Machine learning survival prediction in esophageal cancer using radiomics and body composition from pretreatment and...

Esophageal cancer carries a poor prognosis with low 5-year survival rates and limited early detection options. The skeletal muscle index at the L3 vertebral level is a well-established prognostic marker in esophageal cancer, but most follow-up computed tomography (CT) scans do not extend to L3 and limiting its utility. Radiomics has emerged as a powerful tool for extracting prognostic information from medical images.

To evaluate the influential features for esophageal cancer prognosis by integrating radiomic and body composition-based indices of skeletal muscle and adipose tissue at the T12 level from both pretreatment and follow-up CT images, in order to assess their value in predicting overall survival (OS).

This retrospective study included 212 esophageal cancer patients who underwent concurrent chemoradiotherapy, with both pretreatment and follow-up chest CT scans available. Body organ analysis (BOA) and radiomic features were extracted from skeletal muscle and adipose tissue at the T12 level using automated tools. Four feature subsets (no-radiomics, pretreatment only, follow-up only, and combined inputs) were developed using logistic regression (LR) with least absolute shrinkage and selection operator for feature selection, followed by Cox regression. Prognostic models - including nomogram, support vector classifier, LR, and extra trees classifier - were constructed to predict 1-, 2-, and 3-year OS.

The model integrating both BOA and radiomics from pretreatment and follow-up CT, combined with clinical data, achieved the best performance for 2-year OS prediction, with an area under the time-dependent receiver operating characteristic curve of 0.91, sensitivity of 0.81, and specificity of 0.88 using the LR model. The most predictive features included both clinical variables, body composition indices, and radiomic features, particularly from follow-up VAT. Follow-up imaging contributed significantly to model performance, reinforcing its value in treatment response evaluation.

This is the first study to demonstrate that BOA indices and their corresponding radiomics at the T12-level from both pretreatment and follow-up CT scans - combined with clinical data - can provide accurate prognostic information for esophageal cancer. This approach offers a practical alternative when L3-level imaging is unavailable and supports the clinical integration of automated T12-based imaging biomarkers. The integration of these imaging features with clinical parameters enhances the prediction of survival outcomes and contributes to non-invasive, personalized treatment planning.