A state-of-the-art review highlights the use of multimodal imaging and AI to improve diagnosis and management of radiation-induced brain injury (RIBI).
Key Details
- 1Radiation-induced brain injury (RIBI) is a complex complication following cranial radiotherapy, impacting neurological function and quality of life.
- 2Multimodal imaging methods—including structural/functional MRI, diffusion and perfusion imaging, PET/CT, and radiomics—enhance early detection and differential diagnosis of RIBI versus tumor recurrence.
- 3AI techniques and machine learning models enable extraction of quantitative features, promising improved non-invasive diagnosis accuracy.
- 4Current interventions are shifting towards targeted, mechanism-driven therapies; Bevacizumab remains the only validated drug for radiation necrosis, while experimental approaches like stem cell therapy and neuromodulation are under study.
- 5Major challenges include lack of unified diagnostic criteria, early biomarkers, and seamless clinical integration of multimodal imaging and AI.
- 6The article calls for standardized protocols, expanded research, and multidisciplinary collaboration to achieve precision management.
Why It Matters
RIBI is an increasingly recognized complication given longer brain tumor survival, yet its diagnosis remains difficult using conventional imaging. Integrating advanced multimodal techniques and AI-driven analysis could transform early detection and precision treatment, representing a critical area for radiology and imaging AI innovation.
Source
EurekAlert
Related News
•EurekAlert
AI Model Accurately Predicts Blood Loss Risk in Liposuction
A machine learning model predicts blood loss during high-volume liposuction with 94% accuracy.
•EurekAlert
AI-Driven CT Tool Predicts Cancer Spread in Oropharyngeal Tumors
Researchers have created an AI tool that uses CT imaging to predict the spread risk of oropharyngeal cancer, offering improved treatment stratification.
•EurekAlert
AI Model PRTS Predicts Spatial Transcriptomics From H&E Histology Images
Researchers developed PRTS, a deep learning model that infers single-cell spatial transcriptomics from standard H&E-stained tissue images.