An artificial neural network considerably improves the diagnostic power of percent free prostate‐specific antigen in prostate cancer diagnosis: Results of a 5‐year investigation

Abstract

Our study was performed to evaluate the diagnostic usefulness of %fPSA alone and combined with an ANN at different PSA concentration ranges, including the low range 2-4 ng/ml, to improve the risk assessment of prostate cancer. A total of 928 men with prostate cancer and BPH without any pretreatment of the prostate in the PSA range 2-20 ng/ml were enrolled in the study between 1996 and 2001. An ANN with input data of PSA, %fPSA, patient's age, prostate volume and DRE status was developed to calculate the individual's risk before performing a prostate biopsy within the different PSA ranges 2-4, 4.1-10 and 10.1-20 ng/ml. ROC analysis and cut-off calculations were used to estimate the diagnostic improvement of %fPSA and ANN in comparison to PSA. At the 90% sensitivity level, %fPSA and ANN performed better than PSA in all ranges, enhancing the specificity by 15-28% and 32-44%, respectively. For the low PSA range 2-4 ng/mL, we recommend a first-time biopsy at an ANN specificity level of 90%. For PSA 4 -10 ng/mL, we recommend a first-time biopsy based on the ANN at the 90% sensitivity level. Use of an ANN enhances the %fPSA performance to further reduce the number of unnecessary biopsies within the PSA range 2-10 ng/ml. © 2002 Wiley-Liss, Inc. Key words: prostate cancer; prostate-specific antigen; receiver operating characteristic; neural network; prostate biopsyProstate cancer is the most common neoplasia among men in the Western world and PSA is recognized as the best marker for its early detection. 1 Due to lack of specificity, various techniques, such as PSA density, velocity and age-specific ranges, have been developed but are reported to be only partially successful. 1 Measurements of the molecular forms of PSA improve specificity over tPSA alone. 2,3 Approximately 20 -25% of all biopsies could be avoided using %fPSA in the tPSA range 4 -10 ng/ml 4,5 as well as for tPSA values lower than 4 ng/ml. 6 -8 For further enhancement of %fPSA specificity to differentiate between prostate cancer and benign prostate diseases, logistic regression 9,10 and ANNs have been successfully used. 11,12 Briefly, these models can predict diagnostic outcome using a variety of factors. Based on the data of 28 studies, both methods perform equally, especially for large cohorts. 13 However, ANNs can predict an outcome for an individual patient, which cannot currently be done with traditional statistics and they can better handle a greater number of variables with more nonlinear relations. 14 In a study of 656 men within the 4 -10 ng/ml tPSA range, Finne et al. 11 demonstrated an advantage of ANNs compared to logistic regression, avoiding approximately one-third of unnecessary biopsies at 95% sensitivity. For the lower tPSA range 2.6 -4 ng/ml, Babaian et al. 12 used a combination of 3 different ANNs and saved 63.6% of all unnecessary biopsies. To date, no study has evaluated 1 valid, clinically practicable ANN on %fPSA data for the whole tPSA range 2-10 or 2-20 ng/ml.We used data from a 5-year period of %fPSA measurements with only 1 assa...

The ratio of prostate‐specific antigen (PSA) to prostate volume (PSA density) as a parameter to improve the detection of prostate carcinoma in PSA values in the range of < 4 ng/mL

“…This may be the best possible approach with the advent of artificial neural networks, which are capable of weighing the influence of each factor in determining the final result. 56 Data from the two different periods showed increasing prostate volume in patients with PCa and BPH within the last years. Because no method was changed, it is difficult to explain this result.…”

Algorithms based on prostate‐specific antigen (PSA), free PSA, digital rectal examination and prostate volume reduce false‐postitive PSA results in prostate cancer screening

“…Combined use of several variables has been shown to reduce the number of false-positive PSA results more efficiently than single use of the proportion of free PSA. [12][13][14][15][16][17][18][19] However, LR or neural network algorithms for prostate cancer detection have not been studied prospectively; i.e., use of algorithm outcomes for making real-life biopsy decisions has not been reported. We simulated a prospective setting by using earlier subjects as training data and later ones for testing of the algorithms.…”

“…DISCUSSION Earlier studies showed that algorithms based on total PSA, free PSA, DRE and prostate volume can be used to reduce the number of false-positive PSA results in screening for prostate cancer more efficiently than the proportion of free PSA alone. [12][13][14][15][16][17][18][19] Despite this, there are no reports on the use of such algorithms in a prospective setting. Our study included 1,775 screen-positive men from the 3 largest participating centers in the European Randomized Study of Screening for Prostate Cancer, 20 -22 making it the largest study on predicting prostate biopsy results in the PSA range 4 -10 g/l using LR and neural networks.…”

Comparison of Eight Computer Programs for Receiver-Operating Characteristic Analysis

“…To compare the programs, we used a previously described dataset of 928 men with prostate cancer (n ϭ 606) and benign prostatic hyperplasia (n ϭ 322) and subgroups of this population (3 ). ROC analyses of total prostate-specific antigen (tPSA), 3 free PSA (fPSA), the ratio of fPSA to tPSA (fPSA/tPSA), and of other values calculated by an artificial neural network approach with the mentioned dataset (3 ) were carried out to estimate the advantages and disadvantages of each program.…”