Risk modeling for esophageal cancer based on adaptive Lasso and Cox regression

TL;DR

This study identifies INR as a key biomarker for predicting survival in esophageal cancer patients after surgery, improving risk models for better treatment planning.

Contribution

The study introduces INR as a novel and independent prognostic biomarker for postoperative survival in esophageal cancer patients.

Findings

INR was the most significant predictor of survival in post-surgical esophageal cancer patients.

Higher INR levels correlated with better 3-year and 5-year survival outcomes compared to PNI.

INR was significantly linked to tumor differentiation, infiltration, and positive/negative status in EC.

Abstract

Esophageal cancer (EC) is one of the most aggressive tumor types worldwide, and malnutrition is extremely common among EC patients. By identifying EC biomarkers and conducting risk assessments on patients, more accurate diagnosis and treatment plans can be developed to prolong patients’ survival.

This study developed a risk assessment model for post-surgical EC patients using clinical data from patients who underwent esophagectomy. Prognostic factors influencing survival were evaluated using Adaptive Lasso for variable selection, followed by Cox proportional hazards regression and Receiver Operating Characteristic (ROC) curve. Among multiple clinical variables, the International Normalized Ratio (INR) emerged as the most significant predictor of survival.

Elevated INR levels were significantly associated with improved 3-year and 5-year survival outcomes compared to the Prognostic Nutritional Index (PNI). Patients with higher INR exhibited notably better postoperative survival rates. Further analysis demonstrated that INR was significantly correlated with the final differentiation degree, final infiltration degree, and final positive/negative status of EC.

INR serves as a valuable and independent prognostic biomarker for postoperative survival assessment in EC patients. Incorporating INR into clinical risk models can enhance the accuracy of prognosis and assist clinicians in optimizing individualized therapeutic strategies for surgical EC patients.

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