We present an accurate and reliable method for localizing a mammographic lesion by ultrasound using a simple coordinate system. It does not require special grid equipment or additional personnel. We use our system, step-by-step, on a sample patient and include appropriate image documentation. The nipple is the point of reference or "origin". The lesion is located on ultrasound using its x and y coordinates, which are the two distances from the nipple in the horizontal and vertical axes, measured with an ordinary ruler or caliper tool. The true distance from the nipple can also easily be measured and reported. Our method is reproducible and shortens ultrasound exam times to less than 10 minutes.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/tbj.12005 | DOI Listing |
Radiographics
February 2025
From the Washington University School of Medicine, Mallinckrodt Institute of Radiology, 510 S Kingshighway Blvd, St. Louis, MO 63110.
Annual review of false-negative (FN) mammograms is a mandatory and critical component of the Mammography Quality Standards Act (MQSA) annual mammography audit. FN review can help hone reading skills and improve the ability to detect cancers at mammography. Subtle architectural distortion, asymmetries (seen only on one view), small lesions, lesions with probably benign appearance (circumscribed regular borders), isolated microcalcifications, and skin thickening are the most common mammographic findings when the malignancy is visible at retrospective review of FN mammograms.
View Article and Find Full Text PDFBreast Cancer Res Treat
January 2025
Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA, 02114, USA.
Purpose: Traditional computer-assisted detection (CADe) algorithms were developed for 2D mammography, while modern artificial intelligence (AI) algorithms can be applied to 2D mammography and/or digital breast tomosynthesis (DBT). The objective is to compare the performance of a traditional machine learning CADe algorithm for synthetic 2D mammography to a deep learning-based AI algorithm for DBT on the same mammograms.
Methods: Mammographic examinations from 764 patients (mean age 58 years ± 11) with 106 biopsy-proven cancers and 658 cancer-negative cases were analyzed by a CADe algorithm (ImageChecker v10.
Radiol Phys Technol
January 2025
Department of Diagnostic Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.
Clin Radiol
December 2024
Royal Liverpool University Hospital, Breast Radiology Unit, Liverpool, UK.
Aim: This study aimed to detail our experience of using SCOUT® radar reflector for lesion localisation in the breast and axilla.
Materials And Methods: This is a prospective cohort study describing our clinical experience with the first 500 patients who received SCOUT® to localise lesions in the breast and axilla (from 23 July 2020 to 4 April 2022). Study measures include patient demographics, lesion location, diagnostic pathways (screening or symptomatic), imaging, and surgical and pathology outcomes.
Med Phys
January 2025
Breast Imaging Department, Red Cross Hospital Munich, Munich, Germany.
Background: A significant proportion of false positive recalls of mammography-screened women is due to benign breast cysts and simple fibroadenomas. These lesions appear mammographically as smooth-shaped dense masses and require the recalling of women for a breast ultrasound to obtain complementary imaging information. They can be identified safely by ultrasound with no need for further assessment or treatment.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!