Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy
We present a voxel-level radiomics-based deep learning model to predict pCR to neoadjuvant immunotherapy combined with chemotherapy based on pretreatment diagnostic CT images with high accuracy and robustness. This model could provide a promising tool for individualized management of patients with ESCC.
Authors
- Leonard Wee (ORCID: https://orcid.org/0000-0003-1612-9055)
- André Dekker (ORCID: https://orcid.org/0000-0002-0422-7996)
- Jian Zeng (ORCID: https://orcid.org/0000-0002-0573-2118)
- Tianchen Luo
- Jianming Zheng (ORCID: https://orcid.org/0000-0003-1471-9078)
- Iñigo Bermejo (ORCID: https://orcid.org/0000-0001-9105-8088)
- Wencheng Zhang (ORCID: https://orcid.org/0000-0003-3730-5361)
- Min Fang (ORCID: https://orcid.org/0000-0001-5241-6460)
- Jingping Yuan (ORCID: https://orcid.org/0000-0001-7470-6899)
- Kaiyi Tao
- Zhen Zhang (ORCID: https://orcid.org/0000-0001-6335-9529)
- Yan Meng (ORCID: https://orcid.org/0009-0001-4398-0243)
- Haixia Shen
- Yongling Ji
- Youhua Jiang (ORCID: https://orcid.org/0000-0003-2735-1399)
- Dirk De Ruysscher (ORCID: https://orcid.org/0000-0002-1214-3557)
Institutions
- National University of Singapore (SG)
- Tianjin Medical University Cancer Institute and Hospital (CN)
- Wuhan University (CN)
- Zhejiang Cancer Hospital (CN)
- Renmin Hospital of Wuhan University (CN)
- Maastro Clinic (NL)
- Hangzhou Cancer Hospital (CN)
- Hasselt University (BE)
Publication Details
- Journal
- Journal for ImmunoTherapy of Cancer
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1136/jitc-2024-011149
- Citations
- 33
- Primary Topic
- Esophageal Cancer Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 64.21
Funders
- National Natural Science Foundation of China