Artificial intelligence in automatic image segmentation system for exploring recurrence patterns in small cell carcinoma of the lung
The utilization of AI-based automatic image segmentation system for delineating the initial CTV has proven pivotal. Analysis and modeling of recurrent images reveal that the initial GTV and GTVnd are critical regions for recurrence. Leveraging partition parameter and variable information, we constructed a clinically viable recurrence prediction model. This model significantly aids in guiding the precise clinical targeting of treatment areas, demonstrating the potential of AI to enhance patient management and treatment outcomes in small cell lung cancer.
Authors
- Zhikai Liu (ORCID: https://orcid.org/0000-0001-5576-2318)
- Shaobin Wang (ORCID: https://orcid.org/0000-0002-0377-2095)
- Mingyi Di (ORCID: https://orcid.org/0000-0001-7540-8383)
- Mei Li (ORCID: https://orcid.org/0000-0002-9265-8541)
- Qi Chen (ORCID: https://orcid.org/0000-0001-6353-9589)
- Jing Shen (ORCID: https://orcid.org/0000-0002-4566-5925)
- Fuquan Zhang (ORCID: https://orcid.org/0000-0001-7813-3071)
- Ke Hu (ORCID: https://orcid.org/0000-0002-4135-6762)
- Hui Guan (ORCID: https://orcid.org/0000-0003-4377-2644)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- China Rural Technology Development Center (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-05-01
- DOI
- https://doi.org/10.3389/fonc.2025.1534740
- Primary Topic
- Lung Cancer Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Peking Union Medical College Hospital