Development and external validation of an extended 10-core biopsy nomogram

OBJECTIVES: To test the accuracy of a previously externally validated sextant biopsy nomogram in referred men exposed to > or =10 or more biopsy cores. Moreover, we explored the hypothesis that a more accurate predictive tool could be developed. METHODS: Previous nomogram predictors (age, digital rectal examination, prostate-specific antigen, and percent free PSA) were used to assess the accuracy of our previous nomogram in a cohort consisting of 2900 men referred for prostatic evaluation. Moreover, these variables were complemented with sampling density (SD) (i.e., ratio of gland volume and the number of planned biopsy cores) within multivariable logistic regression models (LRM) predicting presence of prostate cancer (pCA) on the initial 10 or more core biopsy. The LRMs were used to develop and internally validate (200 bootstrap resamples) a new nomogram in 1162 men from Hamburg, Germany. The LRMs' external validity was tested in three separate cohorts (Hamburg, n=582; Milan, n=961; Seattle, n=195). RESULTS: The contemporary external validation of the previously validated sextant nomogram demonstrated 70% accuracy. Internal validation of the new nomogram demonstrated 77% accuracy, and external cohorts demonstrated 73-76% accuracy. CONCLUSIONS: In the era of extended biopsy schemes, previously developed predictive models are less accurate in predicting the probability of pCA on initial biopsy. We developed a new tool that allows obtaining more accurate predictions. Moreover, before biopsy, it also allows defining the ideal ratio between gland volume and the number of planned biopsy cores that would yield the ideal biopsy rate.     

Development and validation of a nomogram predicting the outcome of prostate biopsy based on patient age, digital rectal examination and serum prostate specific antigen

PURPOSE: We developed and validated a nomogram which predicts presence of prostate cancer (PCa) on needle biopsy. MATERIALS AND METHODS: We used 3 cohorts of men who were evaluated with sextant biopsy of the prostate and whose presenting prostate specific antigen (PSA) was not greater than 50 ng/ml. Data from 4,193 men from Montreal, Canada were used to develop a nomogram based on age, digital rectal examination (DRE) and serum PSA. External validation was performed on 1,762 men from Hamburg, Germany. Data from these men were subsequently used to develop a second nomogram in which percent free PSA (%fPSA) was added as a predictor. External validation was performed using 514 men from Montreal. Both nomograms were based on multivariate logistic regression models. Predictive accuracy was evaluated with areas under the receiver operating characteristic curve and graphically with loess smoothing plots. RESULTS: PCa was detected in 1,477 (35.2%) men from Montreal, 739 (41.9%) men from Hamburg and 189 (36.8%) men from Montreal. In all models all predictors were significant at 0.05. Using age, DRE and PSA external validation AUC was 0.69. Using age, DRE, PSA and %fPSA external validation AUC was 0.77. CONCLUSIONS: A nomogram based on age, DRE, PSA and %fPSA can highly accurately predict the outcome of prostate biopsy in men at risk for PCa.     

Development, validation, and head-to-head comparison of logistic regression-based nomograms and artificial neural network models predicting prostate cancer on initial extended biopsy

OBJECTIVES: Using cohorts examined by extended biopsy, we developed and validated multivariate models predicting prostate cancer on initial biopsy and examined whether these extended biopsy-based models outperform previously established models. METHODS: Initial extended biopsy (median 22 cores) was performed in 1509 Japanese men including 1083 at Tokyo Medical and Dental University Hospital (TMDU) and 426 at Cancer Institute Hospital (CIH). Logistic regression-based nomograms 1 and artificial neural network (ANN) 1 incorporating age, digital rectal examination, and prostate-specific antigen (PSA) and free PSA, and nomogram 2 and ANN2 further incorporating transrectal ultrasound (TRUS) findings and prostate volume were constructed on the TMDU data. These and previously established models were externally validated on the CIH data set and predictive accuracy was compared directly. RESULTS: Without TRUS-derived information, nomogram 1 outperformed the ANN1. With TRUS-derived information, nomogram 2 was more accurate than ANN2. External validation revealed applicability of the Western models to Japanese population, superiority of the nomograms over ANN models, and better predictive accuracy of our extended biopsy-based nomograms than the previous 6-10-core biopsy-based models. Using nomograms 1 and 2, 16% and 19% unnecessary biopsies would be saved at 95% sensitivity. CONCLUSIONS: We developed new nomograms predicting prostate cancer on initial biopsy in men with PSA <20ng/ml. Predictive accuracy of these extended biopsy-based nomograms is better than those of previously established models based on 6-10-core biopsies. Our models might help clinicians to decide if a patient requires biopsy and to avoid unnecessary biopsies.     

