Integrative Radiogenomics Using MRI Radiomics and Microarray Gene Expression Analysis to Predict Pathological Complete Response in Patients with Breast Cancer Undergoing Neoadjuvant Chemotherapy
Machine learning-based radiogenomics, which combines MRI features and DNA microarray data, improved the accuracy of pCR prediction after NAC, although the improvement was not statistically significant. These findings suggest the potential utility of radiogenomics as a non-invasive tool to support treatment decision-making in patients undergoing NAC.
Authors
- Yasuto Naoi (ORCID: https://orcid.org/0000-0002-6090-0137)
- Shoji Kido (ORCID: https://orcid.org/0000-0001-5293-4110)
- Yuki Suzuki
- Yuichi Motoyama
- Yoshiaki Sota (ORCID: https://orcid.org/0000-0001-5963-652X)
- Noriyuki Tomiyama (ORCID: https://orcid.org/0000-0001-9918-7327)
- Eiichi Morii (ORCID: https://orcid.org/0000-0001-8490-3650)
- Yukiko Tokuda (ORCID: https://orcid.org/0000-0001-7520-3310)
- Kenzo Shimazu (ORCID: https://orcid.org/0000-0002-0837-2357)
- Masahiro Yanagawa (ORCID: https://orcid.org/0000-0002-0911-6769)
- Masatoshi Hori (ORCID: https://orcid.org/0000-0003-2732-0648)
- S. Oda
Institutions
- Kyoto Prefectural University (JP)
- Kyoto Prefectural University of Medicine (JP)
- The University of Osaka (JP)
Publication Details
- Journal
- Cureus
- Published
- 2025-06-18
- DOI
- https://doi.org/10.7759/cureus.86287
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72