Predicting preoperative lymph node status in patients with cervical cancer: development of interpretable machine learning model and support for the biological plausibility
This study investigated the notable association between monocyte count and lymph node metastasis, highlighting the importance of monocytes in cervical cancer via bulk RNA-sequencing and single cell RNA-sequencing analysis. The developed interpretable machine learning models effectively aid clinicians in decision-making processes. Additionally, the Shapley Additive Explanation method improved the applicability of these machine learning models in real world.
Authors
- Hui Shen (ORCID: https://orcid.org/0000-0003-0335-6064)
- Yuting Jiang
- Hongning Xie (ORCID: https://orcid.org/0000-0002-1705-1328)
- Lihong Wu (ORCID: https://orcid.org/0000-0002-4561-9400)
- Han Bai
- Lihe Zhang (ORCID: https://orcid.org/0009-0009-6580-5743)
- Liu Du (ORCID: https://orcid.org/0000-0003-0381-3085)
- Zheng Qiao (ORCID: https://orcid.org/0000-0001-5765-4644)
Institutions
- Sun Yat-sen University (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-10-10
- DOI
- https://doi.org/10.3389/fimmu.2025.1654332
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00