Department of Urology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX.
Urol Oncol. 2019 Apr;37(4):292.e1-292.e9. doi: 10.1016/j.urolonc.2018.12.002. Epub 2018 Dec 22.
Accurate risk stratification prior to radical nephroureterectomy remains a challenge with upper-tract urothelial carcinoma (UTUC). Herein, we generated an optimized preoperative tool predicting high-risk nonorgan-confined (NOC)-UTUC.
Retrospective evaluation of 699 patients undergoing radical nephroureterectomy at 3 academic centers. Multiplex preoperative patient, imaging, endoscopic, and laboratory values were evaluated. Model derivation and validation were based on a split-sample method. Patients were divided randomly into a development (training) cohort (70% of patients) and validation (test) cohort (30% of patients). Univariate and multivariate logistic regression addressed the prediction of NOC disease (pT3/pT4 and/or pN+) based on training cohort. A backward stepdown selection process achieved the most informative nomogram. The ROC analysis identified a cut-off point predicting high-risk disease. The test cohort served as "external" validation to verify the findings based on the training cohort. Bootstrap resampling was conducted for both internal and "external" validation to evaluate the model fitting.
Total of 566 patients included for analysis, mean age 69.7 years, 85% Caucasian, 64% male, 62% high grade. NOC-UTUC was found in 184 (32.5%) patients on final pathology. Of 184 patients with NOC-UTUC, an equal number of renal pelvis and ureter only tumors (n = 74; 40.2% for each location) were noted; 36 (19.6%) had tumors in both locations. Multivariate model based on development cohort (n = 396) demonstrated clinical stage (odds ratio [OR] 14.0, P < 0.01), biopsy tumor grade (OR 3.3, P = 0.01), tumor architecture (OR 2.65, P = 0.09), and Hgb (OR 0.8, P = 0.02) level were independently associated with NOC disease. A preoperative nomogram incorporating these 4 variables achieved 82% accuracy, 48% sensitivity, and 95% specificity in predicting NOC-UTUC. The cut-off point for predicting high-risk disease was ≥0.49.
We established and validated an accurate tool for the prediction of locally advanced NOC-UTUC. This preoperative nomogram can be used to more optimally select patients for preoperative systemic chemotherapy, and facilitate clinical trial enrollment.
在上尿路尿路上皮癌(UTUC)中,根治性肾输尿管切除术前进行准确的风险分层仍然是一个挑战。在此,我们生成了一种优化的术前工具,用于预测高危非器官局限性(NOC)-UTUC。
回顾性分析了在 3 个学术中心接受根治性肾输尿管切除术的 699 例患者。评估了多重术前患者、影像学、内镜和实验室值。模型推导和验证基于拆分样本方法。患者随机分为发展(训练)队列(70%的患者)和验证(测试)队列(30%的患者)。单变量和多变量逻辑回归根据训练队列评估 NOC 疾病(pT3/pT4 和/或 pN+)的预测。向后逐步选择过程获得了最具信息量的列线图。ROC 分析确定了预测高危疾病的截断值。测试队列用作基于训练队列的“外部”验证,以验证结果。bootstrap 重采样用于内部和“外部”验证,以评估模型拟合度。
共分析了 566 例患者,平均年龄 69.7 岁,85%为白种人,64%为男性,62%为高级别。最终病理发现 184 例(32.5%)患者存在 NOC-UTUC。在 184 例 NOC-UTUC 患者中,肾盂和输尿管仅肿瘤的数量相等(n=74;各部位各占 40.2%);36 例(19.6%)肿瘤位于两个部位。基于发展队列(n=396)的多变量模型显示临床分期(优势比[OR] 14.0,P<0.01)、活检肿瘤分级(OR 3.3,P=0.01)、肿瘤结构(OR 2.65,P=0.09)和 Hgb(OR 0.8,P=0.02)水平与 NOC 疾病独立相关。纳入这 4 个变量的术前列线图在预测 NOC-UTUC 中达到 82%的准确性、48%的敏感性和 95%的特异性。预测高危疾病的截断值为≥0.49。
我们建立并验证了一种预测局部晚期 NOC-UTUC 的准确工具。这种术前列线图可用于更优化地选择接受术前全身化疗的患者,并促进临床试验的入组。