Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support vector machine
Incorporating pathomics and DMS-PSO optimization significantly improved NPC oligometastasis prediction. This model showed high discriminative ability, calibration, and clinical utility, suggesting that pathomics and machine learning-based strategies could aid early recognition of high-risk patients and inform individualized treatment approaches. A demo of the DMS-PSO-SVM modeling algorithm code used in this study can be found on Github (https://github.com/Edward-E-S-Wang/DMS-PSO-SVM).
Authors
- Dongni Zhang (ORCID: https://orcid.org/0000-0001-9788-3249)
- Yiheng Hu (ORCID: https://orcid.org/0000-0002-6513-9834)
- Cheng‐Han Yang (ORCID: https://orcid.org/0000-0001-7517-3799)
- Wenhui Cheng (ORCID: https://orcid.org/0009-0009-7007-6725)
- Yunfei Li (ORCID: https://orcid.org/0000-0003-1734-5008)
- Yiren Wang (ORCID: https://orcid.org/0000-0001-6757-5923)
- Ping Zhou (ORCID: https://orcid.org/0000-0002-0306-3520)
- Zhongjian Wen
Institutions
- Southwest Medical University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-06-19
- DOI
- https://doi.org/10.3389/fonc.2025.1589919
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 2.48