Integrative Predictive Nomograms for Treatment Decision-Making in Resectable Synchronous Colorectal Liver Metastases

Background: Currently, there is no established standard for managing resectable synchronous colorectal liver metastases (CRLM): upfront surgery or neoadjuvant therapy. This study has integrated four available clinical factors- clinicopathological characteristics, gene mutation profiles, imaging findings, and hematological indicators- to create a potentially robust tool aiding clinicians in deciding between upfront surgery and neoadjuvant therapy. Methods: This retrospective cohort study included individuals diagnosed with resectable synchronous CRLM between 2008 and 2018. The development of prediction nomograms entailed identifying independent prognostic indicators through univariate and multivariate Cox analyses. The accuracy of the predictions was evaluated through calibration curves and the C-index. Furthermore, the clinical effectiveness of the nomograms was assessed using DCA and ROC curves. To enhance accessibility, two web servers were developed to simplify the utilization of the nomograms for an improved user experience. Results: A total of 386 patients with resectable synchronous CRLM were included. The patients were categorized randomly into a training cohort (n = 270, 70%) and a testing cohort (n = 116, 30%). The nomograms incorporated nine predictors: metastatic tumor count, cN stage, KRAS and BRAF mutation status, age, primary tumor location, neutrophil and platelet counts, and D-Dimer levels. The calibration plots for resectable synchronous CRLM survival predictions showed remarkable consistency. The C-index of OS and DFS prediction models were both above 0.7. And the area under the ROC curve of 1-, 3- and 5-year OS and DFS exceeded 0.7 as well. As demonstrated by the DCA plots, both nomograms exhibit satisfactory clinical effectiveness. A web-based application was developed to demonstrate the practical application of the prediction models. Conclusion: The personalized web-based predictive models exhibited moderate predictive accuracy in resectable synchronous CRLM. These tools offer valuable assistance to physicians in deciding between upfront surgery and neoadjuvant therapy for resectable synchronous CRLM.