Establishing a predictive model for tumor mutation burden status based on 18F-FDG PET/CT and clinical features of non-small cell lung cancer patients
In this study, a predictive model combining <sup>18</sup>F-FDG PET/CT and clinical features of NSCLC patients effectively distinguished between TMB-high and TMB-low status. The nomogram generated from this model holds significant promise for predicting TMB status, offering valuable insights for clinical decision-making.
Authors
- Wenbo Li (ORCID: https://orcid.org/0000-0002-4988-9766)
- Zheng Chen (ORCID: https://orcid.org/0000-0002-8630-7825)
- Xueping Chen (ORCID: https://orcid.org/0000-0003-4290-7404)
- Yue Li (ORCID: https://orcid.org/0000-0001-5808-6095)
- Hua Pang
- Linjun Ju
Institutions
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Translational Lung Cancer Research
- Published
- 2024-09-01
- DOI
- https://doi.org/10.21037/tlcr-24-416
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- Chongqing Municipal Education Commission