# Reassessing the Prognostic Value of Lymph Node Metastasis in Deficient Mismatch Repair Colorectal Cancer

## TL;DR

This study finds that LOODS is a better predictor of survival in dMMR colorectal cancer than traditional lymph node staging.

## Contribution

The study introduces LOODS as a superior prognostic model over traditional pN staging in dMMR CRC.

## Key findings

- Traditional pN staging failed to distinguish survival outcomes between pN1 and pN2 dMMR CRC patients.
- LOODS outperformed traditional pN staging in predicting overall and disease-free survival.
- A model combining LOODS with age showed better predictive performance than traditional TN staging.

## Abstract

Background: In non-metastatic deficient mismatch repair (dMMR) colorectal cancer (CRC), traditional prognostic factors, such as pN staging, often fail to distinguish patient outcomes effectively. Methods: This retrospective study included a cohort of 792 dMMR CRC patients who underwent surgical treatment without neoadjuvant chemoradiotherapy or immunotherapy. Traditional prognostic factors were compared with lymph node-based models (NLN, LNR, LOODS) for their ability to predict overall survival (OS) and disease-free survival (DFS). Results: The study demonstrated that traditional factors, such as histologic type, differentiation, and vascular invasion, had limited predictive value in dMMR CRC. Furthermore, the pN stage failed to effectively distinguish between pN1 and pN2 for both OS (p = 0.219) and DFS (p = 0.095). Conversely, LOODS demonstrated superior performance over traditional pN staging in predicting both OS and DFS (p < 0.001). A prognostic model combining LOODS with age exhibited superior predictive performance compared with the traditional TN staging system. Conclusions: LOODS was identified as a more effective independent prognostic factor compared with traditional pN staging, enabling more precise stratification of pN+ patients in non-metastatic dMMR CRC, highlighting its potential utility in guiding postoperative treatment and optimizing therapeutic strategies.