Wang Xiao-Xuan, Zhou Yu-Wen, Wang Bo, Cao Peng, Luo De-Yun, Li Chun-Hong, Wang Kai, Qiu Meng
Colorectal Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Department of Abdominal Cancer, Cancer Center, Shang Jin Hospital of West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Therap Adv Gastroenterol. 2024 Oct 8;17:17562848241284229. doi: 10.1177/17562848241284229. eCollection 2024.
Fruquintinib is a third-line and subsequent targeted therapy for patients with metastatic colorectal cancer (mCRC). Identifying survival predictors after fruquintinib is crucial for optimizing the clinical use of this medication.
We aimed to identify factors influencing the prognosis of patients with mCRC treated with fruquintinib and to leverage these insights to develop a nomogram model for estimating survival rates in this patient population.
Multicenter retrospective observational study.
We collected patient data from January 2019 to October 2023, with one healthcare institution's data serving as the training cohort and the other three hospitals' data serving as the multicenter validation cohort. The nomogram for overall survival was calculated from Cox regression models, and variable selection was screened using the univariate Cox regression analysis with additional variables based on clinical experience. Model performance was measured by the concordance index (C-index), calibration curves, decision curve analyses (DCA), and utility (patient stratification into low-risk vs high-risk groups).
Data were ultimately collected on 240 patients, with 144 patients included in the training cohort and 96 included in the multicenter validation cohort. Predictors included in the nomogram were CA199, body mass index, T stage, the primary site of the tumor, and other metastatic and pathological differentiation. The C-index of the nomogram in the training set and multicenter validation was 0.714 and 0.729, respectively. The models were fully calibrated and their predictions aligned closely with the observed data. DCA curves indicated the promising clinical benefits of the predictive model. Finally, the reliability of the model was also verified through the risk classification using the nomogram.
We constructed a nomogram for mCRC treated with fruquintinib based on six variables that may be used to assist in personalizing the use of the drug.
呋喹替尼是转移性结直肠癌(mCRC)患者的三线及后续靶向治疗药物。确定呋喹替尼治疗后的生存预测因素对于优化该药物的临床应用至关重要。
我们旨在确定影响接受呋喹替尼治疗的mCRC患者预后的因素,并利用这些见解开发一个列线图模型,以估计该患者群体的生存率。
多中心回顾性观察研究。
我们收集了2019年1月至2023年10月的患者数据,其中一家医疗机构的数据作为训练队列,其他三家医院的数据作为多中心验证队列。通过Cox回归模型计算总生存列线图,并使用单变量Cox回归分析并基于临床经验添加其他变量进行变量筛选。通过一致性指数(C指数)、校准曲线、决策曲线分析(DCA)和效用(将患者分层为低风险与高风险组)来衡量模型性能。
最终收集了240例患者的数据,其中144例患者纳入训练队列,96例纳入多中心验证队列。列线图中纳入的预测因素包括CA199、体重指数、T分期、肿瘤原发部位以及其他转移情况和病理分化。训练集和多中心验证中列线图的C指数分别为0.714和0.729。模型校准良好,其预测与观察数据紧密吻合。DCA曲线表明预测模型具有良好的临床效益。最后,通过使用列线图进行风险分类也验证了模型的可靠性。
我们基于六个变量构建了接受呋喹替尼治疗的mCRC列线图,可用于协助该药物的个体化使用。