# Refining Outcomes in Technically Resectable Colorectal Liver Metastases: A Simplified Risk Model and the Role of Preoperative Chemotherapy

## TL;DR

A simple risk model using tumor count and size helps identify high-risk colorectal liver metastasis patients, guiding treatment and follow-up.

## Contribution

A simplified, rule-based risk model using only two tumor features for predicting outcomes in colorectal liver metastases.

## Key findings

- High-risk patients had earlier recurrence and shorter survival based on tumor count and size.
- The model performed similarly to existing prediction tools like the Beppu nomogram and Fong’s score.
- Preoperative chemotherapy response further refined outcomes in high-risk patients.

## Abstract

Preoperative chemotherapy is often given before liver resection for colorectal liver metastases, yet practical postoperative risk tools remain scarce. This retrospective study of 115 patients developed an easy, rule-based risk model using only two tumor features: the number of liver metastases and the size of the largest tumor. Patients were considered high risk if they had three or more metastases, or if they had one to two metastases with the largest tumor being 5 cm or greater. This high-risk group experienced earlier recurrence and shorter survival, and the model performed similarly to commonly used prediction tools. By focusing on two tumor features, this tool may help clinicians rapidly identify high-risk patients, guide additional therapies, and refine follow-up plans. These findings could support further research aimed at developing more personalized strategies for caring for patients with liver metastasis.

Background: Preoperative chemotherapy is increasingly used for colorectal liver metastases (CRLM), but simple risk stratification tools for routine practice remain limited. We developed a simple risk model to predict outcomes after curative-intent CRLM resection, including in patients receiving preoperative chemotherapy. Methods: We retrospectively analyzed 115 patients who underwent initial curative-intent liver resection for CRLM at two centers. Factors associated with recurrence-free survival (RFS) and overall survival (OS) were evaluated using Cox proportional hazards models and log-rank tests. Model performance was benchmarked against the Beppu nomogram and Fong’s clinical risk score using the area under the curve (AUC). Outcomes were also assessed based on response to preoperative chemotherapy. Results: Having ≥3 CRLMs was the only independent predictor common to both OS and RFS. Among patients with 1–2 CRLMs, the largest tumor diameter being ≥5 cm independently predicted RFS. A composite high-risk definition (≥3 CRLMs, or 1–2 CRLMs with a diameter ≥ 5 cm) independently predicted recurrence (HR 2.05, p = 0.007) and overall mortality (HR 2.24, p = 0.017). The AUCs were similar to the Beppu nomogram for recurrence (0.68 vs. 0.70 (p = 0.683) at 36 months, 0.66 vs. 0.68 (p = 0.766) at 60 months) and to Fong’s score for survival (0.59 vs. 0.64 (p = 0.430) at 36 months, 0.65 vs. 0.74 (p = 0.074) at 60 months). Among patients receiving preoperative chemotherapy (n = 72), high-risk status was associated with poorer RFS (HR 3.11, p < 0.001) and OS (HR 2.80, p = 0.010). Within this subgroup, progressive disease (PD) was associated with worse outcomes than disease control (CR/PR/SD). Conclusions: This two-variable, rule-based model provides an easy-to-use tool for postoperative risk stratification after CRLM resection, and incorporating chemotherapy response may further refine prognostication.