Cesarean Scar Pregnancy Prognostic Classification System Based on Machine‐Learning and Traditional Linear Scoring Models
Multiple features were associated with CSP prognosis, such as GSUCL, IMPL, RMT, and the anterior-posterior diameter of the gestational sac at the level of the niche (GSSH). The CSP prognostic prediction can be achieved by integrating machine learning and linear scoring models to balance performance and interpretability, which can assist clinicians in treatment decisions.
Authors
- Yuqing Yang
- Zhenzhen Liu (ORCID: https://orcid.org/0000-0003-2141-6665)
- Xinyan Liu (ORCID: https://orcid.org/0000-0001-8575-6883)
- Xin Wang (ORCID: https://orcid.org/0009-0004-6761-2682)
- Zhenhong Qi
- Siting Peng
- Liyuan Ma
- Qing Dai (ORCID: https://orcid.org/0000-0002-7322-4334)
- Na Su (ORCID: https://orcid.org/0000-0001-6466-1797)
- Jihao Wu (ORCID: https://orcid.org/0000-0003-3235-0446)
- Jianchu Li (ORCID: https://orcid.org/0000-0002-1529-1504)
Institutions
- Beijing University of Posts and Telecommunications (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- National Clinical Research Center for Digestive Diseases (CN)
Publication Details
- Journal
- Journal of Ultrasound in Medicine
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1002/jum.16688
- Citations
- 1
- Primary Topic
- Ectopic Pregnancy Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- National Natural Science Foundation of China