A Prognostic Nomogram of Colon Cancer With Liver Metastasis: A Study of the US SEER Database and a Chinese Cohort - Liu, Chuan; Hu, Chuan; Huang, Jiale; Xiang, Kanghui; Li, Zhi; Qu, Jinglei; Chen, Ying; Yang, Bowen; Qu, Xiujuan; Liu, Yunpeng; Zhang, Guangwei; Wen, Ti (Frontiers in Oncology, 2021)
Background: Among colon cancer patients, liver metastasis is a commonly deadly phenomenon, but there are few prognostic models for these patients. Methods: The clinicopathologic data of colon cancer with liver metastasis (CCLM) patients were downloaded from the Surveillance, Epidemiology and End Results (SEER) database. All patients were randomly divided into training and internal validation sets based on the ratio of 7:3. A prognostic nomogram was established with Cox analysis in the training set, which was validated by two independent validation sets. Results: A total of 5700 CCLM patients were included. Age, race, tumor size, tumor site, histological type, grade, AJCC N status, carcinoembryonic antigen (CEA), lung metastasis, bone metastasis, surgery, and chemotherapy were independently associated with the overall survival (OS) of CCLM in the training set, which were used to establish a nomogram. The AUCs of 1-, 2- and 3-year were higher than or equal to 0.700 in the training, internal validation, and external validation sets, indicating the favorable effects of our nomogram. Besides, whether in overall or subgroup analysis, the risk score calculated by this nomogram can divide CCLM patients into high-, middle- and low-risk groups, which suggested that the nomogram can significantly determine patients with different prognosis and is suitable for different patients. Conclusion: Higher age, the race of black, larger tumor size, higher grade, histological type of mucinous adenocarcinoma and signet ring cell carcinoma, higher N stage, RCC, lung metastasis, bone metastasis, without surgery, without chemotherapy, and elevated CEA were independently associated with poor prognosis of CCLM patients. A nomogram incorporating the above variables could accurately predict the prognosis of CCLM.