External validation and head-to-head comparison of Japanese and Western prostate biopsy nomograms using Japanese data sets

The objective of this study was to perform external validation of a previously developed prostate biopsy nomogram (the CHIBA nomogram) and to compare it with previously published nomograms developed in Japanese and overseas populations. Two different cohorts of patients were used: one from the Chiba Cancer Center ( n  = 392) in which transperineal 16-core biopsy was performed, and another from Chibaken Saiseikai Narashino Hospital ( n  = 269) in which transrectal 16-core biopsy was carried out. All patients were Japanese men with serum prostate-specific antigen levels less than 10 ng/mL. The predictive accuracy of our CHIBA nomogram and of four other published nomograms (Finne's sextant biopsy-based logistic regression model, Karakiewicz's sextant biopsy-based nomogram, Chun's 10-core biopsy-based nomogram and Kawakami's three-dimensional biopsy-based nomogram) was quantified based on area under the curve derived from receiver operating characteristic curves. Head-to-head comparison of area under the curve values demonstrated that our nomogram was significantly more accurate than all other models except Chun's ( P  = 0.012 vs Finne's, P  = 0.000 vs Karakiewicz's, and P  = 0.003 vs Kawakami's). Our nomogram appears to be more useful for the Japanese population than Western models. Moreover, external validation demonstrates that its predictive accuracy does not vary according to biopsy approach. This is the first report to demonstrate that the predictive accuracy of a nomogram is independent from the biopsy method.     

History of malignancy is a predictor of prostate cancer detection: Incorporation into a pre‐biopsy nomogram

Objectives: To examine whether history of malignancy adds any significant information to the prediction of positive prostate biopsy in referred men with moderately elevated prostate‐specific antigen (PSA) and to develop a predicting nomogram that does not require extra examinations other than PSA. Methods: A total of 1767 consecutive Japanese men with PSA less than 10 ng/mL who underwent prostate biopsy were included in the study cohort. Age, digital rectal examination (DRE), PSA, body mass index, family history of prostate cancer and number of previous malignancies other than the prostate were evaluated in regard to their association with prostate cancer. A logistic regression‐based nomogram for predicting prostate cancer was developed and externally validated. Results: Of the 1767 men, 269 had a history of malignancy with a total of 312 primary sites. Univariate and multivariate analyses revealed that DRE, PSA, age, family history and number of previous malignancies are independent and significant predictors of positive biopsy result. External validation revealed that the predicting accuracy of a nomogram incorporating these five variables is significantly higher than those of PSA or PSA and DRE. Using the nomogram, 8% of unnecessary biopsies would be saved at 95% sensitivity. Conclusions: We demonstrated for the first time that history of malignancy is a potent predictor of prostate cancer in men with moderately elevated PSA even if the established risk factors are adjusted. The nomogram can be a useful tool in decision‐making of prostate biopsy. In daily practice, history of malignancy should be rigorously taken from these men before a decision is made regarding prostate biopsy.     

