Evaluating peritumoral and intratumoral radiomics signatures for predicting lymph node metastasis in surgically resectable non-small cell lung cancer
The radiomics-based model incorporating both peritumoral and intratumoral features from CT images can more accurately predict lymph node metastasis in NSCLC than traditional methods.
Authors
- Ran Xu (ORCID: https://orcid.org/0000-0002-2816-8468)
- Chenghao Wang (ORCID: https://orcid.org/0000-0001-8846-4130)
- Jiaxin Shi (ORCID: https://orcid.org/0000-0001-6842-4211)
- Xiang Zhou (ORCID: https://orcid.org/0000-0002-1829-9368)
- Kaiyu Wang (ORCID: https://orcid.org/0000-0001-5209-5417)
- Tong Lu (ORCID: https://orcid.org/0000-0001-5805-5336)
- Jiaying Zhao (ORCID: https://orcid.org/0000-0002-6009-0568)
- Bo Peng (ORCID: https://orcid.org/0000-0002-1915-0549)
- Linyou Zhang (ORCID: https://orcid.org/0000-0002-7713-2963)
Institutions
- Harbin Medical University (CN)
- Shanghai Jiao Tong University (CN)
- Ruijin Hospital (CN)
- Second Affiliated Hospital of Harbin Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-10-11
- DOI
- https://doi.org/10.3389/fonc.2024.1427743
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Harbin Medical University
- Second Affiliated Hospital of Harbin Medical University