incidence of cancer in a population of men who participated in an early-detection program and whose PSA level was between 2.5 and 4.0 ng/mL. All the men underwent biopsy using an 11-core multisite-directed biopsy scheme, and all biopsy specimens were examined by one pathologist. All men had a second blood specimen drawn before the biopsy for a determination of serum PSA, creatinine kinase, prostatic acid phosphatase, and fPSA. A new neural network algorithm was developed with PSA, creatinine kinase, prostatic acid phosphatase, fPSA, and age as input variables to produce a single-valued prostate cancer detection index (PCD-I). This new algorithm was then prospectively tested in the 151 men. Performance parameters (including sensitivity, specificity, positive and negative predictive values, and biopsies saved) were calculated, and a comparative analysis was performed to evaluate the differences among the new algorithm, percent fPSA, PSA density, and PSA density-transition zone.

RESULTS

Cancer was histologically confirmed in 24.5% (37 of 151) of the men. The median age of the men was 62 years (range 43 to 74). At a sensitivity of 92%, the specificity for percent fPSA was 11%. The new algorithm (PCD-I) demonstrated an additional enhancement of specificity to 62% at 92% sensitivity. Clinically, the PCD-I would result in a savings of 49% (74 of 151) of all biopsies or 63.6% (71 of 114) of all unnecessary biopsies.

CONCLUSIONS

A new generation algorithm, derived from a neural network (PCD-I) incorporating the parameters of age, creatinine kinase, PSA, prostatic acid phosphatase, and fPSA can significantly enhance the specificity and reduce the number of biopsies while maintaining a 92% sensitivity rate.

Prostate specific antigen: a decade of discovery--what we have learned and where we are going

PURPOSE

Many advances have occurred during the last decade in the clinical use of prostate specific antigen (PSA) for detecting, staging and monitoring prostate cancer. We review the clinical usefulness and limitations of serum PSA as a tumor marker of prostate cancer.

MATERIALS AND METHODS

The English language literature was reviewed with respect to the major contributions and limitations of PSA in present clinical practice.

RESULTS

Although controversial, age specific PSA reference ranges can improve the sensitivity for prostate cancer detection in young men and the specificity in older men. Percent free PSA improves the specificity for prostate cancer detection in men with PSA values between 4 and 10 ng./ml., and a PSA density of greater than 0.15 may better distinguish benign prostatic hyperplasia from prostate cancer. PSA velocity can improve the ability to detect prostate cancer when 3 serial PSA values are measured during a 2-year period. For prostate cancer staging PSA is most useful combined with clinical stage and Gleason score in multivariate analysis. Percent free PSA may prove useful for staging prostate cancer but further clinical trials are needed to determine its clinical usefulness. PSA is the most clinically useful means to monitor disease recurrence after treatment of prostate cancer. With ultrasensitive PSA assays it is now possible to increase the lead time for detection of disease recurrence by several months.

CONCLUSIONS

During the last decade much of the focus has been on improving the ability of this tumor marker to detect prostate cancer. PSA remains the best and most widely used tumor marker in urology today.

Treatment of prostate cancer

Prostate cancer is the leading cause of cancer death among older men in western countries. However, controversy surrounds many issues related to this disease, particularly its most appropriate treatment, with a wide spectrum of opinions ranging from watchful waiting to aggressive therapy. Patients with newly diagnosed prostate cancer, as well as their doctors, will have to make difficult decisions regarding treatment of this disease. In this article we discuss the current available treatment options and some novel therapeutic approaches to tackling the patient with prostate cancer.