FLAIR-based radiomics signature from brain-tumor interface for early prediction of response to EGFR-TKI therapy in NSCLC patients with brain metastasis
The study developed a validated non-invasive model (RS-BTIplusVPE) based on integrating BTI-based radiomics features and VPE, which showed improved prediction of EGFR-TKI response in NSCLC patients with BM compared to tumor-focused models.
Authors
- Yan‐Jun Hu (ORCID: https://orcid.org/0000-0002-1840-5773)
- Ying Fan (ORCID: https://orcid.org/0000-0002-2925-507X)
- Xiran Jiang (ORCID: https://orcid.org/0000-0002-9640-6368)
- Chunna Yang
- Yan Wang (ORCID: https://orcid.org/0000-0002-4006-5859)
- Yue Zhang (ORCID: https://orcid.org/0000-0003-0809-4542)
- Zekun Wang (ORCID: https://orcid.org/0000-0001-5175-4118)
- Yiyao Sun
- Bo Sun (ORCID: https://orcid.org/0000-0002-1660-2378)
- Qianhui Zhang (ORCID: https://orcid.org/0000-0002-1777-3398)
- Zhiguang Yang (ORCID: https://orcid.org/0000-0001-7006-6134)
- Wenyan Jiang (ORCID: https://orcid.org/0009-0004-6647-605X)
- Mingchen Jiang
Institutions
- Dalian Medical University (CN)
- Fudan University (CN)
- Shenyang Center for Disease Control and Prevention (CN)
- First Affiliated Hospital of Dalian Medical University (CN)
- Liaoning Cancer Hospital & Institute (CN)
- China Medical University (CN)
Publication Details
- Journal
- Frontiers in Cell and Developmental Biology
- Published
- 2025-05-14
- DOI
- https://doi.org/10.3389/fcell.2025.1525989
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36