Prostate cancer detection in the "grey area" of prostate-specific antigen below 10 ng/ml: head-to-head comparison of the updated PCPT calculator and Chun's nomogram, two risk estimators incorporating prostate cancer antigen 3.
BACKGROUND
Prostate cancer antigen 3 (PCA3) holds promise in diagnosing prostate cancer (PCa), but no consensus has been reached on its clinical use. Multivariable predictive models have shown increased accuracy over individual risk factors.


OBJECTIVE
To compare the performance of the two available risk estimators incorporating PCA3 in the detection of PCa in the "grey area" of prostate-specific antigen (PSA) <10 ng/ml: the updated Prostate Cancer Prevention Trial (PCPT) calculator and Chun's nomogram.


METHODS
Two hundred eighteen patients presenting with an abnormal PSA (excluding those with PSA >10 ng/ml) and/or abnormal digital rectal examination were prospectively enrolled in a multicentre Italian study between October 2008 and October 2009. All patients underwent ≥12-core prostate biopsy.


METHODS
PCA3 scores were assessed using the Progensa assay (Gen-Probe, San Diego, CA, USA). Comparisons between the two models were performed using tests of accuracy (area under the receiver operating characteristic curve [AUC-ROC]), calibration plots, and decision curve analysis. Biopsy predictors were identified by univariable and multivariable logistic regression. In addition, performance of PCA3 was analysed through AUC-ROC and predictive values.


CONCLUSIONS
PCa was detected in 73 patients (33.5%). Among predictors included in the models, only PCA3, PSA, and prostate volume retained significant predictive value. AUC-ROC was higher for the updated PCPT calculator compared to Chun's nomogram (79.6% vs 71.5%; p=0.043); however, Chun's nomogram displayed better overall calibration and a higher net benefit on decision curve analysis. Using a probability threshold of 25%, no high-grade cancers would be missed; the PCPT calculator would save 11% of biopsies, missing no cancer, whereas Chun's nomogram would save 22% of avoidable biopsies, although missing 4.1% non-high-grade cancers. The small number of patients may account for the lack of statistical significance in the predictive value of individual variables or model comparison.


CONCLUSIONS
Both Chun's nomogram and the PCPT calculator, by incorporating PCA3, can assist in the decision to biopsy by assignment of an individual risk of PCa, specifically in the PSA levels <10 ng/ml.
Perdonà
Sisto
S
Istituto Nazionale Tumori, Fondazione G. Pascale, Napoli, Italy.
Cavadas
Vitor
V
Di Lorenzo
Giuseppe
G
Damiano
Rocco
R
Chiappetta
Gennaro
G
Del Prete
Paola
P
Franco
Renato
R
Azzarito
Giuseppina
G
Scala
Stefania
S
Arra
Claudio
C
De Sio
Marco
M
Autorino
Riccardo
R
D000368:Aged; D000951:Antigens, Neoplasm / Q000235:genetics / Q000652:urine*; D001706:Biopsy; D016009:Chi-Square Distribution; D051517:Digital Rectal Examination; D006801:Humans; D007558:Italy; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D049451:Nomograms; D016017:Odds Ratio; D011237:Predictive Value of Tests; D011446:Prospective Studies; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000276:immunology / Q000473:pathology; D012333:RNA, Messenger / Q000652:urine; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D047368:Tumor Burden
D000951:Antigens, Neoplasm; D012333:RNA, Messenger; C479341:prostate cancer antigen 3, human; D017430:Prostate-Specific Antigen