An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery
An XGBoost model was developed to predict the likelihood of ELM after PDAC surgery with high accuracy. Additionally, the model was implemented as an application, providing clinicians with an accessible visual tool to support personalized clinical strategies and ultimately enhance patient outcomes.
Authors
- Junyi Qiu (ORCID: https://orcid.org/0000-0001-7662-6921)
- Xiaofeng Xue (ORCID: https://orcid.org/0000-0002-0963-7994)
- Xiaojie Gan (ORCID: https://orcid.org/0000-0002-6095-483X)
- Kejia Wu (ORCID: https://orcid.org/0000-0003-3985-2908)
- Yiyan Zhou
- Weigang Zhang (ORCID: https://orcid.org/0000-0002-2488-159X)
- Hao Zhu (ORCID: https://orcid.org/0000-0003-2809-1111)
- Danyang Shen
- Ding Sun
- Xiaohua Yang
Institutions
- Nantong University (CN)
- Soochow University (CN)
- Affiliated Hospital of Nantong University (CN)
- First Affiliated Hospital of Soochow University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-07-01
- DOI
- https://doi.org/10.1186/s12885-025-14503-3
- Citations
- 4
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 4.18
Funders
- National Natural Science Foundation of China