A predictive model for early recurrence of colorectal-cancer liver metastases based on clinical parameters. (26th January 2021)

• Record Type: Journal Article
• Authors: Dai, Siqi; Ye, Yao; Kong, Xiangxing; Li, Jun; Ding, Kefeng
• Is Part Of: Gastroenterology report. Volume 9:Number 3(2021)
• Page Start: 241
• Page End: 251
• Publication Date: 2021-01-26
• Subjects: colorectal cancer -- liver metastases -- early recurrence -- prediction model -- post-operative surveillance; Gastroenterology -- Periodicals; 616.33
• DOI: 10.1093/gastro/goaa092
• Languages: English
• ISSNs: 2052-0034

Abstract

Abstract: Background: The prognosis for patients with colorectal-cancer liver metastases (CRLM) after curative surgery remains poor and shows great heterogeneity. Early recurrence, defined as tumor recurrence within 6 months of curative surgery, is associated with poor survival, requiring earlier detection and intervention. This study aimed to develop and validate a bedside model based on clinical parameters to predict early recurrence in CRLM patients and provide insight into post-operative surveillance strategies. Material and methods: A total of 202 consecutive CRLM patients undergoing curative surgeries between 2012 and 2019 were retrospectively enrolled and randomly assigned to the training ( n = 150) and validation ( n = 52) sets. Baseline information and radiological, pathological, and laboratory findings were extracted from medical records. Predictive factors for early recurrence were identified via a multivariate logistic-regression model to develop a predictive nomogram, which was validated for discrimination, calibration, and clinical application. Results: Liver-metastases number, lymph-node suspicion, neurovascular invasion, colon/rectum location, albumin and post-operative carcinoembryonic antigen, and carbohydrate antigen 19–9 levels (CA19–9) were independent predictive factors and were used to construct the nomogram for early recurrence after curative surgery. The area under the curve was 0.866 and 0.792 for internal and external validation, respectively. The model significantly outperformed the clinical risk score and Beppu's model in our data set. In the lift curve, the nomogram boosted the detection rate in post-operative surveillance by two-fold in the top 30% high-risk patients. Conclusion: Our model for early recurrence in CRLM patients after curative surgeries showed superior performance and could aid in the decision-making for selective follow-up strategies.