Kattan, M.W.

The relationship between prostate-specific antigen and prostate cancer risk: The prostate biopsy collaborative group (Article)

2010-09-01T00:00:00Z

Purpose: The relationship between prostate-specific antigen (PSA) level and prostate cancer risk remains subject to fundamental disagreements. We hypothesized that the risk of prostate cancer on biopsy for a given PSA level is affected by identifiable characteristics of the cohort under study. Experimental Design: We used data from five European and three U.S. cohorts of men undergoing biopsy for prostate cancer; six were population-based studies and two were clinical cohorts. The association between PSA and prostate cancer was calculated separately for each cohort using locally weighted scatterplot smoothing. Results: The final data set included 25,772 biopsies and 8,503 cancers. There were gross disparities between cohorts with respect to both the prostate cancer risk at a given PSA level and the shape of the risk curve. These disparities were associated with identifiable differences between cohorts: for a given PSA level, a greater number of biopsy cores increased the risk of cancer (odds ratio for >6- versus 6-core biopsy, 1.35; 95% confidence interval, 1.18-1.54; P < 0.0005); recent screening led to a smaller increase in risk per unit change in PSA (P = 0.001 for interaction term) and U.S. cohorts had higher risk than the European cohorts (2.14; 95% confidence interval, 1.99-2.30; P < 0.0005). Conclusions: Our results suggest that the relationship between PSA and risk of a positive prostate biopsy varies, both in terms of the probability of prostate cancer at a given PSA value and the shape of the risk curve. This poses challenges to the use of PSA-driven algorithms to determine whether biopsy is indicated.

Editorial: What patients newly diagnosed with clinically localized prostate cancer need to see: An editorial dialogue (Article)

2010-08-01T00:00:00Z

Author's response: Probabilistic predictions, medical decisions, and clinical usefulness (Article)

2010-01-01T00:00:00Z

Assessing the performance of prediction models: A framework for traditional and novel measures (Article)

2010-01-01T00:00:00Z

The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.

The Comparability of Models for Predicting the Risk of a Positive Prostate Biopsy with Prostate-Specific Antigen Alone: A Systematic Review (Article)

2008-08-01T00:00:00Z

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.

Validation of Pretreatment Nomograms for Predicting Indolent Prostate Cancer: Efficacy in Contemporary Urological Practice (Article)

2008-07-01T00:00:00Z

Purpose: Many patients diagnosed with low grade and early stage prostate cancer have indolent disease and may not benefit from immediate therapy. In patients referred for biopsy following community screening we validated the Kattan and Steyerberg nomograms for predicting indolent disease in a contemporary urological practice. Materials and Methods: A total of 296 patients who underwent prostate biopsy and radical prostatectomy at a single institution were identified for nomogram validation. All patients had clinically localized, stage T1c or T2a and biopsy Gleason score 6 prostate cancer. Clinical and biopsy pathological information was compared to surgery pathology results for nomogram validation with indolent disease defined as surgical Gleason score 6 or less, tumor volume less than 0.5 cc and organ confined disease. Nomogram performance was assessed by the ROC curve. Results: Of the patients 27.4% had pathologically indolent disease at prostatectomy. Based on pretreatment variables the Kattan and Steyerberg nomograms were able to predict indolent disease with similar discrimination levels (AUC 0.777 and 0.772, respectively). Conclusions: Two previously described nomograms performed equally well for predicting indolent disease. These data further establish the role of validated nomograms for clinical decision making for managing screening detected prostate cancer.

Nomogram use for the prediction of indolent prostate cancer: Impact on screen-detected populations (Article)

2007-11-15T00:00:00Z

BACKGROUND. Screening for prostate cancer has resulted in an increased incidence-to-mortality ratio. Not all cancers deserve immediate treatment. It has therefore become more important to be able to identify those cases of screen-detected prostate cancer most likely to show indolent behavior. METHODS. The Kattan-nomogram for the prediction of indolent prostate cancer was validated and recalibrated for use in a screening setting. The recalibrated nomogram was used to calculate the number of men who were predicted to have indolent cancer in a screen-detected cohort from the European Randomized study of Screening for Prostate Cancer (ERSPC), section Rotterdam. RESULTS. Of 1629 cancers detected in 2 subsequent screening rounds 825 were suitable for nomogram use. The remainder were very unlikely to have indolent cancer. A total of 485 men (485 of 825 = 59%) were predicted to have indolent cancer, which is 30% (485 of 1629) of all screen-detected cases. Cancers found at repeated screening after 4 years had a higher probability of indolent cancer than cases from the prevalence screening (44% vs 23%; P < .001). CONCLUSIONS. The current nomogram can identify substantial groups of screen-detected cancers that are likely indolent and can therefore be considered for active surveillance.

Prediction of Indolent Prostate Cancer: Validation and Updating of a Prognostic Nomogram (Article)

2007-01-01T00:00:00Z

Purpose: Screening with serum prostate specific antigen testing leads to the detection of many prostate cancers early in their natural history. Statistical models have been proposed to predict indolent cancer. We validated and updated model predictions for a screening setting. Materials and Methods: We selected 247 patients with clinical stage T1C or T2A from the European Randomized Study on Screening for Prostate Cancer who were treated with radical prostatectomy. We validated a nomogram that had previously been developed in a clinical setting. Predictive characteristics were serum prostate specific antigen, ultrasound prostate volume, clinical stage, prostate biopsy Gleason grade, and total length of cancer and noncancer tissue in biopsy cores. Indolent cancer was defined as pathologically organ confined cancer 0.5 cc or less in volume without poorly differentiated elements. Logistic regression was used to update the previous model and examine the contribution of other potential predictors. Results: Overall 121 of 247 patients (49%) had indolent cancer, while the average predicted probability was around 20% (p <0.001). Effects of individual variables were similar to those found before and discriminative ability was adequate (AUC 0.76). An updated model was constructed, which merely recalibrated the nomogram and did not apply additional predictors. Conclusions: Prostate cancers identified in a screening setting have a substantially higher likelihood of being indolent than those predicted by a previously proposed nomogram. However, an updated model can support patients and clinicians when the various treatment options for prostate cancer are considered.