Deep learning ensemble models for CT-based differentiation of malignant and benign sacral bone tumors: development and evaluation
First artificial intelligence-radiologist ensemble for noncontrast computed tomography (NCCT)-based sacral tumor classification. Boosts all radiologists' performance, with the greatest gains for juniors, potentially reducing referrals. Enables reliable NCCT diagnosis, overcoming contrast/magnetic resonance imaging dependency in musculoskeletal oncology.
Authors
- Yongmei Li (ORCID: https://orcid.org/0000-0003-2625-2006)
- Nan Hong (ORCID: https://orcid.org/0000-0003-0301-0514)
- Kewei Liang
- Fei Zheng
- Li Yang
- Ning Lang
- Lin Lu
- Ping Yin
- Ke Liu
Institutions
- Beijing Institute of Technology (CN)
- Memorial Sloan Kettering Cancer Center (US)
- Shanxi Medical University (CN)
- Peking University (CN)
- Peking University People's Hospital (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Peking University Third Hospital (CN)
- Shanxi Provincial People’s Hospital (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2026-03-03
- DOI
- https://doi.org/10.1186/s13244-026-02220-9
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China