Variational autoencoder-based deep learning and radiomics for predicting pathologic complete response to neoadjuvant chemoimmunotherapy in locally advanced oesophageal squamous cell carcinoma
We innovatively used VAE-based deep learning and radiomics to construct the DLR model for predicting pCR of LA-ESCC after nCIT.
Authors
- Leonard Wee (ORCID: https://orcid.org/0000-0003-1612-9055)
- Shenlun Chen
- Petros Kalendralis (ORCID: https://orcid.org/0000-0002-4471-2021)
- Jingping Yuan (ORCID: https://orcid.org/0000-0001-7470-6899)
- Qing Gu (ORCID: https://orcid.org/0000-0001-5394-6615)
- Jin Wang (ORCID: https://orcid.org/0000-0001-5527-0102)
- Yan Meng (ORCID: https://orcid.org/0009-0001-4398-0243)
- Youhua Jiang (ORCID: https://orcid.org/0000-0003-2735-1399)
- André Dekker
Institutions
- Maastricht University Medical Centre (NL)
- Tianjin Medical University Cancer Institute and Hospital (CN)
- Wuhan University (CN)
- Fudan University Shanghai Cancer Center (CN)
- Zhejiang Cancer Hospital (CN)
- Renmin Hospital of Wuhan University (CN)
- Maastro Clinic (NL)
- Hangzhou Cancer Hospital (CN)
Publication Details
- Journal
- British Journal of Radiology
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1093/bjr/tqaf239
- Primary Topic
- Esophageal Cancer Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00