预测中国男性前列腺癌切片检查阳性率的视距诺模图：基于台湾地区的研究

最近有许多前列腺癌检测的视距诺模图被开发出来。然而,中国男性前列腺癌的发病率较低,这些针对于其他人群所开发的视距诺模图可能无法直接应用于中国男性身上。因此,我们根据临床与实验室数据,开发了一个预测中国男性前列腺切片检查阳性率的模型。我们收集893位转诊来做初步前列腺切片检查的中国男性病人资料,利用其中697位的资料来开发新的视距诺模图,然后再用其他196位来做外部验证。我们分析了年龄、前列腺体积、总前列腺特异性抗原、前列腺特异性抗原密度、直肠指检以及经直肠超声波回声结果。统计使用罗吉斯回归分析来估算胜算比、95％置信区间以及P值。切片检查阳性率的独立预测因子包括年龄、前列腺体积、总前列腺特异性抗原升高、异常直肠指检以及经直肠超声波高回声或低回声。我们使用这些独立预测因子开发了一个预测切片检查阳性几率的视距诺模图。此视距诺模图的接收操作特征曲线下面积为88．8％,高于单独以总前列腺特异性抗原的预测力（接收操作特征曲线下面积为74．7％）。我们将此视距诺模图做外部验证,发现预测概率为0．827,准确率为78．1％。结合临床和实验室数据所开发出的视距诺模图,比起用其他个别因子,大大提高了预测前列腺癌的准确率。     

Validation of a nomogram for predicting positive repeat biopsy for prostate cancer

PURPOSE: We reported a nomogram and subsequently a corrected version for predicting the probability of positive biopsy in men with 1 or more prior negative biopsies. In this study we assessed the validity of this nomogram when applied to an external dataset. MATERIALS AND METHODS: There were 230 patients from the Brooklyn Veterans Administration Medical Center who underwent 1 or more repeat biopsies after initial negative biopsy from January 1993 to June 2003. Predictor variables studied in the nomogram were patient age, family history of prostate cancer, digital rectal examination, serum prostate specific antigen, prostate specific antigen slope, months from initial negative biopsy session, months from previous negative biopsy session, cumulative number of negative cores previously taken and history of high grade intraepithelial neoplasm or atypical small acinar proliferation. We calculated the nomogram predicted probability in each patient. These predicted outcomes were compared with actual biopsy results. Area under the ROC curve was calculated as a measure of discrimination. Calibration was assessed graphically. RESULTS: We evaluated a total of 356 repeat biopsies in 230 patients (mean 2.56 total biopsies per patient). The mean number of total cores per patient was 17.9. There were 78 positive biopsies. The area under the ROC curve was 0.71, which was greater than any single risk factor. Nomogram calibration appeared to be good. CONCLUSIONS: Our corrected nomogram for predicting positive repeat biopsy performed well when applied to a sample of men at the Brooklyn Veterans Administration Medical Center. This nomogram can provide important additional information to aid the urologist and patient with a negative biopsy in evaluating clinical options.     

Validation of an MRI-based prostate cancer prebiopsy Gleason score predictive nomogram

Background:Gleason score grading is a cornerstone of risk stratification and management of patients with prostate cancer (PCa). In this work, we derive and validate a nomogram that uses prostate multiparametric magnetic resonance imaging (MP-MRI) and clinical patient characteristics to predict biopsy Gleason scores (bGS).Materials and methods:A predictive nomogram was derived from 143 men who underwent MP-MRI prior to any prostate biopsy and then validated on an independent cohort of 235 men from a different institution who underwent MP-MRI for PCa workup. Screen positive lesions were defined as lesions positive on T2W and DWI sequences on MP-MRI. Prostate specific antigen (PSA) density, number of screen positive lesions, and MRI suspicion were associated with PCa Gleason score on biopsy and were used to generate a predictive nomogram. The independent cohort was tested on the nomogram and the most likely bGS was noted.Results:The mean PSA in the validation cohort was 9.25ng/mL versus 6.8ng/mL in the original cohort (p = 0.001). The distribution of Gleason scores between the 2 cohorts were not significantly different (p = 0.7). In the original cohort of men, the most probable nomogram generated Gleason score agreed with actual pathologic bGS findings in 61% of the men. In the validation cohort, the most likely nomogram predicted bGS agreed with actual pathologic bGS 51% of the time. The nomogram correctly identified any PCa versus non-PCa 63% of the time and clinically significant (Gleason score ≥ 7) PCa 69% of the time. The negative predictive value for clinically significant PCa using this prebiopsy nomogram was 74% in the validation group.Conclusions:A preintervention nomogram based on PSA and MRI findings can help narrow down the likely pathologic finding on biopsy. Validation of the nomogram demonstrated a significant ability to correctly identify the most likely bGS. This feasibility study demonstrates the potential of a prebiopsy prediction of bGS and based on the high negative predictive value, identification of men who may not need biopsies, which could impact future risk stratification for PCa.     

