A multimodal synergistic model for personalized neoadjuvant immunochemotherapy in esophageal cancer

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.

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Publication Details

Journal
Cell Reports Medicine
Published
2025-12-01
DOI
https://doi.org/10.1016/j.xcrm.2025.102479
Primary Topic
Esophageal Cancer Research and Treatment
Type
article
Field-Weighted Citation Impact
0.00

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article

A multimodal synergistic model for personalized neoadjuvant immunochemotherapy in esophageal cancer

Muyan Cai, Zihan Zhao, Baishen Zhang, Shuman Li et al.
Cell Reports Medicine
Esophageal Cancer Research and Treatment
article

A multimodal synergistic model for personalized neoadjuvant immunochemotherapy in esophageal cancer

Muyan Cai, Zihan Zhao, Baishen Zhang, Shuman Li, Shuangjiang Li, Ke Zheng, Jian Zhou, Chuanmiao Xie, Xiaobo Wen, Dan Xie, Wenqun Xing, Esther Wu, Dongmei Chen, Ruixuan Wang, Xueyi Zheng, Weihao Lin, Xinke Zhang, Yan Zheng, Shaobin Chen, Xiaolong Wei
article en

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.

Cell Reports MedicineVol. 6(12)
Sun Yat-sen University (CN), Shantou University (CN), Zhengzhou University (CN), Cancer Hospital of Shantou University Medical College (CN), MRC Centre for Regenerative Medicine (GB), Henan Cancer Hospital (CN), Sun Yat-sen University Cancer Center (CN), Centre for Inflammation Research (GB)
National Natural Science Foundation of China, Sun Yat-sen University, Science and Technology Planning Project of Guangdong Province, Sun Yat-sen University Cancer Center, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 42%
Esophageal Cancer Research and Treatment
DOIOpen Access PDFOpenAlex
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