Development and validation of a Super learner-based model for predicting survival in Chinese Han patients with resected colorectal cancer. (29th June 2020)

• Record Type: Journal Article
• Authors: Li, Jiqing; Gu, Jianhua; Lu, Yuan; Wang, Xiaoqing; Si, Shucheng; Xue, Fuzhong
• Is Part Of: Japanese journal of clinical oncology. Volume 50:Number 10(2020)
• Page Start: 1133
• Page End: 1140
• Publication Date: 2020-06-29
• Subjects: colorectal cancer -- overall survival -- prognosis prediction -- The C-index of Super learner; Oncology -- Periodicals; Cancer -- Periodicals; 616.994005
• DOI: 10.1093/jjco/hyaa103
• Languages: English
• ISSNs: 0368-2811

Abstract

Abstract: Objective: 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. Methods: 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. Results: 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. Conclusions: 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. Abstract : We developed and externally validated a Super learner-based model for patients with Stage I–III colorectal cancer, which could provide the probability of death from colorectal cancer in patients with resected colorectal cancer.