A nomogram is more accurate than a regression tree in predicting lymph node invasion in prostate cancer

OBJECTIVE

To compare the performance and discriminant properties of two instruments (a tree‐structured regression model and a logistic regression‐based nomogram), recently developed to predict lymph node invasion (LNI) at radical prostatectomy (RP), in a contemporary cohort of European patients.

PATIENTS AND METHODS

The cohort comprised 1525 consecutive men treated with RP and bilateral pelvic LN dissection (PLND) in two tertiary academic centres in Europe. Clinical stage, pretreatment prostate‐specific antigen (PSA) level and biopsy Gleason sum were used to test the ability of the regression tree and the nomogram to predict LNI. Accuracy was quantified by the area under the receiver operating characteristic curve (AUC). All analyses were repeated for each participating institution.

RESULTS

The AUC for the nomogram was 81%, vs 77% for the regression tree (P = 0.007). When data were stratified according to institution, the nomogram invariably had a higher AUC than the regression tree (Hamburg cohort: nomogram 82.1% vs regression tree 77.0%, P = 0.002; Milan cohort: 82.4% vs 75.9%, respectively; P = 0.03).

CONCLUSIONS

Nomogram‐based predictions of LNI were more accurate than those derived from a regression tree; therefore, we recommend the use of nomogram‐derived predictions.     

External Validation of Urinary PCA3-Based Nomograms to Individually Predict Prostate Biopsy Outcome

Background

Prior to safely adopting risk stratification tools, their performance must be tested in an external patient cohort.

Objective

To assess accuracy and generalizability of previously reported, internally validated, prebiopsy prostate cancer antigen 3 (PCA3) gene-based nomograms when applied to a large, external, European cohort of men at risk of prostate cancer (PCa).

Design, setting, and participants

Biopsy data, including urinary PCA3 score, were available for 621 men at risk of PCa who were participating in a European multi-institutional study.

Intervention

All patients underwent a ≥10-core prostate biopsy. Biopsy indication was based on suspicious digital rectal examination, persistently elevated prostate-specific antigen level (2.5–10 ng/ml) and/or suspicious histology (atypical small acinar proliferation of the prostate, >/= two cores affected by high-grade prostatic intraepithelial neoplasia in first set of biopsies).

Measurements

PCA3 scores were assessed using the Progensa assay (Gen-Probe Inc, San Diego, CA, USA). According to the previously reported nomograms, different PCA3 score codings were used. The probability of a positive biopsy was calculated using previously published logistic regression coefficients. Predicted outcomes were compared to the actual biopsy results. Accuracy was calculated using the area under the curve as a measure of discrimination; calibration was explored graphically.

Results and limitations

Biopsy-confirmed PCa was detected in 255 (41.1%) men. Median PCA3 score of biopsy-negative versus biopsy-positive men was 20 versus 48 in the total cohort, 17 versus 47 at initial biopsy, and 37 versus 53 at repeat biopsy (all p ≤ 0.002). External validation of all four previously reported PCA3-based nomograms demonstrated equally high accuracy (0.73–0.75) and excellent calibration. The main limitations of the study reside in its early detection setting, referral scenario, and participation of only tertiary-care centers.

Conclusions

In accordance with the original publication, previously developed PCA3-based nomograms achieved high accuracy and sufficient calibration. These novel nomograms represent robust tools and are thus generalizable to European men at risk of harboring PCa. Consequently, in presence of a PCA3 score, these nomograms may be safely used to assist clinicians when prostate biopsy is contemplated.     

Validación prospectiva de un nomograma predictivo de la presencia de cáncer de próstata en pacientes que se someten a biopsia transrectal ecodirigida de 10 cilindros

