Predictive and Prognostic Factors of Synchronous Colorectal Lung-Limited Metastasis

Yuqiang Li; Zhongyi Zhou; Da Liu; Ming Zhou; Fengbo Tan; Wenxue Liu; Hong Zhu

doi:10.1155/2020/6131485

Predictive and Prognostic Factors of Synchronous Colorectal Lung-Limited Metastasis

Gastroenterol Res Pract. 2020 Nov 23:2020:6131485. doi: 10.1155/2020/6131485. eCollection 2020.

Authors

Yuqiang Li¹, Zhongyi Zhou¹, Da Liu², Ming Zhou², Fengbo Tan¹, Wenxue Liu^{3

4}, Hong Zhu⁵

Affiliations

¹ Department of Gastrointestinal Surgery, Xiangya Hospital, Central South University, Changsha, China.
² Department of Thoracic Surgery 3 Zone, Cancer Center of Guangzhou Medical University, Guangzhou, China.
³ Department of Rheumatology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
⁴ Department of Cardiology, Xiangya Hospital, Central South University, Changsha, China.
⁵ Department of Radiotherapy, Xiangya Hospital, Central South University, Changsha, China.

Abstract

Aim: This study is aimed at investigating predictive and prognostic factors of synchronous colorectal lung-limited metastasis (SCLLM) based on The Surveillance, Epidemiology, and End Results (SEER) database.

Methods: A multivariate logistic regression model was constructed to identify independent predictors of SCLLM. A multivariate Cox proportional hazards regression model was used to distinguish independent prognostic factors.

Results: This study enrolled 168,007 colorectal cancer (CRC) patients without metastatic diseases and 1,298 cases with SCLLM. Eight features, involving race, tumor location, pathological grade, histological type, T stage, N stage, and tumor size as well as CEA, could be used as the independent predictors. As the nomogram shown, the T4 stage contributed the most to SCLLM, followed by the N2 stage, elevated CEA, and rectal cancer. A multivariate regression analysis discriminated 9 independent prognostic factors, including age, race, marital status, pathological grade, T stage, colectomy/proctectomy, chemotherapy, CEA, and TD. The prognostic nomogram illustrated that nonresection/NOS played as the poorest prognostic factor, followed by nonchemotherapy, ≥75-year old and T4 stage. The cumulative survival curves revealed the influence of each prognostic factor on survival after controlling the other variables.

Conclusions: This study identified independent predictors and prognostic factors for SCLLM based on a large database of the United States. The predictors and prognostic factors can provide supporting evidence for the prevention and treatment of SCLLM.