Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
Institute for Medical Dataology, Shandong University, Jinan, China.
Jpn J Clin Oncol. 2020 Sep 28;50(10):1133-1140. doi: 10.1093/jjco/hyaa103.
Improved prognostic prediction for patients with colorectal cancer stays an important challenge. This study aimed to develop an effective prognostic model for predicting survival in resected colorectal cancer patients through the implementation of the Super learner.
A total of 2333 patients who met the inclusion criteria were enrolled in the cohort. We used multivariate Cox regression analysis to identify significant prognostic factors and Super learner to construct prognostic models. Prediction models were internally validated by 10-fold cross-validation and externally validated with a dataset from The Cancer Genome Atlas. Discrimination and calibration were evaluated by Harrell concordence index (C-index) and calibration plots, respectively.
Age, T stage, N stage, histological type, tumor location, lymph-vascular invasion, preoperative carcinoembryonic antigen and sample lymph nodes were integrated into prediction models. The concordance index of Super learner-based prediction model (SLM) was 0.792 (95% confidence interval: 0.767-0.818), which is higher than that of the seventh edition American Joint Committee on Cancer TNM staging system 0.689 (95% confidence interval: 0.672-0.703) for predicting overall survival (P < 0.05). In the external validation, the concordance index of the SLM for predicting overall survival was also higher than that of tumor-node-metastasis (TNM) stage system (0.764 vs. 0.682, respectively; P < 0.001). In addition, the SLM showed good calibration properties.
We developed and externally validated an effective prognosis prediction model based on Super learner, which offered more reliable and accurate prognosis prediction and may be used to more accurately identify high-risk patients who need more active surveillance in patients with resected colorectal cancer.
提高结直肠癌患者的预后预测仍然是一个重要的挑战。本研究旨在通过实施 Super learner 为接受结直肠癌切除术的患者建立一种有效的生存预后预测模型。
本研究纳入了 2333 名符合纳入标准的患者。我们使用多变量 Cox 回归分析来识别显著的预后因素，并通过 Super learner 构建预后模型。通过 10 折交叉验证对预测模型进行内部验证，并使用来自癌症基因组图谱的数据集进行外部验证。通过 Harrell 一致性指数（C-index）和校准图分别评估区分度和校准度。
年龄、T 分期、N 分期、组织学类型、肿瘤位置、淋巴血管侵犯、术前癌胚抗原和样本淋巴结被整合到预测模型中。基于 Super learner 的预测模型（SLM）的一致性指数为 0.792（95%置信区间：0.767-0.818），高于第七版美国癌症联合委员会 TNM 分期系统的 0.689（95%置信区间：0.672-0.703），用于预测总生存（P<0.05）。在外部验证中，SLM 预测总生存的一致性指数也高于 TNM 分期系统（0.764 与 0.682，分别；P<0.001）。此外，SLM 表现出良好的校准特性。
我们开发并外部验证了一种基于 Super learner 的有效预后预测模型，该模型提供了更可靠和准确的预后预测，可能用于更准确地识别需要更积极监测的高风险患者。
