Department of Urology, Urological Science Institute, Yonsei University College of Medicine, Seoul, Korea.
Yonsei Med J. 2011 Jan;52(1):74-80. doi: 10.3349/ymj.2011.52.1.74.
Due to the availability of serum prostate specific antigen (PSA) testing, the detection rate of insignificant prostate cancer (IPC) is increasing. To ensure better treatment decisions, we developed a nomogram to predict the probability of IPC.
The study population consisted of 1,471 patients who were treated at multiple institutions by radical prostatectomy without neoadjuvant therapy from 1995 to 2008. We obtained nonrandom samples of n = 1,031 for nomogram development, leaving n = 440 for nomogram validation. IPC was defined as pathologic organ-confined disease and a tumor volume of 0.5 cc or less without Gleason grade 4 or 5. Multivariate logistic regression model (MLRM) coefficients were used to construct a nomogram to predict IPC from five variables, including serum prostate specific antigen, clinical stage, biopsy Gleason score, positive cores ratio and maximum % of tumor in any core. The performance characteristics were internally validated from 200 bootstrap resamples to reduce overfit bias. External validation was also performed in another cohort.
Overall, 67 (6.5%) patients had a so-called "insignificant" tumor in nomogram development cohort. PSA, clinical stage, biopsy Gleason score, positive core ratio and maximum % of biopsy tumor represented significant predictors of the presence of IPC. The resulting nomogram had excellent discrimination accuracy, with a bootstrapped concordance index of 0.827.
Our current nomogram provides sufficiently accurate information in clinical practice that may be useful to patients and clinicians when various treatment options for screen-detected prostate cancer are considered.
由于血清前列腺特异性抗原（PSA）检测的普及，前列腺癌（IPC）的检出率不断增加。为了确保更好的治疗决策，我们开发了一个列线图来预测 IPC 的概率。
该研究人群由 1995 年至 2008 年间在多个机构接受根治性前列腺切除术且未接受新辅助治疗的 1471 例患者组成。我们从非随机样本中获得了 n = 1031 例用于列线图开发，留下 n = 440 例用于列线图验证。IPC 定义为病理器官局限疾病且肿瘤体积为 0.5 cc 或更小，无 Gleason 分级 4 或 5。多变量逻辑回归模型（MLRM）系数用于构建一个列线图，从五个变量预测 IPC，包括血清前列腺特异性抗原、临床分期、活检 Gleason 评分、阳性核心比和任何核心中肿瘤的最大%。通过 200 次 bootstrap 重采样进行内部验证，以减少过度拟合偏差。还在另一个队列中进行了外部验证。
在列线图开发队列中，共有 67 例（6.5%）患者存在所谓的“不显著”肿瘤。PSA、临床分期、活检 Gleason 评分、阳性核心比和活检肿瘤最大%是 IPC 存在的显著预测因子。由此产生的列线图具有出色的判别准确性，bootstrap 一致性指数为 0.827。
我们目前的列线图在临床实践中提供了足够准确的信息，当考虑各种筛查发现的前列腺癌的治疗选择时，可能对患者和临床医生有用。
