MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort study
The Tf_Radiomics+Resnet101 model, based on MRI and Resnet101 deep learning, shows a high ability to predict the clinically complete response (cCR) efficacy of PD-1 inhibitor combined with GP in advanced NPC. This model can significantly enhance the treatment management of patients with advanced NPC.
Authors
- Weimei Huang (ORCID: https://orcid.org/0000-0002-2478-284X)
- Kai Hu (ORCID: https://orcid.org/0000-0002-2777-9080)
- Rensheng Wang (ORCID: https://orcid.org/0000-0002-0768-9344)
- Fuli Chen (ORCID: https://orcid.org/0000-0002-3929-3651)
- Liang Xian
- Zhechen Ouyang
- Siyi He (ORCID: https://orcid.org/0009-0005-1481-7104)
- Xinling Qin
- Yiru Wang (ORCID: https://orcid.org/0009-0002-1609-6839)
Institutions
- Guangxi Medical University (CN)
- First Affiliated Hospital of GuangXi Medical University (CN)
Publication Details
- Journal
- Translational Oncology
- Published
- 2024-12-10
- DOI
- https://doi.org/10.1016/j.tranon.2024.102245
- Citations
- 9
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.64
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangxi Province