An auxiliary diagnosis model for the pathological classification of cervical cancer based on radiomics biomarkers
The proposed deep learning-radiomics framework enables accurate lesion segmentation and effective pathological classification of cervical cancer. This auxiliary diagnostic model may reduce unnecessary invasive procedures and improve early screening and clinical decision-making.
Authors
- Y. Cao (ORCID: https://orcid.org/0000-0002-8036-8491)
- Qin Zhou (ORCID: https://orcid.org/0000-0001-5334-7188)
- Mengchen Zhu (ORCID: https://orcid.org/0009-0005-7440-3026)
- Mei Wang
- Peilin Zhao
- Junqiang Lei
- Jizhao Liu
- Hongxiang Lan
Institutions
- Lanzhou Jiaotong University (CN)
- Lanzhou University Second Hospital (CN)
- First Hospital of Lanzhou University (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Frontiers in Genetics
- Published
- 2026-03-11
- DOI
- https://doi.org/10.3389/fgene.2026.1685927
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Gansu Province
- Science and Technology Department of Gansu Province