Premium
Predicting the outcome of prostate biopsy: comparison of a novel logistic regression‐based model, the prostate cancer risk calculator, and prostate‐specific antigen level alone
Author(s) -
Hernandez David J.,
Han Misop,
Humphreys Elizabeth B.,
Mangold Leslie A.,
Taneja Samir S.,
Childs Stacy J.,
Bartsch Georg,
Partin Alan W.
Publication year - 2009
Publication title -
bju international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 148
eISSN - 1464-410X
pISSN - 1464-4096
DOI - 10.1111/j.1464-410x.2008.08127.x
Subject(s) - medicine , prostate cancer , logistic regression , receiver operating characteristic , rectal examination , prostate specific antigen , prostate biopsy , prostate , biopsy , cancer , oncology , area under the curve , gynecology , urology
OBJECTIVES To develop a logistic regression‐based model to predict prostate cancer biopsy at, and compare its performance to the risk calculator developed by the Prostate Cancer Prevention Trial (PCPT), which was based on age, race, prostate‐specific antigen (PSA) level, a digital rectal examination (DRE), family history, and history of a previous negative biopsy, and to PSA level alone. PATIENTS AND METHODS We retrospectively analysed the data of 1280 men who had a biopsy while enrolled in a prospective, multicentre clinical trial. Of these, 1108 had all relevant clinical and pathological data available, and no previous diagnosis of prostate cancer. Using the PCPT risk calculator, we calculated the risks of prostate cancer and of high‐grade disease (Gleason score ≥7) for each man. Receiver operating characteristic (ROC) curves for the risk calculator, PSA level and the novel regression‐based model were compared. RESULTS Prostate cancer was detected in 394 (35.6%) men, and 155 (14.0%) had Gleason ≥7 disease. For cancer prediction, the area under the ROC curve (AUC) for the risk calculator was 66.7%, statistically greater than the AUC for PSA level of 61.9% ( P < 0.001). For predicting high‐grade disease, the AUCs were 74.1% and 70.7% for the risk calculator and PSA level, respectively ( P = 0.024). The AUCs increased to 71.2% ( P < 0.001) and 78.7% ( P = 0.001) for detection and high‐grade disease, respectively, with our novel regression‐based models. CONCLUSIONS ROC analyses show that the PCPT risk calculator modestly improves the performance of PSA level alone in predicting an individual’s risk of prostate cancer or high‐grade disease on biopsy. This predictive tool might be enhanced by including percentage free PSA and the number of biopsy cores.