IntroductionUltrasound-guided transrectal prostate biopsy is currently an indispensable test for diagnosing prostate cancer. Many variables have been related to the presence of cancer in the biopsy (e.g. digital rectal examination [DRE], serum levels of prostatespecific antigen [PSA], free PSA fraction [PSAI/PSAt]). Multivariate mathematical models integrating these variables (nomograms, artificial network models) and improving the capacity to predict tests results are currently available.ObjectiveTo develop a nomogram for predicting the probability of a positive prostate biopsy in patients in whom this test is requested, and to use such nomogram in subsequent patients to assess its predictive ability.Material and methodsA total of 410 consecutive patients undergoing biopsy due to a suspicious digital rectal examination or two serum PSA values higher than 4 ng/mL were enrolled into the study. Ten cores were taken in the prostate biopsy. Patients with both PSA levels >20 ng/ml and prior biopsies were excluded. The following variables were recorded in each patient: age, total PSA, free PSA fraction, prostate volume, transition zone volume, PSA density, PSA density adjusted by transition zone volume, digital rectal examination, and findings suggesting cancer during transrectal ultrasound (hypoechogenic nodules). Prospective external validation was performed with 185 patients who met the same inclusion criteria.Statistical analysis consisted of four phases: a univariate study, a multivariate logistic regression study which was used to develop the nomogram, internal validation, and prospective external validation. S-Plus#r Programme Design and SPSS 12.0#r software was used for the procedure.ResultsVariables found to be independently and significantly associated to the presence of cancer included age, digital rectal examination, trnsition zone volume, PSA density, and the presence of hypoechogenic nodules during transrectal ultrasound. Such variables were therefore used to develop the nomogram. The goodness-of-fit of the nomogram was 84%. Validation with an external sample showed a 73% concordance index.ConclusionA nomogram having a satisfactory predictive ability and fit that allows for predicting the prostate biopsy result with a high accuracy rate was developed.     

建立预测中国人前列腺重复穿刺活检阳性的数学模型

目的:建立可以预测国人经直肠超声引导下重复穿刺活检阳性的数学模型。方法:170例在首次穿刺活检诊断为前列腺良性病变的患者行重复穿刺。记录并分析患者的年龄、前列腺体积、血清PSA、游离PSA(f-PSA)/总PSA(t-PSA)、PSA上升速度、PSA密度(PSAD)、直肠指检(DRE)、首次穿刺病理结果等相关因素。将变量通过逐步回归建立回归方程,在此基础上建立重复穿刺活检阳性的危险评分数学模型。该模型的预测价值通过受试者工作曲线下面积来评估。结果:170例前列腺重复穿刺活检的患者中,前列腺癌的穿刺检出率为31.8%(54/170)。建立的数学模型影响因素包括:患者的年龄、前列腺体积、PSA、f-PSA/t-PSA、PSA上升速度、PSAD、DRE、首次穿刺结果是否为上皮内瘤变。该模型预测价值较高,曲线下面积为82.4%,大于患者PSAD、前列腺体积、PSA上升速度、f-PSA/t-PSA、DRE等单因素的66.9%、72.6%、69.6%、69.3%、58.5%。结论:该数学模型是临床多因素综合分析基础上建立的,可以很好地预测前列腺重复穿刺活检阳性的概率。     

Can nomograms derived in the U.S. applied to German patients? A study about the validation of preoperative nomograms predicting the risk of recurrence after radical prostatectomy

In patients suffering from prostate cancer, preoperative nomograms, which predict the risk of recurrence may provide a helpful tool in regard to the counselling and planning of an appropriate therapy. The best known nomograms were published by the Baylor College of Medicine, Houston and the Harvard Medical School, Boston. We investigated these nomograms derived in the U.S. when applied to German patients. Data from 1003 patients who underwent radical prostatectomy at the University-Hospital Hamburg were used for validation. Nomogram predictions of the probability for 2-years (Harvard nomogram) and 5-years (Kattan nomogram) freedom from PSA recurrence were compared with actual follow-up recurrence data using areas under the receiver-operating-characteristic curves (AUC). The recurrence free survival after 2 and 5 years was 78% and 58%, respectively. The AUC of the Harvard nomogram predicting 2-years probability of freedom from PSA recurrence was 0.80 vs. Kattan-Nomogram 5-years prediction of 0.83. Thereby, the Kattan nomogram showed a significant higher predictive accuracy (p=0.0274). For that reason preoperative nomograms derived in the U.S. can be applied to german patients. However, we would recommend the utilization of the Kattan nomogram due to its higher predictive accuracy.     

Multicenter European External Validation of a Prostate Health Index–based Nomogram for Predicting Prostate Cancer at Extended Biopsy

Background

External validation of a prediction tool is mandatory to assess the tool's accuracy and generalizability within different patient cohorts.

