CT-based habitat radiomics for predicting treatment response to neoadjuvant chemoimmunotherapy in esophageal cancer patients
The results revealed that the model based on habitat data outperforms traditional radiomic analysis models. In addition, when the model is combined with clinical features, it improves the predictive accuracy of pathological complete response in patients undergoing neoadjuvant chemoimmunotherapy.
Authors
- Kun Zhu (ORCID: https://orcid.org/0000-0003-0069-368X)
- Yunlong Huang (ORCID: https://orcid.org/0000-0002-3328-1924)
- Qi Zhang (ORCID: https://orcid.org/0000-0003-0876-7495)
- Yuhang Ma (ORCID: https://orcid.org/0009-0008-3782-9396)
- Yao Long (ORCID: https://orcid.org/0000-0002-5717-8593)
- Hanlin Wang (ORCID: https://orcid.org/0000-0003-0443-4378)
- Junrui Xu
- Weibo Kong (ORCID: https://orcid.org/0000-0003-1309-5072)
- Kaiming Wu (ORCID: https://orcid.org/0000-0002-3108-1646)
- Renquan Zhang (ORCID: https://orcid.org/0000-0002-4274-9066)
Institutions
- Anhui Medical University (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-12-03
- DOI
- https://doi.org/10.3389/fonc.2024.1418252
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- Anhui Provincial Department of Education