Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning
Integrating intratumoral and peritumoral-vasculature radiomics with clinical features enabled the development of a predictive model for PFS in LC patients with IO. The proposed model's capability to estimate individualized PFS probability and differentiate the prognosis status held promise to facilitate personalized medicine and improve patient outcomes in LC.
Authors
- Yuh‐Min Chen (ORCID: https://orcid.org/0000-0003-0585-8463)
- Tsu‐Hui Shiao
- Heng‐Sheng Chao (ORCID: https://orcid.org/0000-0002-0309-0983)
- Chien-Yi Liao (ORCID: https://orcid.org/0009-0007-0872-327X)
- Chia‐Feng Lu (ORCID: https://orcid.org/0000-0003-1344-9950)
- Yu‐Te Wu (ORCID: https://orcid.org/0000-0002-6942-0340)
- Hwa‐Yen Chiu (ORCID: https://orcid.org/0000-0001-8759-7871)
- Jyun-Ru Chen
- Tingwei Wang (ORCID: https://orcid.org/0000-0001-7697-6024)
Institutions
- National Yang Ming Chiao Tung University (TW)
- Taipei Veterans General Hospital (TW)
- The University of Texas Southwestern Medical Center (US)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2024-09-30
- DOI
- https://doi.org/10.1186/s40644-024-00779-4
- Citations
- 23
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 9.30
Funders
- University System of Taiwan
- National Science and Technology Council