Development and validation of a prediction model based on two-dimensional dose distribution maps fused with computed tomography images for noninvasive prediction of radiochemotherapy resistance in non-small cell lung cancer
Compared to the traditional radiomics model, the 2D dosiomics model demonstrates superior predictive performance. The combined model based on clinical data, radiomics, and dosiomics has improved the prediction of radiochemotherapy resistance in NSCLC and effectively performed survival stratification. Through precise risk assessment, doctors can better understand which patients may develop resistance to treatment and optimize treatment plans accordingly.
Authors
- Tony He (ORCID: https://orcid.org/0000-0002-5363-7780)
- Benlan Li
- Xia Ji (ORCID: https://orcid.org/0000-0001-7597-2769)
- Bing Lü (ORCID: https://orcid.org/0000-0002-0031-5999)
- Y. J. Mo (ORCID: https://orcid.org/0000-0003-4308-3152)
- Bo Du (ORCID: https://orcid.org/0000-0002-0059-8458)
- Fangyang Lu
- Yongxiang Hu (ORCID: https://orcid.org/0000-0001-8526-108X)
- Zhongjun Huang
- Ya Li (ORCID: https://orcid.org/0000-0002-3351-7909)
- Yang Yang (ORCID: https://orcid.org/0000-0003-1731-5837)
- Jie Peng (ORCID: https://orcid.org/0000-0001-8907-701X)
- Min Zhang (ORCID: https://orcid.org/0000-0001-9301-6615)
Institutions
- Guiyang Medical University (CN)
- Shanghai Public Health Clinical Center (CN)
- Affiliated Hospital of Guizhou Medical University (CN)
Publication Details
- Journal
- Translational Cancer Research
- Published
- 2025-03-01
- DOI
- https://doi.org/10.21037/tcr-24-1897
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00