Nomogram for predicting overall survival in stage II‐III colorectal cancer

列线图 结直肠癌 阶段(地层学) 肿瘤科 医学 内科学 总体生存率 生存分析 癌症 生物 古生物学
作者
Jungang Liu,Xiaoliang Huang,Wenkang Yang,Chan Li,Zhengtian Li,Chuqiao Zhang,Shaomei Chen,Guan-Ling Wu,Weishun Xie,Chunyin Wei,Chao Tian,Lingxu Huang,Franco Jeen,Xianwei Mo,Weizhong Tang
出处
期刊:Cancer Medicine [Wiley]
卷期号:9 (7): 2363-2371 被引量:54
标识
DOI:10.1002/cam4.2896
摘要

The overall survival (OS) of patients diagnosed with stage II-III colorectal cancer (CRC) can vary greatly, even between patients with the same tumor stage. We aimed to design a nomogram to predict OS in resected, stage II-III CRC and stratify patients with CRC into different risk groups.Based on data from 873 patients with CRC, we used univariate Cox regression analysis to select the significant prognostic features, which were subjected to the least absolute shrinkage and selection operator (LASSO) regression algorithm for feature selection. Cross-validation was used to confirm suitable tuning parameters (λ) for LASSO logistic regression. Then, the nomogram was used to estimate 3- and 5-year OS based on the multivariable Cox regression model. The survival curves of the two groups were produced using the Kaplan-Meier method. Risk group stratification was performed to assess the predictive capacity of the nomogram.Preoperative mean platelet volume, preoperative platelet distribution width, monocytes, and postoperative adjuvant chemotherapy were identified as independent prognostic factors by LASSO regression and integrated for the construction of the nomogram. The nomogram provided good discrimination, with C-indices of 0.67 and 0.69 for the training and validation sets, respectively. Calibration plots illustrated excellent agreement between the nomogram predictions and actual observations for 3- and 5-year OS. Moreover, a significant difference in OS was shown between patients stratified into different risk groups (P < .001).We constructed and validated an original predictive nomogram for OS in patients with CRC after surgery, facilitating physicians to appraise the individual survival of postoperative patients accurately and identify high-risk patients who need more aggressive treatment and follow-up strategies.

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