Predicting clinical outcomes using 18F-FDG PET/CT-based radiomic features and machine learning algorithms in patients with esophageal cancer.

Objective: This study evaluated the relationship between 18F-fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) radiomic features and clinical parameters, including tumor localization, histopathological subtype, lymph node metastasis, mortality, and treatment response, in esophageal cancer (EC) patients undergoing chemoradiotherapy and the predictive performance of various machine learning (ML) models.

Methods: In this retrospective study, 39 patients with EC who underwent pretreatment 18F-FDG PET/CT and received concurrent chemoradiotherapy were analyzed. Texture features were extracted using LIFEx software. Logistic regression, Naive Bayes, random forest, extreme gradient boosting (XGB), and support vector machine classifiers were applied to predict clinical outcomes. Cox regression and Kaplan-Meier analyses were used to evaluate overall survival (OS), and the accuracy of ML algorithms was quantified using the area under the receiver operating characteristic curve.

Results: Radiomic features showed significant associations with several clinical parameters. Lymph node metastasis, tumor localization, and treatment response emerged as predictors of OS. Among the ML models, XGB demonstrated the most consistent and highest predictive performance across clinical outcomes.

Conclusion: Radiomic features extracted from 18F-FDG PET/CT, when combined with ML approaches, may aid in predicting treatment response and clinical outcomes in EC. Radiomic features demonstrated value in assessing tumor heterogeneity; however, clinical parameters retained a stronger prognostic value for OS.