Objective

To externally validate a previously developed Prostate Health Index (PHI)–based nomogram for predicting the presence of prostate cancer (PCa) at biopsy.

Design, setting, and participants

The study population consisted of 883 patients who were scheduled for a prostate biopsy at one of five European tertiary care centers. Total prostate-specific antigen (tPSA), free prostate-specific antigen (fPSA), and [−2]pro–prostate-specific antigen (p2PSA) levels were determined. The fPSA-to-tPSA ratio (%fPSA), p2PSA, and PHI ([p2PSA / fPSA] × √tPSA) were calculated.

Intervention

Extended initial and repeat prostate biopsy.

Outcome measurements and statistical analysis

Logistic regression models were fitted to test the predictors of PCa and to determine their predictive accuracy. A calibration plot was used to evaluate the extent of overestimation or underestimation between nomogram predictions and observed PCa rate. Decision curve analysis (DCA) provided an estimate of the net benefit obtained by using the PHI-based nomogram.

Results and limitations

Of 833 patients, 365 (41.3%) were diagnosed with PCa at extended prostate biopsy. In accuracy analyses, PHI was the most informative predictor of PCa (0.68), outperforming tPSA (0.51) and %fPSA (0.64). The predictive accuracy of the previously developed nomogram was 75.2% (95% confidence interval, 71.4–78.1). Calibration of the nomogram was good in patients at a low to intermediate predicted probability of PCa, while calibration was suboptimal, with a tendency to overestimate the presence of PCa, in high-risk patients. Finally, DCA demonstrated that the use of the PHI-based nomogram resulted in the highest net benefit. The main limitation of the study is the fact that only Caucasian patients were included.

Conclusions

At external validation, the previously developed PHI-based nomogram confirmed its ability to determine the presence of PCa at biopsy. These findings provide further evidence supporting the potential role of the nomogram in the biopsy decision pathway for European men with suspected PCa.

Patient summary

In the current study, we externally validated a Prostate Health Index–based nomogram to predict the presence of prostate cancer (PCa) at biopsy. This tool may help clinicians determine the need for a prostate biopsy in European patients with suspected PCa.     

Head-to-head comparison of two online nomograms for prostate biopsy outcome prediction

Study Type – Diagnosis (exploratory cohort)
Level of Evidence 2b What’s known on the subject? and What does the study add? In recent years, several nomograms were developed in an effort to decrease the number of unnecessary prostate biopsies. The European SWOP‐PRI and the North American PCPT are among the most popular. However, evidence on the relative predictive accuracy is lacking. A head‐to‐head comparison on the diagnostic accuracy of two previously validated prostate cancer risk predictors on biopsy confirmed the superiority of these tools over PSA alone. Moreover, in the studied population, the European SWOP‐PRI proved to be more accurate than the North American PCPT‐CRC.

OBJECTIVE

? To compare the diagnostic accuracy of two previously validated prostate cancer risk predictors on biopsy.

PATIENTS AND METHODS

? In total, 390 consecutive patients submitted to 10‐core systematic transrectal prostate biopsy at our institution were included in this retrospective study. ? External validation of a European (European Randomized Study of Screening for Prostate Cancer derived Prostate Risk Indicator; SWOP‐PRI) and a North American (Prostate Cancer Prevention Trial Cancer Risk Calculator; PCPT‐CRC) nomogram was performed. ? The predictive accuracy of these online available nomograms was calculated based on the area under the curve derived from receiver–operator characteristic curves and then compared using the DeLong method.

RESULTS

? Both tools were confirmed to be superior to prostate‐specific antigen alone. Moreover, the SWOP‐PRI (77.9%) displays a 7.96% increase in the predictive accuracy compared to the PCPT‐CRC (69.9%) in a statistically significant fashion (P= 0.002).

CONCLUSIONS

? The results obtained in the present study confirm the utility of nomograms with respect to biopsy outcome prediction in patients with suspicion of prostate cancer. ? In the current sample of patients, the European‐based nomogram appears to be more accurate than the North American nonogram, which lacks information regarding prostate volume and prostatic ultrasonographic lesions. ? To our knowledge, this is the first study to compare the accuracy of these popular risk calculators in a specific population.     

