Adding MRI to a multimodal AI model significantly improves breast cancer detection and risk prediction.
Key Details
- 1NYU Langone researchers evaluated a transformer AI model using mammography, DBT, ultrasound, breast MRI, and clinical variables.
- 2The training set included 1.3 million exams from 274,388 women (2010–2022).
- 3A separate test cohort comprised 1,944 women with 18,201 exams.
- 4Without MRI input, the AUROC for 5-year cancer risk prediction was 0.899; with MRI, it increased to 0.94.
- 5Improved model performance allows better identification of high-risk women for screening and prevention.
Why It Matters
This work demonstrates that integrating MRI with other imaging modalities in AI models can meaningfully enhance risk stratification and detection accuracy for breast cancer. Such advances support precision medicine, enabling more targeted screening and preventive interventions in clinical radiology.
Source
AuntMinnie
Related News
•AuntMinnie
Deep Learning Model Enhances RPE Detection on Neck MRI
Finnish researchers developed a deep learning algorithm that accurately detects retropharyngeal edema on MRI neck scans.
•AuntMinnie
AI Advancements and Studies Highlighted in Digital X-Ray Insider
This edition covers AI models for fracture detection, mortality prediction, and more, along with new research using x-ray and DEXA modalities.
•AuntMinnie
AI Model Accurately Detects Pediatric Physeal Fractures on X-Ray
A deep learning model accurately identifies hard-to-detect physeal fractures in children's wrist x-rays.