An interpretable machine learning model using multimodal pretreatment features predicts pathological complete response to neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma
We established a clinically applicable, interpretable ML model that accurately predicts pCR to nICT in ESCC by integrating multimodal pretreatment data. This tool may optimize patient selection for nICT and advance precision therapy paradigms.
Authors
- Wencheng Tan
- Minjie Mao (ORCID: https://orcid.org/0000-0002-5250-8186)
- Jin‐Fei Lin
- Wenjun Zhou (ORCID: https://orcid.org/0000-0003-2346-8151)
- Songhe Guo (ORCID: https://orcid.org/0000-0003-3292-1114)
- Xueping Wang (ORCID: https://orcid.org/0000-0002-3519-3100)
- Kewei Huang (ORCID: https://orcid.org/0000-0003-3753-3479)
- Hui Sheng (ORCID: https://orcid.org/0000-0002-2040-8842)
- Hailin Zheng (ORCID: https://orcid.org/0000-0002-5773-385X)
Institutions
- Sun Yat-sen University (CN)
- Guangdong Medical College (CN)
- Dongguan University of Technology (CN)
- Sun Yat-sen University Cancer Center (CN)
- Guangzhou Medical University Cancer Hospital (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-09-16
- DOI
- https://doi.org/10.3389/fimmu.2025.1660897
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Sun Yat-sen University
- Sun Yat-sen University Cancer Center