Non-invasive Prediction of Lymph Node Metastasis in NSCLC Using Clinical, Radiomics, and Deep Learning Features From 18F-FDG PET/CT Based on Interpretable Machine Learning
The combined model, combining clinical, radiomics, and deep learning features from PET/CT, significantly improved the accuracy of LNM prediction in NSCLC patients. SHAP-based interpretability provided valuable insights into the model's decision-making process, enhancing its potential clinical application for preoperative decision-making in NSCLC.
Authors
- Chunyan Yang (ORCID: https://orcid.org/0000-0003-1790-3647)
- Dalong Wang (ORCID: https://orcid.org/0000-0002-0869-8701)
- Minghui Zhang (ORCID: https://orcid.org/0000-0003-0575-623X)
- Fuqing Duan (ORCID: https://orcid.org/0000-0002-3849-8532)
- Xuewei Wang (ORCID: https://orcid.org/0009-0009-0341-2602)
Institutions
- Harbin Medical University (CN)
- Second Affiliated Hospital of Harbin Medical University (CN)
Publication Details
- Journal
- Academic Radiology
- Published
- 2024-12-10
- DOI
- https://doi.org/10.1016/j.acra.2024.11.037
- Citations
- 17
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.87