A commercial AI system can identify up to 33% of interval breast cancers missed by radiologists on digital breast tomosynthesis exams.
Key Details
- 1Study published in Radiology tested AI on digital breast tomosynthesis (DBT) exams preceding confirmed interval cancer diagnoses.
- 2The AI algorithm (Lunit INSIGHT DBT v1.1) flagged up to one-third of interval cancers missed by radiologists.
- 3Interval breast cancers are often more aggressive and have worse prognoses than screen-detected cancers.
- 4Nearly 12 years of retrospective DBT data (Feb 2011–Jun 2023) were analyzed.
- 5Algorithm scored lesions; those over 10 marked as positive, and radiologist review correlated AI findings with actual cancer sites.
Why It Matters
Interval cancers are frequently aggressive and linked with higher morbidity and mortality due to later detection. AI's ability to identify a significant portion of these previously missed cancers suggests potential for earlier intervention and improved patient outcomes in breast imaging.
Source
Health Imaging
Related News
•AuntMinnie
AI-Based Slab Reconstruction Streamlines Digital Breast Tomosynthesis
AI-driven slab reconstruction in DBT improves workflow efficiency without compromising diagnostic accuracy in breast cancer screening.
•AuntMinnie
AI Model Predicts Dosimetry for Lu-177 PSMA Therapy Using PET/CT
A machine learning PET/CT model shows promise for predicting radiation dose prior to Lu-177 PSMA therapy in prostate cancer patients.
•AuntMinnie
AI Model Uses Ultrasound to Assess Fetal Lung Maturity
Researchers demonstrated an AI model's strong accuracy in measuring fetal lung maturity from ultrasound images.