Radiomics meets sarcopenia: Machine learning-based multimodal modeling for esophageal cancer outcomes

Abstract

Esophageal cancer is a highly aggressive malignancy often diagnosed at an advanced stage, with poor prognosis and high recurrence rates despite curative treatment. Accurate prognostic tools are urgently needed to guide personalized management strategies. Recent research has demonstrated significant potential of integrating quantitative imaging biomarkers, specifically radiomics and sarcopenia, with machine learning (ML) techniques to enhance outcome prediction. This review systematically summarizes six recent studies (2022-2024) exploring integrated ML models combining sarcopenia and radiomics biomarkers with clinical parameters to predict survival in patients with esophageal and gastroesophageal cancers. Sample sizes ranged from 83 to 243 patients, with studies utilizing various imaging modalities (positron emission tomography/computed tomography and computed tomography) and model analysis approaches, including Cox regression, random forest, and light gradient boosting machine. These models incorporated features such as skeletal muscle indices, tumor texture, and shape descriptors. Models that combined clinical data, radiomics, and sarcopenia outperformed those using single modalities. These findings support the utility of multimodal imaging biomarkers in developing robust, individualized prognostic models. However, the retrospective nature of most studies highlights the need for standardization and external validation. This review underscores the potential of multimodal ML-based models in enhancing personalized risk stratification and treatment planning for esophageal cancer.

Keywords: Esophageal cancer; Gastroesophageal cancer; Machine learning; Outcome prediction; Radiomics; Sarcopenia.