Antunes Alberto A, Srougi Miguel, Dall'Oglio Marcos F, Crippa Alexandre, Nesrallah Adriano J, Nesrallah Luciano J, Leite Katia R
Division of Urology, University of Sao Paulo Medical School, Sao Paulo, Brazil.
Int Braz J Urol. 2007 Jul-Aug;33(4):477-83; discussion 484-5. doi: 10.1590/s1677-55382007000400004.
Preoperative determination of prostate cancer (PCa) tumor volume (TV) is still a big challenge. We have assessed variables obtained in prostatic biopsy aiming at determining which is the best method to predict the TV in radical prostatectomy (RP) specimens.
Biopsy findings of 162 men with PCa submitted to radical prostatectomy were revised. Preoperative characteristics, such as PSA, the percentage of positive fragments (PPF), the total percentage of cancer in the biopsy (TPC), the maximum percentage of cancer in a fragment (MPC), the presence of perineural invasion (PNI) and the Gleason score were correlated with postoperative surgical findings through an univariate analysis of a linear regression model.
The TV correlated significantly to the PPF, TPC, MPC, PSA and to the presence of PNI (p < 0.001). However, the Pearson correlation analysis test showed an R2 of only 24%, 12%, 17% and 9% for the PPF, TPC, MPC, and PSA respectively. The combination of the PPF with the PSA and the PNI analysis showed to be a better model to predict the TV (R2 of 32.3%). The TV could be determined through the formula: Volume = 1.108 + 0.203 x PSA + 0.066 x PPF + 2.193 x PNI.
The PPF seems to be better than the TPC and the MPC to predict the TV in the surgical specimen. Due to the weak correlation between those variables and the TV, the PSA and the presence of PNI should be used together.
术前确定前列腺癌（PCa）肿瘤体积（TV）仍是一项巨大挑战。我们评估了前列腺活检中获得的变量，旨在确定哪种是预测根治性前列腺切除术（RP）标本中TV的最佳方法。
回顾了162例行根治性前列腺切除术的PCa男性患者的活检结果。通过线性回归模型的单变量分析，将术前特征，如前列腺特异性抗原（PSA）、阳性碎片百分比（PPF）、活检中癌症的总百分比（TPC）、碎片中癌症的最大百分比（MPC）、神经周围侵犯（PNI）的存在情况以及Gleason评分与术后手术结果进行关联。
TV与PPF、TPC、MPC、PSA以及PNI的存在显著相关（p < 0.001）。然而，Pearson相关分析测试显示，PPF、TPC、MPC和PSA的R²分别仅为24%、12%、17%和9%。PPF与PSA以及PNI分析的组合显示是预测TV的更好模型（R²为32.3%）。TV可通过以下公式确定：体积 = 1.108 + 0.203×PSA + 0.066×PPF + 2.193×PNI。
在预测手术标本中的TV方面，PPF似乎优于TPC和MPC。由于这些变量与TV之间的相关性较弱，应将PSA和PNI的存在情况一起使用。
