Development and validation of CT radiomics diagnostic models: differentiating benign from malignant pulmonary nodules and evaluating malignancy degree
Radiomics-based RMs showed good diagnostic performance in differentiating <3 cm lung nodules and assessing malignancy. COMs, which combined independent predictors and RMs, had better diagnostic performance than CMs, indicating potential for clinical use. These models can guide treatment decisions, such as conservative management for benign-predicted nodules, sublobar resection for low-grade malignancies, and radical lobectomy with lymph node dissection for high-grade malignancies.
Authors
- Jun Zhu (ORCID: https://orcid.org/0000-0003-1224-2771)
- Yuichi Saito (ORCID: https://orcid.org/0000-0002-2025-9226)
- Bin Ni (ORCID: https://orcid.org/0000-0002-1412-2901)
- Jiayu Tao
- Yuxuan Wang (ORCID: https://orcid.org/0000-0002-2932-6042)
- Kexin Chen (ORCID: https://orcid.org/0000-0003-4248-7681)
- Xiaochen Lu
- Chonggang Ma
- Jiaqiang Liu (ORCID: https://orcid.org/0000-0002-9328-7957)
- M J Zhu
Institutions
- Soochow University (CN)
- First Affiliated Hospital of Soochow University (CN)
- Teikyo University (JP)
Publication Details
- Journal
- Journal of Thoracic Disease
- Published
- 2025-03-01
- DOI
- https://doi.org/10.21037/jtd-2025-152
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36