LAUSR

BACKGROUND The current study aimed to comprehensively analyze the clinical prognostic factors of malignant esophageal fistula (MEF). Furthermore, this study sought to establish and validate prognostic nomograms incorporating radiomics and clinical factors to predict overall survival and median survival after fistula for patients with MEF. METHODS The records of 76 patients with MEF were retrospectively analyzed. A stepwise Cox proportional hazards regression model was employed to screen independent prognostic factors and develop clinical nomograms. Radiomic features were extracted from prefistula CT images and post fistula CT images. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression algorithm was used to filter radiomic features and avoid overfitting. Radiomic signature was a linear combination of optimal features and corresponding coefficients. The joint prognostic nomograms was constructed by radiomic signatures and clinical features. All models were validated by Harrell's concordance index (C-index), caliberation and bootstrap validation. RESULTS For overall survival, age, prealbumin, KPS and interval between diagnosis of esophageal cancer and fistula were identified as independent prognostic factors and incorporated into the clinical nomogram. Age, prealbumin, serum albumin, KPS and neutrophil proportion were selected for the clinical nomogram of post fistula survival. The C-index of overall survival nomogram was 0.719 (95% CI: 0.645-0.793) and that was 0.722 (95% CI: 0.653-0.791) in the post fistula survival nomogram. The radiomic signature developed by radiomic features of prefistula CT showed a significant correlation with both overall survival and post fistula survival. The C-index of joint nomogarm for overall survival and post fistula survival was 0.831 (95% CI: 0.757-0.905) and 0.77 (95% CI: 0.686-0.854), respectively. The calibration curve showed the joint nomograms outperformed the clinical ones. CONCLUSIONS The study presents nomograms incorporating independent clinical risk factors and radiomic signature to predict the prognosis of MEF. This prognostic classification system has the potential to guide therapeutic decisions for patients with malignant esophageal fistulas.