Department of Urology, Mayo Clinic, Rochester, MN, USA.
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Eur Urol. 2018 May;73(5):772-780. doi: 10.1016/j.eururo.2018.01.005. Epub 2018 Feb 3.
Predicting oncologic outcomes is important for patient counseling, clinical trial design, and biomarker study testing.
To develop prognostic models for progression-free (PFS) and cancer-specific survival (CSS) in patients with clear cell renal cell carcinoma (ccRCC), papillary RCC (papRCC), and chromophobe RCC (chrRCC).
DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort review of the Mayo Clinic Nephrectomy registry from 1980 to 2010, for patients with nonmetastatic ccRCC, papRCC, and chrRCC.
Partial or radical nephrectomy.
PFS and CSS from date of surgery. Multivariable Cox proportional hazards regression was used to develop parsimonious models based on clinicopathologic features to predict oncologic outcomes and were evaluated with c-indexes. Models were converted into risk scores/groupings and used to predict PFS and CSS rates after accounting for competing risks.
A total of 3633 patients were identified, of whom 2726 (75%) had ccRCC, 607 (17%) had papRCC, and 222 (6%) had chrRCC. Models were generated for each histologic subtype and a risk score/grouping was developed for each subtype and outcome (PFS/CSS). For PFS, the c-indexes were 0.83, 0.77, and 0.78 for ccRCC, papRCC, and chrRCC, respectively. For CSS, c-indexes were 0.86 and 0.83 for ccRCC and papRCC. Due to only 22 deaths from RCC, we did not assess a multivariable model for chrRCC. Limitations include the single institution study, lack of external validation, and its retrospective nature.
Using a large institutional experience, we generated specific prognostic models for oncologic outcomes in ccRCC, papRCC, and chrRCC that rely on features previously shown-and validated-to be associated with survival. These updated models should inform patient prognosis, biomarker design, and clinical trial enrollment.
We identified routinely available clinical and pathologic features that can accurately predict progression and death from renal cell carcinoma following surgery. These updated models should inform patient prognosis, biomarker design, and clinical trial enrollment.
预测肿瘤学结果对于患者咨询、临床试验设计和生物标志物研究测试非常重要。
为透明细胞肾细胞癌（ccRCC）、乳头状肾细胞癌（papRCC）和嫌色细胞肾细胞癌（chrRCC）患者建立无进展生存期（PFS）和癌症特异性生存期（CSS）的预后模型。
设计、设置和参与者：回顾性队列研究来自 1980 年至 2010 年梅奥诊所肾切除术登记处的患者，这些患者患有非转移性 ccRCC、papRCC 和 chrRCC。
部分或根治性肾切除术。
从手术日期开始的 PFS 和 CSS。多变量 Cox 比例风险回归用于基于临床病理特征建立简洁的模型，以预测肿瘤学结果，并通过 C 指数进行评估。将模型转换为风险评分/分组，并用于在考虑竞争风险后预测 PFS 和 CSS 率。
共确定了 3633 名患者，其中 2726 名（75%）患有 ccRCC、607 名（17%）患有 papRCC 和 222 名（6%）患有 chrRCC。为每个组织学亚型生成了模型，并为每个亚型和结局（PFS/CSS）开发了风险评分/分组。对于 PFS，ccRCC、papRCC 和 chrRCC 的 C 指数分别为 0.83、0.77 和 0.78。对于 CSS，ccRCC 和 papRCC 的 C 指数分别为 0.86 和 0.83。由于仅有 22 例 RCC 死亡，我们没有评估 chrRCC 的多变量模型。局限性包括单机构研究、缺乏外部验证和回顾性。
使用大型机构经验，我们为 ccRCC、papRCC 和 chrRCC 的肿瘤学结果生成了特定的预后模型，这些模型依赖于先前显示并验证与生存相关的特征。这些更新的模型应告知患者预后、生物标志物设计和临床试验入组。
我们确定了常规可用的临床和病理特征，这些特征可以准确预测手术后肾细胞癌的进展和死亡。这些更新的模型应告知患者预后、生物标志物设计和临床试验入组。
