Preoperative discrimination of invasive and non-invasive breast cancer using machine learning based on automated breast volume scanning (ABVS) radiomics and virtual touch quantification (VTQ)
Our findings indicate that machine learning models, which integrate ABVS radiomics and VTQ, are significantly effective in preoperatively distinguishing between invasive and non-invasive breast cancer. Particularly, the DT model stood out in the validation set, establishing it as the primary model in our study. This highlights its potential utility in enhancing clinical decision-making processes.
Authors
- Lifang Fan (ORCID: https://orcid.org/0000-0002-3996-497X)
- Shujian Wu (ORCID: https://orcid.org/0000-0002-3264-6901)
- Xiangming Zhu (ORCID: https://orcid.org/0009-0008-7974-047X)
- Chaoxue Zhang
- Yimin Wu
Institutions
- Wannan Medical College (CN)
- Anhui Medical University (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
- First Affiliated Hospital of Wannan Medical College (CN)
- East China Normal University (CN)
Publication Details
- Journal
- Discover Oncology
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1007/s12672-024-01438-7
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81