# MRI-Based Morphological Features as Predictors of Clinical Outcomes in Locally Advanced Rectal Cancer Treated with Neoadjuvant Chemoradiotherapy: Insights from a Single-Institution Experience

## TL;DR

This study shows that MRI-based features can predict outcomes in rectal cancer patients treated with chemoradiotherapy, helping guide personalized treatment.

## Contribution

The study identifies specific MRI morphological features as novel predictors of long-term clinical outcomes in locally advanced rectal cancer.

## Key findings

- Patients with TEMP > 5 mm had significantly worse local control and disease-free survival.
- Extramural venous invasion was associated with significantly lower local control.
- Persistent pathological lateral lymph nodes after treatment impacted local control.

## Abstract

Objectives: This study evaluates MRI-based morphological features as predictors of long-term clinical outcomes in patients with locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (CRT). Methods: A retrospective analysis was performed on 134 patients treated between 2014 and 2024. Patients underwent dose-intensified radiotherapy (55 Gy) with concurrent capecitabine followed by surgery. Radiological features analyzed on pre- and post-CRT MRI included Tumor Extension Beyond Muscularis Propria (TEMP), Circumferential Resection Margin (CRM), Extramural Venous Invasion (EMVI), and Lateral Lymph Nodes (LLN). Results: Five-year Overall Survival (OS), Disease-Free Survival (DFS), and Local Control (LC) rates were 85%, 83%, and 88%, respectively. Patients with TEMP > 5 mm had significantly worse LC (p = 0.02) and DFS (p = 0.04). A positive CRM (<1 mm) significantly correlated with reduced DFS (p = 0.04). The presence of EMVI was associated with significantly lower LC (p = 0.01). Additionally, persistent pathological LLN after treatment significantly impacted LC (p = 0.04). Conclusions: MRI morphological features such as TEMP > 5 mm, CRM < 1 mm, EMVI, and pathological LLN are significant predictors of worse oncological outcomes. Identifying these imaging biomarkers allows for better risk stratification and personalized treatment strategies in LARC.