Presentation Authors: Cosimo De Nunzio*, Antonio Cicione, Antonio Nacchia, Riccardo Lombardo, Alberto Trucchi, Andrea Tubaro, Rome, Italy
Introduction: To evaluate Metabolic syndrome (MetS) as a risk factor of Prostate cancer (PC) in patient undergone to repeat trans-rectal prostate biopsy (PB).
Methods: Between May 2010 and October 2018 a prospective cohort study was carried out by enrolling patients underwent to repeat biopsy for persistence of clinical suspicious of PC: PSAâ‰¥ 4 ng/ml, suspect digital rectal examination findings, more than three biopsy cores with High Grade Prostatic Intraepithelial Neoplasia (wHGPIN). PSAâ‰¥20 ng/ml was the main exclusion criteria. PB was performed by six months of the initial one. A 12-14 core prostate biopsy template was used in both biopsies. MetS was defined according to the Adult Treatment Panel III criteria. A binary logistic model was computed to assess risk factors of PC on re-biopsy. A nomogram was generated to predict PC. Accuracy was evaluated using the L-ROC
Results: Overall 309 patients aged 67.6Â±7.7 years were enrolled. Mean BMI was 27.5Â±3.4, mean prostate volume was 53.7Â±24.8cc. An initial diagnosis of chronic prostatitis, ASAP and HGPIN were respectively reported in 231 (74.8%), 9 (2.9%) and 69 (22.3%) patients, while 141 (37.0%) patients had a diagnosis of MS. Repeat biopsy diagnosed 96 (31.1%) cancers, 3 (1%) ASAP, 62 (20.1%) HGPIN, 142 (46.0%) benign lesions. Among cancers, 71 (74%) were Gleason Score (GS)=6 and 25 (26%) GSâ‰¥7. No significative difference was found regarding median PSA on initial and repeat biopsy, 6.9ng/ml IQR 5.2-8.2 Vs 6.6ng/ml IQR 4.3-11.2, p>0.05. On univariate analysis, MS (OR 1.6 CI 1.03-2.78, p=0.03), AGE (OR 1.05 CI 1.01-1.08, p=0.003) and wHGPIN (OR 3 CI 1.7-5.2, p=0.01) were the only independently risk factors of PC on repeat while PSA (0.9 0.82-0.98 p=0.21) and PSA density (OR 0.15 0.1-1.82 p=0.13) were not. MS diagnosis was the only risk factor able to predict a GS â‰¥7 cancer: OR 3 CI 1.17-8.07 p=0.02. On multivariate analysis the model including age, MS and wHGPIN was able to predict GS â‰¥7 (LROC 0.82) with a net benefit in the range of probability between 10% an 45% (figure 1).
Conclusions: In our patients MetS was an independent predictor of PC and particularly of high grade PC, confirming the importance of evaluating metabolic factors in patients at risk of prostate cancer. Our model if validated and could be used to reduce the number of unnecessary biopsy in patients with a previous negative biopsy.