Radiomics and prognostic nutritional index for predicting postoperative survival in esophageal carcinoma

Surgery offers the potential for a radical cure and prolonged survival in individuals diagnosed with esophageal squamous cell carcinoma (ESCC). However, survival rates exhibit significant variability among patients. Accurately assessing surgical outcomes remains a critical challenge. This study aimed to evaluate the predictive value of preoperative radiomics and the prognostic nutritional index for individuals with ESCC and to develop a comprehensive model for estimating postoperative overall survival (OS) in these patients.

A retrospective analysis was conducted on 466 patients with ESCC from two medical centers. The dataset was randomly divided into a training cohort (TC, hospital 1, 246 cases), an internal validation cohort (IVC, hospital 1, 106 cases), and an external validation cohort (EVC, hospital 2, 114 cases). Radiological features were extracted after delineating the region of interest, followed by the application of the least absolute shrinkage and selection operator (LASSO) regression to identify optimal radiomics features and compute the radiomics score (RS). Independent prognostic factors identified via Cox regression analysis were incorporated with the RS to construct a combined nomogram. The predictive performance of the model was evaluated using the concordance index, time-dependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis.

The predictive model, which integrated preoperative radiomics, the prognostic nutritional index, and tumor-node-metastasis (TNM) staging to estimate the 3 year OS rate, achieved area under the ROC curve (AUC) values of 0.812, 0.786, and 0.810 in the TC, IVC, and EVC, respectively, demonstrating excellent prognostic accuracy. These values surpassed the AUCs of the TNM staging model in the TC, IVC, and EVC, which were 0.717, 0.612, and 0.699, respectively. The combined model's concordance indexes in the TC, IVC, and EVC were 0.780, 0.760, and 0.764, respectively. Calibration and decision curves highlighted the nomogram's superior calibration and clinical utility.

This study developed a predictive model by combining radiomics with the prognostic nutritional index, enabling the estimation of OS in postoperative patients with ESCC. This model holds promise as a tool for preoperative risk stratification.