Computed Tomography-Based Radiomic Nomogram to Predict Occult Pleural Metastasis in Lung Cancer
Radiomic features derived from CT scans show significant promise in identifying patients with lung cancer at risk of OPM. The nomogram developed in this study, which integrates CEA, the NLR, and radiomic tumor area scores, enhances the precision of preoperative OPM prediction and provides a valuable tool for clinical decision-making.
Authors
- Guanchao Jiang
- Xiangyu Zeng (ORCID: https://orcid.org/0000-0003-4913-3704)
- Xiaoyi Zhao (ORCID: https://orcid.org/0000-0002-6646-0953)
- Yun Li (ORCID: https://orcid.org/0000-0003-0949-6519)
- Feng Yang (ORCID: https://orcid.org/0000-0001-8418-2507)
- Heng Zhao
- Kongxu Dai (ORCID: https://orcid.org/0009-0000-7724-2225)
Institutions
- Peking University (CN)
- Peking University People's Hospital (CN)
Publication Details
- Journal
- Current Oncology
- Published
- 2025-04-11
- DOI
- https://doi.org/10.3390/curroncol32040223
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Peking University
- Peking University People's Hospital