***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: CONTEXT Three fourths of US men older than 50 years have been screened with prostate-specific antigen ( PSA ) for prostate cancer . OBJECTIVE To estimate the receiver operating characteristic ( ROC ) curve for PSA . DESIGN , SETTING , AND PARTICIPANTS Calculation of PSA ROC curves in the placebo group of the Prostate Cancer Prevention Trial , a r and omized , prospect i ve study conducted from 1993 to 2003 at 221 US centers . Participants were 18 882 healthy men aged 55 years or older without prostate cancer and with PSA levels less than or equal to 3.0 ng/mL and normal digital rectal examination results , followed up for 7 years with annual PSA measurement and digital rectal examination .  If PSA level exceeded 4.0 ng/mL or rectal examination result was abnormal , a prostate biopsy was recommended . After 7 years of study participation , an end-of- study prostate biopsy was recommended in all cancer-free men . MAIN OUTCOME MEASURES Operating characteristics of PSA for prostate cancer detection , including sensitivity , specificity , and ROC curve . RESULTS Of 8575 men in the placebo group with at least 1 PSA measurement and digital rectal examination in the same year , 5587 ( 65.2 % ) had had at least 1 biopsy ; of these , 1225 ( 21.9 % ) were diagnosed with prostate cancer . Of 1213 cancers with Gleason grade recorded , 250 ( 20.6 % ) were Gleason grade 7 or greater and 57 ( 4.7 % ) were Gleason grade 8 or greater . The areas under the ROC curve ( AUC ) for PSA to discriminate any prostate cancer vs no cancer , Gleason grade 7 or greater cancer vs no or lower- grade cancer , and Gleason grade 8 or greater cancer vs no or lower- grade cancer were 0.678 ( 95 % confidence interval [ CI ] , 0.666 - 0.689 ) , 0.782 ( 95 % CI , 0.748 - 0.816 ) , and 0.827 ( 95 % CI , 0.761 - 0.893 ) , respectively ( all P values < .001 for AUC vs 50 % ) . For detecting any prostate cancer , PSA cutoff values of 1.1 , 2.1 , 3.1 , and 4.1 ng/mL yielded sensitivities of 83.4 % , 52.6 % , 32.2 % , and 20.5 % , and specificities of 38.9 % , 72.5 % , 86.7 % , and 93.8 % , respectively . Age-stratified analyses showed slightly better performance of PSA in men younger than 70 years vs those 70 years or older with AUC values of 0.699 ( SD , 0.013 ) vs 0.663 ( SD , 0.013 ) ( P = .03 ) . CONCLUSION There is no cutpoint of PSA with simultaneous high sensitivity and high specificity for monitoring healthy men for prostate cancer , but rather a continuum of prostate cancer risk at all values of PSA OBJECTIVES To determine the prostate-specific antigen ( PSA ) velocity , PSA slope , and PSA doubling time ( PSADT ) in men with positive biopsies , negative biopsies , and no biopsy indications 4 years after an initial screening ;  and to use this information to improve the test characteristics in the early detection of prostate cancer and provide normal values for these parameters in screened men with and without evidence of prostate cancer . METHODS Within the European R and omized Study of Screening for Prostate Cancer , section Rotterdam , we identified 9575 men with a second determination of PSA 4 years after the initial screening . These men were divided into three groups : men with positive biopsies , negative biopsies , and no biopsy  indications in the  second  round ( PSA less than 3.0 ng/mL ) . The predictive values of PSA dynamics for detection of prostate cancer were calculated . RESULTS The mean PSA velocity of men with prostate cancer was 0.62 ng/mL/yr versus 0.46 ng/mL/yr for men with a negative biopsy ( P = 0.001 ) . The mean PSADT for men with prostate cancer was 5.1 years and for those with a negative biopsy it was 6.1 years ( P = 0.002 ) . The PSADT for men with no indication for biopsy was 25.1 years . However , receiver operating characteristic analyses revealed only a moderate value for these test parameters in predicting biopsy outcome . CONCLUSIONS The mean values of PSA velocity , PSA slope , and PSADT in a rescreened population differed significantly between men with and without prostate cancer . However , in predicting the biopsy outcome , the PSA dynamics were of limited value BACKGROUND The optimal upper limit of the normal range for prostate-specific antigen ( PSA ) is unknown . We investigated the prevalence of prostate cancer among men in the Prostate Cancer Prevention Trial who had a PSA level of 4.0 ng per milliliter or less . METHODS Of 18,882 men enrolled in the prevention trial , 9459 were r and omly assigned to receive placebo and had an annual measurement of PSA and a digital rectal examination . Among these 9459 men , 2950 men never had a PSA level of more than 4.0 ng per milliliter or an abnormal digital rectal examination , had a final PSA determination , and underwent a prostate biopsy after being in the study for seven years . RESULTS Among the 2950 men ( age range , 62 to 91 years ) , prostate cancer was diagnosed in 449 ( 15.2 percent ) ; 67 of these 449 cancers ( 14.9 percent ) had a Gleason score of 7 or higher . The prevalence of prostate cancer was 6.6 percent among men with a PSA level of up to 0.5 ng per milliliter , 10.1 percent among those with values of 0.6 to 1.0 ng per milliliter , 17.0 percent among those with values of 1.1 to 2.0 ng per milliliter , 23.9 percent among those with values of 2.1 to 3.0 ng per milliliter , and 26.9 percent among those with values of 3.1 to 4.0 ng per milliliter . The prevalence of high- grade cancers increased from 12.5 percent of cancers associated with a PSA level of 0.5 ng per milliliter