AI-enhanced breast MRI with a 75% reduced gadolinium dose maintained diagnostic sensitivity comparable to full-dose protocols.
Key Details
- 1Single-center study with 20 female participants having biopsy-proven malignancies or high-risk indications.
- 2Low-dose (0.025 mmol/kg) MRI scans with AI were compared to standard-dose (0.1 mmol/kg) with/without AI.
- 3Diagnostic sensitivity with low-dose + AI was 83.3% (p=1 vs. standard), accuracy was 74.5% vs. 69.5% (standard) and 65.5% (standard+AI).
- 498 lesions detected; 12 were biopsy-proven malignancies; high NPVs (93-94%) across all protocols.
- 5AI-assisted low-dose MRI had the highest PPV (50%) and substantial interreader agreement (kappa 0.65-0.74).
- 6Study was small and lacked long-term outcome data; funded by Bracco.
Why It Matters
Gadolinium retention and cost are growing concerns in breast imaging. This study shows that AI-powered image enhancement could allow safer, lower-dose protocols without compromising cancer detection, marking a significant step for both patient safety and clinical feasibility.
Source
AuntMinnie
Related News
•AuntMinnie
AI Projected to Reshape Radiologist Workload But Not Eliminate Jobs
Stanford researchers predict AI could reduce radiologist hours by up to 49% over the next five years, though workforce size is likely to remain stable due to rising imaging demand.
•Radiology Business
Imaging AI Systems Could Play Key Role in Future Pandemic Response
A study finds that repurposed AI detection tools in radiology could help address future pandemics more effectively.
•AuntMinnie
XGBoost Outperforms Logistic Regression for Lung Cancer Risk Prediction
XGBoost-based lung cancer risk prediction model shows greater accuracy than logistic regression in a large screening cohort.