The first episode of a new podcast available on the European Society of Radiology (ESR) website explores the role radiology in breast cancer diagnosis and treatment.
The Patient Podcast, produced by the ESR Patient Advisory Group, aims to foster open communication between patients, radiologists, and radiographers on key topics in radiology. The guests will be patient representatives, radiologists, and radiographers, and the objective is to provide insights from different perspectives, valuable for anyone with an interest in radiology, according to the producers.
Episode one features Caroline Justich, current chairperson of the ESR Patient Advisory Group (PAG) and founder of Be accepted, and Cheryl Cruwys, ESR-PAG member and European education coordinator at DenseBreast-info.
Justich and Cruwys share their personal experiences and discuss the modality that detected their own breast cancer, when and at what stage, and the circumstances (e.g., population breast cancer screening or an emergency situation) of how their cases were diagnosed eight years ago.
Other topics discussed include breast care centers, routine breast cancer screening, routine follow-up checks and blood testing, how to prepare for a breast cancer screening, communication with the radiologist, a tumor board experience, dense breasts, and experience with the radiographers.
Listen to the 29 October podcast here.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
