Breast density determined by breast radiologists and also automatically estimated by applications has been widely investigated. However, no study has yet clarified whether the use of these applications by breast radiologists improves reading efficacy. Therefore, this study aimed to assess the usefulness of applications when used by breast radiologists. A application (App) developed by Konica Minolta, Inc. (Tokyo, Japan) was used. Independent and sequential tests were conducted to assess the usefulness of the concurrent- and second-look modes. Fifty and 100 cases were evaluated using sequential and independent tests, respectively. Each dataset was configured based on the evaluation by an expert breast radiologist who developed the Japanese guidelines for breast density. Nine breast radiologists evaluated the mammary gland content ratio and breast density; the inter-observer and expert-to-observer variability were calculated. The time required to complete the experiments was also recorded. The inter-observer variability was significant with the App, as revealed by the independent test. The use of the App significantly improved the agreement between the responses of the observers for the mammary gland content ratio and those of the expert by 6.6% and led to a reduction of 186.9 seconds in the average time required by the observers to evaluate 100 cases. However, the results of the sequential test did not suggest the effectiveness of the App. These findings suggest that the concurrent use of the App improves reading efficiency.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11254854 | PMC |
http://dx.doi.org/10.7759/cureus.62560 | DOI Listing |
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.
Self-supervised learning (SSL) has gained attention in the medical field as a deep learning approach utilizing unlabeled data. The Jigsaw puzzle task in SSL enables models to learn both features of images and the positional relationships within images. In breast cancer diagnosis, radiologists evaluate not only lesion-specific features but also the surrounding breast structures.
View Article and Find Full Text PDFFront Radiol
December 2024
Center for Artificial Intelligence in Medicine, University of Bern, Bern, Switzerland.
Purpose: Successful performance of deep learning models for medical image analysis is highly dependent on the quality of the images being analysed. Factors like differences in imaging equipment and calibration, as well as patient-specific factors such as movements or biological variability (e.g.
View Article and Find Full Text PDFCurr Probl Diagn Radiol
January 2025
Department of Medical Imaging, University of Arizona, 1501 N Campbell Ave, Tucson AZ 85724, USA; Banner University Medical Center Tucson, 1625 N Campbell Ave, Tucson AZ 85719, USA.
Breast magnetic resonance imaging (MRI) has the highest sensitivity for breast cancer detection compared to other breast imaging modalities such as mammography and ultrasound. As a functional modality, it captures the increased angiogenic activity of breast cancer through gadolinium-based contrast enhancement. Normal breast tissue also enhances, albeit in distinct patterns termed background parenchymal enhancement (BPE).
View Article and Find Full Text PDFEur J Breast Health
January 2025
Department of Biomedical Engineering, Yeditepe University Faculty of Engineering, İstanbul, Turkey.
Objective: To investigate integrating an artificial intelligence (AI) system into diagnostic breast ultrasound (US) for improved performance.
Materials And Methods: Seventy suspicious breast mass lesions (53 malignant and 17 benign) from seventy women who underwent diagnostic breast US complemented with shear wave elastography, US-guided core needle biopsy and verified histopathology were enrolled. Two radiologists, one with 15 years of experience and the other with one year of experience, evaluated the images for breast imaging-reporting and data system (BI-RADS) scoring.
Sci Rep
December 2024
Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766, Jingshi Road, Jinan, 250014, Shandong, People's Republic of China.
This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound images to assist radiologists in distinguishing between benign breast lesions (BBL) and malignant breast lesions (MBL). A total of 382 patients with breast lesions were included, comprising 183 benign lesions and 199 malignant lesions that were collected and confirmed through clinical pathology or biopsy. The enrolled patients were randomly allocated into two groups: a training cohort and an independent test cohort, maintaining a ratio of 7:3.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!