AI-powered MRI radiomics significantly improves prediction of treatment response in advanced liver cancer patients.
Key Details
- 1Multicenter study presented at ASCO 2025 focused on advanced hepatocellular carcinoma (HCC).
- 2AI-based radiomics model analyzed MRI data to predict response to atezolizumab and bevacizumab therapy.
- 3Study included 240 patients; training cohort of 161, validation cohort of 79.
- 4Radiomics model achieved AUC of 0.913 (training) and 0.825 (validation); combined with a key MRI feature, AUC increased to 0.951 and 0.835, respectively.
- 5Significant correlation found between radiomic and conventional MRI features for intrahepatic lesions.
Why It Matters
This work demonstrates the power of AI-driven radiomics to personalize cancer treatment planning, improve patient selection for liver cancer therapies, and highlights growing integration of advanced imaging analytics in clinical oncology workflows.
Source
AuntMinnie
Related News
•Radiology Business
Meta Funds Ohio State’s Radiology AI Innovation Projects
Meta will fund Ohio State researchers to explore AI-driven advancements in radiology, including chest X-ray reporting and disease prediction.
•AuntMinnie
AI Triage Tool Speeds Up ICH Head CT Turnaround in ED
AI-based triage for intracranial hemorrhage on head CT reduced emergency department report turnaround by 13 minutes.
•AuntMinnie
ChatGPT-5 Performs Poorly at Detecting Distal Radius Fractures on X-rays
ChatGPT-5 missed about half of distal radius fractures on x-rays, showing limited diagnostic capability in a real-world scenario.