Division of Urology, Department of Surgical Oncology, Princess Margaret Hospital, University Health Network, Toronto, Canada.
J Urol. 2012 Mar;187(3):840-4. doi: 10.1016/j.juro.2011.10.148. Epub 2012 Jan 15.
The role of neoadjuvant chemotherapy before surgery in patients with muscle invasive bladder cancer remains debated and the need for tools to identify patients who would benefit from chemotherapy is pertinent. We previously published a preoperative algorithm to predict nonorgan confined disease. This algorithm included tumor markers (CEA, CA 125 and CA 19-9) as well as clinical parameters. In this study we validated the accuracy of this algorithm in an independent, external cohort.
We used the Toronto Biobank to measure preoperative serum levels of CEA, CA 125 and CA 19-9 in 76 consecutive patients with clinically organ confined bladder cancer (cT2 or less) who underwent radical cystectomy. Clinical parameters were retrieved from our prospective bladder information system database and incorporated into our marker based algorithm. A numerical score was generated for each patient and a previously published cutoff was used to predict the presence of nonorgan confined disease. The accuracy of the model was quantified with the area under the curve, and the positive and negative predictive values were calculated.
On pathological evaluation 38 patients (50%) had nonorgan confined tumors. The AUC of the algorithm was 0.79 (95% CI 0.69-0.89). The positive and negative predictive values were 79% (95% CI 71-87) and 74% (95% CI 66-82), respectively.
We externally validated a pre-cystectomy model to predict pathological stage. The algorithm may possibly aid in selecting patients who would benefit from neoadjuvant chemotherapy before cystectomy.
术前新辅助化疗在肌层浸润性膀胱癌患者中的作用仍存在争议，因此需要有工具来识别可能从化疗中获益的患者。我们之前发表了一种术前算法来预测非器官局限疾病。该算法包括肿瘤标志物（CEA、CA125 和 CA19-9）以及临床参数。在本研究中，我们在一个独立的外部队列中验证了该算法的准确性。
我们使用多伦多生物库测量了 76 例连续的临床器官局限膀胱癌（cT2 或更低）患者术前血清 CEA、CA125 和 CA19-9 的水平，这些患者接受了根治性膀胱切除术。临床参数从我们前瞻性的膀胱信息系统数据库中检索，并纳入到我们基于标志物的算法中。为每个患者生成一个数字评分，并使用之前发表的截定点来预测非器官局限疾病的存在。使用曲线下面积来量化模型的准确性，并计算阳性和阴性预测值。
在病理评估中，38 例患者（50%）存在非器官局限肿瘤。该算法的 AUC 为 0.79（95%CI 0.69-0.89）。阳性和阴性预测值分别为 79%（95%CI 71-87）和 74%（95%CI 66-82）。
我们对外验证了一种预测病理分期的术前模型。该算法可能有助于选择在接受根治性膀胱切除术之前可能从新辅助化疗中获益的患者。
