Han M, Snow P B, Epstein J I, Chan T Y, Jones K A, Walsh P C, Partin A W
James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA.
Urology. 2000 Dec 20;56(6):994-9. doi: 10.1016/s0090-4295(00)00815-3.
To determine the significance of Gleason scores 3+4 (GS3+4) versus 4+3 (GS4+3) with respect to biochemical recurrence in a retrospective review of a series of men with clinically localized prostate cancer who underwent radical retropubic prostatectomy (RRP) and to develop and test an artificial neural network (ANN) to predict the biochemical recurrence after surgery for this group of men using the pathologic and clinical data.
From 1982 to 1998, 600 men had pathologic Gleason score 7 disease without lymph node or seminal vesicle involvement. We analyzed the freedom from biochemical (prostate-specific antigen) progression after RRP on 564 of these men on the basis of their GS3+4 versus GS4+3 (Gleason 7) status. The Cox proportional hazards model was used to determine the importance of Gleason 7 status as an independent predictor of progression. In addition, an ANN was developed using randomly selected training and validation sets for predicting biochemical recurrence at 3 or 5 years. Different input variable subsets, with or without Gleason 7 status, were compared for the ability of the ANN to maximize the prediction of progression. Standard logistic regression was used concurrently on the same random patient population sets to calculate progression risk.
A significant recurrence-free survival advantage was found in men who underwent RRP for GS3+4 compared with those with GS4+3 disease (P <0.0001). The ANN, logistic regression, and proportion hazard models demonstrated the importance of Gleason 7 status in predicting patient outcome. The ANN was better than logistic regression in predicting patient outcome, in terms of prostate-specific antigen progression, at 3 and 5 years.
A simple modification of the Gleason scoring system for men with Gleason 7 disease revealed a difference in the patient outcome after RRP. ANN models can be developed and used to better predict patient outcome when pathologic and clinical features are known.
在对一系列接受根治性耻骨后前列腺切除术（RRP）的临床局限性前列腺癌男性患者进行回顾性研究中，确定 Gleason 评分 3 + 4（GS3 + 4）与 4 + 3（GS4 + 3）在生化复发方面的意义，并开发和测试一个人工神经网络（ANN），使用病理和临床数据预测该组男性患者术后的生化复发情况。
1982 年至 1998 年期间，600 名男性患者患有病理 Gleason 评分 7 分的疾病，且无淋巴结或精囊受累。我们根据这些患者的 GS3 + 4 与 GS4 + 3（Gleason 7）状态，分析了其中 564 名患者 RRP 后无生化（前列腺特异性抗原）进展的情况。采用 Cox 比例风险模型确定 Gleason 7 状态作为进展独立预测因子的重要性。此外，开发了一个 ANN，使用随机选择的训练集和验证集来预测 3 年或 5 年时的生化复发情况。比较了不同输入变量子集（有无 Gleason 7 状态），以评估 ANN 最大化进展预测的能力。同时在相同的随机患者人群集上使用标准逻辑回归来计算进展风险。
与 GS4 + 3 疾病患者相比，接受 RRP 的 GS3 + 4 男性患者具有显著的无复发生存优势（P < 0.0001）。ANN、逻辑回归和比例风险模型均证明了 Gleason 7 状态在预测患者预后方面的重要性。就前列腺特异性抗原进展而言，在 3 年和 5 年时，ANN 在预测患者预后方面优于逻辑回归。
对 Gleason 7 疾病男性患者的 Gleason 评分系统进行简单修改后，发现 RRP 后患者的预后存在差异。当已知病理和临床特征时，可以开发并使用 ANN 模型来更好地预测患者预后。
