Advancing EGFR mutation subtypes prediction in NSCLC by combining 3D pretrained ConvNeXt, radiomics, and clinical features
Both deep learning models and radiomic signature-based models offer reasonably accurate non-invasive predictions of EGFR status and its subtypes. Fusion models hold the potential to enhance noninvasive methods for predicting EGFR mutations and subtypes, presenting a more reliable prediction approach.
Authors
- Jiancheng Yang (ORCID: https://orcid.org/0000-0003-4455-7145)
- Yikai Xu (ORCID: https://orcid.org/0000-0002-6296-2729)
- Hao Peng (ORCID: https://orcid.org/0000-0002-5583-0998)
- Fang Zhou (ORCID: https://orcid.org/0000-0003-0031-1303)
- Chantao Huang
- Jun Xu (ORCID: https://orcid.org/0000-0002-1823-3633)
- Yinghong Yu (ORCID: https://orcid.org/0009-0003-9621-8396)
Institutions
- Nanfang Hospital (CN)
- École Polytechnique Fédérale de Lausanne (CH)
- Southern Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-11-15
- DOI
- https://doi.org/10.3389/fonc.2024.1464555
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.40