AuntMinnie.com Women's Imaging Insider

Shalmali Pal
Aug 20, 2006

Dear Women's Imaging Insider,

MRI bested CT for staging breast cancer and for evaluating any metastasis. Our Insider Exclusive highlights the findings from two studies that validate MR's superiority to CT for characterizing hepatic lesions and the modality's ability to reveal intraductal carcinoma. Click here for the details.

You can also find the latest news in computer-aided detection (CAD) for mammography in the Women's Imaging Digital Community. Learn about how a CAD multimodality workstation can improve radiologist performance for differentiating malignant and benign breast lesions. Plus, find out how to submit your comments to the U.S. Centers for Medicare and Medicaid Services (CMS) on a proposed cut to the reimbursement rate for CAD with digital mammography.

Shifting gears, there are also several articles on maternal and fetal imaging. First, read about the relative safety of sentinel lymph node biopsy with a radiocolloid in pregnant women with breast cancer. Another article has the International Intersex Consensus recommending abdominopelvic sonography for assessing sex development disorders. Also, Chinese clinicians have developed an ultrasound-guided interstitial laser treatment to restore fertility in women with polycystic ovary syndrome.

Finally, ultrasound specialists suggest spatiotemporal image correlation and tomographic ultrasound imaging to simplify fetal echocardiography.

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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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(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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