Exploring the optimal threshold of 3D consolidation tumor ratio value segmentation based on artificial intelligence for predicting the invasive degree of T1 lung adenocarcinoma
This study suggests that setting the CT threshold at -150 HU and using the AI-based threshold segmentation method to calculate the 3D CTR effectively distinguishes whether stage T1 lung adenocarcinoma is invasive, with an optimal cut-off point at 2.75%. Under this threshold, for varying nodule sizes, criteria are proposed: for nodules ≤10 mm with a 3D CTR <2.75%; >10 to 20 mm with a 3D CTR <5.4%; and >20 to 30 mm with a 3D CTR <7.1%, these partially solid nodules can be treated as non-IAC.
Authors
- Guotao Chang
- Zhengpan Wei (ORCID: https://orcid.org/0000-0002-1034-2085)
- Huiyu Zheng
- Wensong Shi (ORCID: https://orcid.org/0000-0002-0963-9359)
- Yingli Sun (ORCID: https://orcid.org/0000-0002-1888-7661)
- Yulun Yang
- Yuzhui Hu
- Xiangnan Li (ORCID: https://orcid.org/0000-0001-6157-8680)
- Qian He (ORCID: https://orcid.org/0000-0002-9981-4631)
- Ming Li (ORCID: https://orcid.org/0000-0003-3081-2424)
- Liang Zhao (ORCID: https://orcid.org/0000-0002-0962-5870)
Institutions
- Fudan University (CN)
- Huadong Hospital (CN)
- Zhengzhou People's Hospital (CN)
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2024-12-01
- DOI
- https://doi.org/10.21037/qims-24-1328
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81