Roberta Katz

IS
6 steps to success in radiology data analytics
The modern radiology practice continues to experience rapid growth in data, thanks to the ongoing development of imaging modalities and the use of advanced medical imaging in molecular, genomic, and precision medicine. As radiology transitions to value-based care, advanced data analytics is no longer an option.
June 2, 2016
2016 02 24 15 09 37 954 Data Analytics 200
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Radiologist Computer Screen X Ray
Administration
Imaging turnaround times continued to climb in 2024
The largest increases were found in ultrasound and x-ray.
(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
Ct Contrast
CT
Liebel-Flarsheim recalls Optiray CT contrast agent
Ai Head
AI
Can LLMs fill the communication gap with referring clinicians?
Aneurysm
Interventional
CT-selected thrombectomy shows 1-year benefit in large-core stroke
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Imaging turnaround times continued to climb in 2024
By Erik L. Ridley
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Radiologist Computer Screen X Ray
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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.
Liebel-Flarsheim recalls Optiray CT contrast agent
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Ct Contrast
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Representative axial fused F-18 FDG-PET/MRI images and corresponding anatomic MRI images from a patient imaged at baseline and after initiation of androgen deprivation therapy are shown. Each row displays a distinct vascular territory. White arrows indicate focal areas of increased tracer uptake within the arterial wall, where spherical volumes of interest (VOIs) were placed for maximum standardized uptake value (SUVmax) measurement. Additional VOIs were placed in the superior vena cava and inferior vena cava for blood-pool activity sampling. SUVmax values from the arterial VOIs were extracted on the corresponding PET images and normalized to the blood-pool mean standardized uptake value to derive the maximum target-to-background ratio.
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