Comparison of Nomograms With Other Methods for Predicting Outcomes in Prostate Cancer: A Critical Analysis of the Literature

Abstract

Purpose: Accurate estimates of risk are essential for physicians if they are to recommend a specific management to patients with prostate cancer. Accurate risk estimates are also required for clinical trial design, to ensure homogeneous patient groups. Because there is more than one model available for prediction of most outcomes, model comparisons are necessary for selection of the best model.We describe the criteria based on which to judge predictive tools, describe the limitations of current predictive tools, and compare the different predictive methodologies that have been used in the prostate cancer literature. Experimental Design: Using MEDLINE, a literature search was done on prostate cancer decision aids from January 1966 to July 2007. Results: The decision aids consist of nomograms, risk groupings, artificial neural networks, probability tables, and classification and regression tree analyses. The following considerations need to be applied when the qualities of predictive models are assessed: predictive accuracy (internal or ideally external validation), calibration (i.e., performance according to risk level or in specific patient subgroups), generalizability (reproducibility and transportability), and level of complexity relative to established models, to assess whether the new model offers advantages relative to available alternatives. Studies comparing decision aids have shown that nomograms outperform the other methodologies. Conclusions: Nomograms provide superior individualized disease-related risk estimations that facilitate management-related decisions. Of currently available prediction tools, the nomograms have the highest accuracy and the best discriminating characteristics for predicting outcomes in prostate cancer patients.

External Validation of the Updated Nomogram Predicting Lymph Node Invasion in Patients with Prostate Cancer Undergoing Extended Pelvic Lymph Node Dissection

“…Here came the necessity to develop validated predictive models, able to better calculate the specific risk of LNI for each patients and to identify men who should benefit from ePLND and those who might be safely spared [6,7,8]. Unfortunately, several nomograms, based on the above-mentioned preclinical features, usually demonstrate high accuracy at the internal validation, but are not provided of any external validation [8,9]. A nomogram developed by Briganti et al [2], including the percentage of positive cores, has recently received an external validation by Heidenreich et al [10], becoming the foremost predictor of LNI in PCa.…”

Evaluation of the prognostic significance of MSMB and CRISP3 in prostate cancer using automated image analysis

“…Many groups have described tissue biomarkers of prognostic value in prostatic tumors (reviwed in 4), but the use of such biomarkers to improve the predictive accuracy of existing nomograms have been largely unsuccessful. 5 Limiting factors include the lack of standardized processing procedures for radical prostatectomy tissue specimens and the lack of methods to reliably quantify immunohistochemical staining. Manual interpretation is highly subjective and hampered by inter-and intraobserver variations.…”

Validation of the Prostate Cancer Risk Index (PRIX): A Simple Scoring System to Predict Risk of Biochemical Relapse after Radical Prostatectomy for Prostate Cancer

“…A scoring system such as the PRIX score offers easier calculation and application than those of the Stephenson nomogram but provides more detailed and accurate risk stratification data than those of the D'Amico risk classification. However, Shariat et al (10) have claimed that scoring systems such as that of the PRIX should be avoided because they assume that each variable exerts an equal prognostic weight on the outcome, which is unlikely to represent the true relation between variables and prognosis. …”