Smart contours: deep learning-driven internal gross tumor volume delineation in non-small cell lung cancer using 4D CT maximum and average intensity projections
DL models can automate IGTV delineation using MIP and AIP images, streamline contouring, and enhance the accuracy and consistency of lung cancer radiotherapy planning to improve patient outcomes.
Authors
- Junyu Li (ORCID: https://orcid.org/0009-0001-9399-5304)
- Junming Jian (ORCID: https://orcid.org/0000-0003-1683-0546)
- Mingming Luo (ORCID: https://orcid.org/0000-0001-9983-2920)
- Mingzhi Liu (ORCID: https://orcid.org/0000-0001-5842-8307)
- Yun Zhang (ORCID: https://orcid.org/0000-0001-5075-727X)
- Yuling Huang (ORCID: https://orcid.org/0000-0003-0018-1799)
- Zan Luo
Institutions
- Jiangxi University of Technology (CN)
- First Affiliated Hospital of Jiangxi Medical College (CN)
- Jiangxi Provincial Cancer Hospital (CN)
Publication Details
- Journal
- Radiation Oncology
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1186/s13014-025-02642-7
- Citations
- 1
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- Natural Science Foundation of Jiangxi Province