Philip Ward, Erik Ridley, Kate Madden Yee, and Brian Casey

2017
Top 5 trends from ECR 2017 in Vienna
The stunning flower-and-skull display that formed the centerpiece of ECR 2017 certainly looked impressive in everybody's photos, on social media, and in the daily newspaper. But what were the truly lasting impressions and meaningful issues to take away from this year's congress in Vienna? Our editorial team has put together a list of five key themes.
March 6, 2017
2017 03 07 12 20 23 435 2017 03 07 Floral Skull Ecr 2017 20170307194549
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Top Stories
Pediatric Imaging
Practice Management
Patient-friendly radiology reports improve family comprehension
Terminology in radiology reports can be difficult for patients and families to comprehend.
Ct Scanner 400
CT
Early CTA leads to improved stroke outcomes for patients with BCVI
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Interventional
Most histotripsy procedures used for metastatic disease treatment
Radiologist Computer Screen X Ray
Administration
Imaging turnaround times continued to climb in 2024
(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.
Breast
DBT-based foundational model classifies breast density
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The largest increases were found in ultrasound and x-ray.
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DBT-based foundational model classifies breast density
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DBT-DINO showed strong performance in classifying breast density from DBT images.
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(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.
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