Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma
Delta-radiomics features obtained from CECT and non-CECT images using RSF successfully predict the incidence of DM in patients with borderline resectable pancreatic carcinoma.
Authors
- Takashi Mizowaki (ORCID: https://orcid.org/0000-0002-8135-8746)
- Michio Yoshimura (ORCID: https://orcid.org/0000-0001-6665-2245)
- Takahiro Iwai (ORCID: https://orcid.org/0000-0003-0078-9235)
- Takanori Adachi (ORCID: https://orcid.org/0000-0003-1356-5118)
- Mitsuhiro Nakamura (ORCID: https://orcid.org/0000-0002-6406-2097)
Institutions
- Kyoto University (JP)
- Kyoto University Hospital (JP)
Publication Details
- Journal
- Advances in Radiation Oncology
- Published
- 2024-11-05
- DOI
- https://doi.org/10.1016/j.adro.2024.101669
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- Takeda Science Foundation
- Foundation for Promotion of Cancer Research
- Japan Society for the Promotion of Science