Attention-Guided Deep Learning of Chemical Exchange Saturation Transfer Magnetic Resonance Imaging to Differentiate Between Tumor Progression and Radiation Necrosis in Brain Metastasis
Attention-guided deep-learning analysis of chemical exchange saturation transfer MRI shows strong potential for accurately distinguishing RN from TP, underscoring the significance of multimodal MRI inputs for post-SRS lesion evaluation.
Authors
- Arjun Sahgal (ORCID: https://orcid.org/0000-0001-7991-8970)
- Ali Sadeghi‐Naini (ORCID: https://orcid.org/0000-0001-5055-339X)
- Wilfred Lam (ORCID: https://orcid.org/0000-0002-9883-6142)
- Greg J. Stanisz (ORCID: https://orcid.org/0000-0002-9488-7612)
- Pejman Maralani (ORCID: https://orcid.org/0000-0001-9975-4379)
- Rachel W. Chan (ORCID: https://orcid.org/0000-0003-0475-1111)
- Dylan Young (ORCID: https://orcid.org/0000-0001-6391-5791)
- Hany Soliman
- Nauman Bashir Bhatti
Institutions
- St. Michael's Hospital (CA)
- Sunnybrook Health Science Centre (CA)
- Sunnybrook Hospital (CA)
Publication Details
- Journal
- International Journal of Radiation Oncology*Biology*Physics
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1016/j.ijrobp.2025.10.040
- Primary Topic
- Lanthanide and Transition Metal Complexes
- Type
- article
- Field-Weighted Citation Impact
- 0.00