Development and internal validation of a nomogram predicting the probability of prostate cancer Gleason sum upgrading between biopsy and radical prostatectomy pathology

OBJECTIVE: Previous reports indicate that as many as 43% of men with low grade PCa at biopsy will be diagnosed with high-grade PCa at RP. We explored the rate of upgrading from biopsy to RP specimen in our contemporary cohort, and developed a model capable of predicting the probability of biopsy Gleason sum upgrading. MATERIALS AND METHODS: The study cohort consisted of 2982 men treated with RP, with available clinical stage, serum prostate specific antigen and biopsy Gleason scores. These clinical data were used as predictors in multivariate logistic regression models (LRM) addressing the rate of Gleason sum upgrading between biopsy and RP pathology. LRM regression coefficients were used to develop a nomogram predicting the probability of Gleason sum upgrading and was subjected to 200 bootstrap resamples for internal validation and to reduce overfit bias. RESULTS: Overall, 875 patients were upgraded (29.3%). In multivariate LRMs, all predictors were highly significant (all p values <0.0001). Bootstrap-corrected predictive accuracy of the nomogram predicting the probability of Gleason sum upgrading between biopsy and RP was 0.804. CONCLUSION: We developed a highly accurate clinical aid for treatment decision-making. It may prove useful when the possibility of a more aggressive Gleason variant may change the treatment options.     

Initial biopsy outcome prediction--head-to-head comparison of a logistic regression-based nomogram versus artificial neural network

OBJECTIVES: Nomograms and artificial neural networks (ANNs) represent alternative methodologic approaches to predict the probability of prostate cancer on initial biopsy. We hypothesized that, in a head-to-head comparison, one of the approaches might demonstrate better accuracy and performance characteristics than the other. METHODS: A previously published nomogram, which relies on age, digital rectal examination, serum prostate-specific antigen (PSA), and percent-free PSA, and an ANN, which relies on the same predictors plus prostate volume, were applied to a cohort of 3980 men, who were subjected to multicore systematic prostate biopsy. The accuracy and the performance characteristics were compared between these two approaches. RESULTS: The accuracy of the nomogram was 71% versus 67% for the ANN (p=0.0001). Graphical exploration of the performance characteristics demonstrated virtually perfect predictions for the nomogram. Conversely, the ANN underestimated the observed rate of prostate cancer. CONCLUSIONS: A 4% increase in predictive accuracy implies that the use of the nomogram instead of the ANN will result in 40 additional patients who will be correctly classified between benign and cancer.     

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.     

A preoperative nomogram identifying decreased risk of positive pelvic lymph nodes in patients with prostate cancer

PURPOSE: We developed a preoperative nomogram for prediction of lymph node metastases in patients with clinically localized prostate cancer. MATERIALS AND METHODS: The study was a retrospective, nonrandomized analysis of 7,014 patients treated with radical prostatectomy at 6 institutions between 1985 and 2000. Exclusion criteria consisted of preoperative androgen ablation therapy, salvage radical prostatectomy and pretreatment prostate specific antigen (PSA) greater than 50 ng/ml. Preoperative predictors of lymph node metastases consisted of pretreatment PSA, clinical stage (1992 TNM) and biopsy Gleason sum. These predictors were used in logistic regression analysis based nomograms to predict the probability of lymph node metastases. RESULTS: Overall 5,510 patients with complete clinical and pathological information were included in the study. Lymph nodes metastases were present in 206 patients (3.7%). Pretreatment PSA, biopsy Gleason sum, clinical stage and institution represented predictors of lymph node status (p <0.001). Bootstrap corrected predictive accuracy of the 3-variable nomogram (clinical stage, Gleason sum and PSA) was 0.76. Inclusion of a fourth variable, which accounts for institutional differences in lymph node metastases, yielded an area under the receiver operating characteristics curve of 0.78. The negative predictive value of our nomograms was 0.99 when they predicted 3% or less chance of positive lymph nodes. CONCLUSIONS: Using clinical information, we produced 2 calibrated and validated nomograms, which accurately predict pathologically negative lymph nodes in men with localized prostate cancer who are candidates for radical prostatectomy.