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 breas...
Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagn...
PURPOSE: To evaluate the feasibility of multiplexed sensitivity-encoding (MUSE) with deep learning-based reconstruction (DLR) for breast imaging in comparison with conventional diffusion-weighted imaging (DWI) and MUSE alone.
BACKGROUND: Accurate preoperative prediction of vascular invasion in breast cancer is crucial for surgical planning and patient management. MRI radiomics has shown promise in enhancing diagnostic precision. This study aims to evaluate the effectivene...
Direct sampling mass spectrometry (MS) has rapidly advanced with the development of ambient ionization MS techniques. Swab touch-spray (TS)-MS has shown promise for rapid clinical diagnostics. However, commercially available swabs are notorious for t...
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
39823915
PURPOSE: To train and validate KB prediction models by merging a large multi-institutional cohort of whole breast irradiation (WBI) plans using tangential fields.
International journal of computer assisted radiology and surgery
39812891
PURPOSE: Breast cancer remains one of the most prevalent cancers globally, necessitating effective early screening and diagnosis. This study investigates the effectiveness and generalizability of our recently proposed data augmentation technique, att...
OBJECTIVE: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2) and a conventional T...
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 image...
Purpose To evaluate the change in digital breast tomosynthesis artificial intelligence (DBT-AI) case scores over sequential screenings. Materials and Methods This retrospective review included 21 108 female patients (mean age ± SD, 58.1 years ± 11.5)...