Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning
AutoML, based on the TPOT algorithm, holds potential as a noninvasive method for effectively discriminating between benign and malignant testicular lesions.
Authors
- Xiangde Min (ORCID: https://orcid.org/0000-0002-1589-6861)
- Wenzhi Lv (ORCID: https://orcid.org/0000-0002-8823-1342)
- Peipei Zhang (ORCID: https://orcid.org/0000-0003-4405-0432)
- Yanhui Feng
- Liang Wang (ORCID: https://orcid.org/0000-0002-8468-6278)
- Jin Li
- Zhiyong Liu (ORCID: https://orcid.org/0000-0003-2649-6211)
- Zhaoyan Feng
Institutions
- Wuhan National Laboratory for Optoelectronics (CN)
- Tongji Hospital (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-03-28
- DOI
- https://doi.org/10.1186/s12885-025-13844-3
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- National Natural Science Foundation of China