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
•AuntMinnie
Toronto Study: LLMs Must Cite Sources for Radiology Decision Support
University of Toronto researchers found that large language models (LLMs) such as DeepSeek V3 and GPT-4o offer promising support for radiology decision-making in pancreatic cancer when their recommendations cite guideline sources.
•AuntMinnie
AI Model Using Mammograms Enhances Five-Year Breast Cancer Risk Assessment
A new image-only AI model more accurately predicts five-year breast cancer risk than breast density alone, according to multinational research presented at RSNA 2025.
•Radiology Business
AI Model Uses CT Scans to Reveal Biomarker for Chronic Stress
Researchers developed an AI model to measure chronic stress using adrenal gland volume on routine CT scans.