Roberts W W, Bergstralh E J, Blute M L, Slezak J M, Carducci M, Han M, Epstein J I, Eisenberger M A, Walsh P C, Partin A W
James Buchanan Brady Urological Institute, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA.
Urology. 2001 Jun;57(6):1033-7. doi: 10.1016/s0090-4295(01)00978-5.
To develop a model that will identify a contemporary cohort of patients at high risk of early prostate cancer recurrence (greater than 50% at 36 months) after radical retropubic prostatectomy for clinically localized disease. Data from this model will provide important information for patient selection and the design of prospective randomized trials of adjuvant therapies.
Proportional hazards regression analysis was applied to two patient cohorts to develop and cross-validate a multifactorial predictive model to identify men with the highest risk of early prostate cancer recurrence. The model and validation cohorts contained 904 and 901 men, respectively, who underwent radical retropubic prostatectomy at Johns Hopkins Hospital. This model was then externally validated using a cohort of patients from the Mayo Clinic.
A model for weighted risk of recurrence was developed: R(W)'=lymph node involvement (0/1)x1.43+surgical margin status (0/1)x1.15+modified Gleason score (0 to 4)x0.71+seminal vesicle involvement (0/1)x0.51. Men with an R(W)' greater than 2.84 (9%) demonstrated a 50% biochemical recurrence rate (prostrate-specific antigen level greater than 0.2 ng/mL) at 3 years and thus were placed in the high-risk group. Kaplan-Meier analyses of biochemical recurrence-free survival demonstrated rapid deviation of the curves based on the R(W)'. This model was cross-validated in the second group of patients and performed with similar results. Furthermore, similar trends were apparent when the model was externally validated on patients treated at the Mayo Clinic.
We have developed a multivariate Cox proportional hazards model that successfully stratifies patients on the basis of their risk of early prostate cancer recurrence.
建立一个模型,用于识别当代一组在接受根治性耻骨后前列腺切除术治疗临床局限性疾病后早期前列腺癌复发风险较高(36个月时大于50%)的患者。该模型的数据将为患者选择和辅助治疗前瞻性随机试验的设计提供重要信息。
对两个患者队列应用比例风险回归分析,以建立和交叉验证一个多因素预测模型,以识别早期前列腺癌复发风险最高的男性。模型队列和验证队列分别包含904名和901名在约翰霍普金斯医院接受根治性耻骨后前列腺切除术的男性。然后使用梅奥诊所的一组患者对该模型进行外部验证。
建立了一个复发加权风险模型:R(W)'=淋巴结受累情况(0/1)×1.43+手术切缘状态(0/1)×1.15+改良Gleason评分(0至4)×0.71+精囊受累情况(0/1)×0.51。R(W)'大于2.84(9%)的男性在3年时生化复发率为50%(前列腺特异性抗原水平大于0.2 ng/mL),因此被归入高危组。对无生化复发生存期的Kaplan-Meier分析表明,基于R(W)',曲线迅速出现偏差。该模型在第二组患者中进行了交叉验证,结果相似。此外,当该模型在梅奥诊所接受治疗的患者中进行外部验证时,也出现了类似趋势。
我们开发了一个多变量Cox比例风险模型,该模型成功地根据患者早期前列腺癌复发风险对其进行了分层。