Department of Gastrointestinal Surgery, Xiangya Hospital, Central South University, Changsha, China.
Department of Rheumatology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Aging (Albany NY). 2020 Dec 3;13(1):477-492. doi: 10.18632/aging.202157.
The incidence of colorectal cancer in patients younger than 50 years has been increasing in recent years.
Develop and validate prognostic nomograms predicting overall survival (OS) and cancer-specific survival (CSS) for early-onset locally advanced colon cancer (EOLACC) based on the Surveillance, Epidemiology, and End Results (SEER) database.
The entire cohort comprised 13,755 patients with EOLACC. The nomogram predicting OS for EOLACC displayed that T stage contributed the most to prognosis, followed by N stage, regional nodes examined (RNE) and surgery. The nomogram predicting CSS for EOLACC demonstrated similar results. Various methods identified the discriminating superiority of the nomograms. X-tile software was used to classify patients into high-risk, medium-risk, and low-risk according to the risk score of the nomograms. The risk stratification effectively avoided the survival paradox.
We established and validated nomograms for predicting OS and CSS based on a national cohort of almost 13,000 EOLACC patients. The nomograms could effectively solve the issue of survival paradox of the AJCC staging system and be an excellent tool to integrate the clinical characteristics to guide the therapeutic choice for EOLACC patients.
Nomograms were constructed based on the SEER database and the Cox regression model.
近年来,50 岁以下结直肠癌患者的发病率呈上升趋势。
基于监测、流行病学和最终结果(SEER)数据库,开发并验证预测早期局部晚期结肠癌(EOLACC)总生存(OS)和癌症特异性生存(CSS)的预后列线图。
整个队列包括 13755 例 EOLACC 患者。预测 EOLACC OS 的列线图显示,T 分期对预后的贡献最大,其次是 N 分期、区域淋巴结检查(RNE)和手术。预测 EOLACC CSS 的列线图也显示了类似的结果。各种方法均证实了列线图的判别优势。X-tile 软件用于根据列线图的风险评分将患者分为高危、中危和低危。风险分层有效地避免了生存悖论。
我们基于近 13000 例 EOLACC 患者的全国队列建立并验证了预测 OS 和 CSS 的列线图。这些列线图可以有效地解决 AJCC 分期系统的生存悖论问题,并成为整合临床特征以指导 EOLACC 患者治疗选择的优秀工具。
列线图基于 SEER 数据库和 Cox 回归模型构建。