Exploration of optimal thresholds for predicting the invasive nature of stage T1 lung adenocarcinoma using artificial intelligence-based 3D solid component volume segmentation
The -350 HU threshold is reasonable for predicting the invasiveness of T1 lung adenocarcinoma based on solid component volume using the threshold segmentation method. However, there are certain differences in the threshold values depending on the diameter of the pulmonary nodule.
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)
- Yinsen Song
- 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)
- Fifth Affiliated Hospital of Zhengzhou University (CN)
- Zhengzhou People's Hospital (CN)
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2024-12-24
- DOI
- https://doi.org/10.21037/qims-24-1627
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81