A multimodal synergistic model for personalized neoadjuvant immunochemotherapy in esophageal cancer

Abstract

Neoadjuvant immunochemotherapy (nICT) has significantly improved the treatment of locally advanced esophageal cancer (EC), yet accurately identifying patients' response remains a major challenge. In this study, we introduce eSPARK, a multimodal framework designed to integrate routinely available clinical data for informed decision-making in nICT treatment for EC. The model is developed using 344 patients from three independent regions, each with pre-treatment-paired computed tomography (CT) imaging and pathological slides, and postoperative pathological complete response (pCR) outcomes. By incorporating cytological semantic information, eSPARK demonstrates superior generalizability, outperforming single-modality models and achieving robust predictive accuracy across multicenter datasets. Additionally, a multi-scale interpretability module identifies several biomarkers, including the neutrophil-to-lymphocyte ratio (NLR) in the tumor microenvironment, associated with nICT response. Our findings underscore the potential of eSPARK as a powerful tool for personalized therapeutic decision-making in locally advanced EC and its broader implications for advancing precision oncology through multidisciplinary data integration.

Keywords: deep learning; esophageal cancer; multimodal; neoadjuvant immunochemotherapy.