Development and validation of an interpretable shap-based machine learning model for predicting postoperative complications in laryngeal cancer
We developed a clinically interpretable ML model that accurately predicts major postoperative complications in patients undergoing laryngeal cancer surgery. This tool provides individualized risk assessments that can guide surgical planning, optimize perioperative strategies, and enhance shared decision-making. Prospective multicenter validation is needed to confirm its utility in routine practice.
Authors
- Changling Li (ORCID: https://orcid.org/0000-0002-2260-8733)
- Dongmin Wei (ORCID: https://orcid.org/0000-0001-6872-162X)
- Ye Qian (ORCID: https://orcid.org/0000-0002-0731-5596)
- Yu Zhang (ORCID: https://orcid.org/0000-0001-6927-3507)
- Ziwei Zhang (ORCID: https://orcid.org/0000-0003-0750-9726)
- Wenming Li (ORCID: https://orcid.org/0000-0003-4069-2251)
- Dapeng Lei (ORCID: https://orcid.org/0000-0002-3404-3207)
- Chenyang Xu (ORCID: https://orcid.org/0000-0003-1174-7233)
- Jinzhuang Xu (ORCID: https://orcid.org/0009-0002-6540-1406)
- Wenhua Song
Institutions
- Shandong University (CN)
- National Health and Family Planning Commission (CN)
- Shandong Province Health and Family Planning Commission (CN)
- Qilu Hospital of Shandong University (CN)
Publication Details
- Journal
- BMC Surgery
- Published
- 2025-10-09
- DOI
- https://doi.org/10.1186/s12893-025-03213-z
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Shandong University
- Qilu Hospital of Shandong University
- National Natural Science Foundation of China
- Natural Science Foundation of Shandong Province