or less to 25.0 percent of cancers associated with a PSA level of 3.1 to 4.0 ng per milliliter . CONCLUSIONS Biopsy-detected prostate cancer , including high- grade cancers , is not rare among men with PSA levels of 4.0 ng per milliliter or less -- levels generally thought to be in the normal range PURPOSE Two artificial neural networks ( ANN ) for the early detection of prostate cancer in men with total prostate-specific antigen ( PSA ) levels from 2.5 to 4 ng/mL and from 4 to 10 ng/mL were prospect ively developed . The predictive accuracy of the ANN was compared with that obtained by use of conventional statistical analysis of st and ard PSA parameters . PATIENTS AND METHODS Consecutive men with a serum total PSA level between 4 and 10 ng/mL ( n = 974 ) and between 2.5 and 4 ng/mL ( n = 272 ) were analyzed . A separate ANN model was developed for each group of patients . Analyses were performed to determine the presence of prostate cancer . RESULTS The area under the receiver operator characteristic ( ROC ) curve ( AUC ) was 87.6 % and 91.3 % for the 2.5 to 4 ng/mL and 4 to 10 ng/mL ANN models , respectively . For the latter model , the AUC generated by the ANN was significantly higher than that produced by the single variables of total PSA , percentage of free PSA , PSA density of the transition zone ( TZ ) , and TZ volume ( P < .01 ) , but not significantly higher compared with multivariate analysis . For the 2.5 to 4 ng/mL model , the AUC of the ANN ROC curve was significantly higher than the AUCs for percentage of free PSA ( P = .0239 ) , PSA-TZ ( P = .0204 ) , and PSA density and total prostate volume ( P < .01 for both ) . CONCLUSION The predictive accuracy of the ANN was superior to that of conventional PSA parameters . ANN models might change the way patients referred for early prostate cancer detection are counseled regarding the need for prostate biopsy OBJECTIVES Several nomograms for prostate cancer detection have recently been developed . Because the incidence of prostate cancer is lower among Asian men , nomograms based on Western population s can not be directly applied to Japanese men . We , therefore , developed a model for predicting the probability of a positive initial prostate biopsy using clinical and laboratory data from a Japanese male population . METHODS Data were collected from 834 Japanese male referrals who underwent initial prostate biopsies as individual screening . We analyzed age , total prostate-specific antigen ( PSA ) level , free/total PSA ( f/t PSA ) ratio , prostate volume , and digital rectal examination findings . Of these data , we r and omly reserved 20 % for study validation . Logistic regression analysis estimated relative risk , 95 % confidence intervals , and P values . RESULTS Independent predictors of a positive biopsy result included elevated PSA levels , decreased f/T PSA ratio , advanced age , small prostate volume , and abnormal digital rectal examination findings . We developed a predictive nomogram for an initial positive biopsy using these variables . The area under the receiver operating characteristic curve for the model was 81.8 % , which was significantly greater than that of the prediction based on PSA alone ( area under the receiver operating characteristic curve 67.8 % ) . If externally vali date d , applying this model could reduce unnecessary biopsy procedures by 32 % and reduce the overall need for prostate biopsies by 26 % . CONCLUSIONS In this study of a Japanese population , incorporating clinical and laboratory data into a prebiopsy nomogram significantly improved the prediction of prostate cancer compared with predictions based solely on the individual factors OBJECTIVES To assess whether an artificial neural network ( multilayer perceptron , MLP ) and logistic regression ( LR ) could eliminate more false-positive prostate-specific antigen ( PSA ) results than the proportion of free PSA in a prostate cancer screening . METHODS MLP and LR models were constructed on the basis of data on  total PSA  , the  proportion of free PSA , digital rectal examination ( DRE ) , and prostate volume  from 656 consecutive men ( aged 55 to 67 years ) with total serum PSA concentrations of 4 to 10 ng/mL in the r and omized population -based prostate cancer screening study in Finl and . The MLP and LR models were vali date d using the " leave-one-out " method . RESULTS Of the 656 men , 23 % had prostate cancer and 77 % had either normal prostatic histology or a benign disease . At a 95 % sensitivity level , 19 % of the false-positive PSA results could be eliminated by using the proportion of free PSA versus 24 % with the LR model and 33 % with the MLP model ( P < 0.001 ) . At 80 % to 99 % sensitivity levels , the accuracy of the MLP and LR models was significantly higher than that of the proportion of free PSA . At 89 % to 99 % sensitivities , the accuracy of the MLP was higher than that of LR ( P < /= 0.001 ) . CONCLUSIONS At clinical ly relevant sensitivity levels , the MLP and LR models based on total PSA , the proportion of free PSA , DRE , and prostate volume could reduce the number of unnecessary biopsies significantly better than the proportion of free PSA alone in men with total PSA levels in the range 4 to 10 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 prospect i ve , 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 prospect ively tested in the 151 men .  Performance parameters ( including sensitivity , specificity , positive and negative predict 
    Output:
    
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