Predicting pathological complete response to chemoradiotherapy using artificial intelligence-based magnetic resonance imaging radiomics in esophageal squamous cell carcinoma
AI-based radiomics analysis of DWI-MRI images in ESCC has the potential to accurately predict the effect of CRT before treatment and contribute to constructing optimal treatment strategies.
Authors
- Atsushi Hirata (ORCID: https://orcid.org/0000-0002-4865-3807)
- Gaku Ohira (ORCID: https://orcid.org/0000-0001-7246-5390)
- Koichi Hayano (ORCID: https://orcid.org/0000-0003-4733-8220)
- Takeshi Toyozumi (ORCID: https://orcid.org/0000-0002-0939-3299)
- Masaya Uesato (ORCID: https://orcid.org/0000-0002-6766-5600)
- Yasunori Matsumoto (ORCID: https://orcid.org/0000-0002-6239-6691)
- Y. Kurata (ORCID: https://orcid.org/0000-0001-8333-2032)
- Tadashi Shiraishi (ORCID: https://orcid.org/0000-0003-1424-3049)
- Akira Nakano (ORCID: https://orcid.org/0000-0002-2506-1825)
- Nobufumi Sekino (ORCID: https://orcid.org/0000-0002-7705-036X)
- Toru Tochigi (ORCID: https://orcid.org/0000-0002-8734-3975)
Institutions
- Chiba University (JP)
Publication Details
- Journal
- World Journal of Gastroenterology
- Published
- 2025-09-18
- DOI
- https://doi.org/10.3748/wjg.v31.i36.111293
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36