Objectives: Clinical practice guidelines endorse the stratification of prostate cancer (PCa) risk according to individual total prostate-specific antigen (tPSA) values and age to enhance the individual risk-benefit ratio. We defined two nomograms to predict the individual risk of high and low grade PCa by combining the assay of tPSA and %free/tPSA (%f/tPSA) in patients with a pre-biopsy tPSA between 2 and 10 μg/L. Methods: The study cohort consisted of 662 patients that had fPSA, tPSA, and a biopsy performed (41.3% with a final diagnosis of PCa). Logistic regression including age, tPSA and %f/tPSA was used to model the probability of having high or low grade cancer by defining 3 outcome levels: no PCa, low grade (International Society of Urological Pathology grade, ISUP<3) and high grade PCa (ISUP≥3). Results: The nomogram identifying patients with: (a) high vs. those with low grade PCa and without the disease showed a good discriminating capability (∼80%), but the calibration showed a risk of underestimation for predictive probabilities >30% (a considerable critical threshold of risk), (b) ISUP<3 vs. those without the disease showed a discriminating capability of 63% and overestimates predictive probabilities >50%. In ISUP 5 a possible loss of PSA immunoreactivity has been observed. Conclusions: The estimated risk of high or low grade PCa by the nomograms may be of aid in the decision-making process, in particular in the case of critical comorbidities and when the digital rectal examinations are inconclusive. The improved characterization of the risk of ISUP≥3 might enhance the use for magnetic resonance imaging in this setting.

Individual risk prediction of high grade prostate cancer based on the combination between total prostate-specific antigen (PSA) and free to total PSA ratio / S. Ferraro, D. Biganzoli, R.S. Rossi, F. Palmisano, M. Bussetti, E. Verzotti, A. Gregori, F. Bianchi, M. Maggioni, F. Ceriotti, C. Cereda, G. Zuccotti, P. Kavsak, M. Plebani, G. Marano, E.M. Biganzoli. - In: CLINICAL CHEMISTRY AND LABORATORY MEDICINE. - ISSN 1434-6621. - 61:7(2023), pp. 1327-1334. [10.1515/cclm-2023-0008]

Individual risk prediction of high grade prostate cancer based on the combination between total prostate-specific antigen (PSA) and free to total PSA ratio

F. Palmisano;M. Bussetti;A. Gregori;G. Zuccotti;G. Marano
Penultimo
;
E.M. Biganzoli
Ultimo
2023

Abstract

Objectives: Clinical practice guidelines endorse the stratification of prostate cancer (PCa) risk according to individual total prostate-specific antigen (tPSA) values and age to enhance the individual risk-benefit ratio. We defined two nomograms to predict the individual risk of high and low grade PCa by combining the assay of tPSA and %free/tPSA (%f/tPSA) in patients with a pre-biopsy tPSA between 2 and 10 μg/L. Methods: The study cohort consisted of 662 patients that had fPSA, tPSA, and a biopsy performed (41.3% with a final diagnosis of PCa). Logistic regression including age, tPSA and %f/tPSA was used to model the probability of having high or low grade cancer by defining 3 outcome levels: no PCa, low grade (International Society of Urological Pathology grade, ISUP<3) and high grade PCa (ISUP≥3). Results: The nomogram identifying patients with: (a) high vs. those with low grade PCa and without the disease showed a good discriminating capability (∼80%), but the calibration showed a risk of underestimation for predictive probabilities >30% (a considerable critical threshold of risk), (b) ISUP<3 vs. those without the disease showed a discriminating capability of 63% and overestimates predictive probabilities >50%. In ISUP 5 a possible loss of PSA immunoreactivity has been observed. Conclusions: The estimated risk of high or low grade PCa by the nomograms may be of aid in the decision-making process, in particular in the case of critical comorbidities and when the digital rectal examinations are inconclusive. The improved characterization of the risk of ISUP≥3 might enhance the use for magnetic resonance imaging in this setting.
high grade; nomogram; risk; risk-benefit ratio; rule-in
Settore MED/01 - Statistica Medica
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1004710
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