In contrast with the reporting requirements currently mandated under the Federal Mammography Quality Standards Act (MQSA), we propose a modification of the Breast Imaging Reporting and Data System (Bi-Rads) in which a concluding assessment category is assigned, not to the examination as a whole, but to every potentially malignant abnormality observed. This modification improves communication between the radiologist and the attending clinician, thereby facilitating clinical judgment leading to appropriate management. In patients with breast cancer eligible for breast conserving therapy, application of this modification brings to attention the necessity for such patients to undergo pretreatment biopsies of all secondary, synchronous ipsilateral lesions scored Bi-Rads 3-5. All contralateral secondary lesions scored Bi-Rads 3-5 also require pretreatment biopsies. The application of this modification of the MSQA demonstrates the necessity to alter current recommendations ("short-interval follow-up") for secondary, synchronous Bi-Rads 3 ("probably benign") image-detected abnormalities prior to treatment of the index malignancy.
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http://dx.doi.org/10.1111/tbj.12492 | DOI Listing |
BMC Med Imaging
January 2025
Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.
Background: Benign and malignant breast tumors differ in their microvasculature morphology and distribution. Histologic biomarkers of malignant breast tumors are also correlated with the microvasculature. There is a lack of imaging technology for evaluating the microvasculature.
View Article and Find Full Text PDFBMC Med Imaging
January 2025
Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Problem: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques.
View Article and Find Full Text PDFNat Protoc
January 2025
Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Premetastatic cancer cells often spread from the primary lesion through the lymphatic vasculature and, clinically, the presence or absence of lymph node metastases impacts treatment decisions. However, little is known about cancer progression via the lymphatic system or of the effect that the lymphatic environment has on cancer progression. This is due, in part, to the technical challenge of studying lymphatic vessels and collecting lymph fluid.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Radiology, The Second Affiliated Hospital of Wannan Medical College, Kangfu Road, Wuhu, 241006, China.
This study aimed to develop a Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression (LR) model using quantitative imaging features from Shear Wave Elastography (SWE) and Contrast-Enhanced Ultrasound (CEUS) to assess the malignancy risk of BI-RADS 4 breast lesions (BLs). The features predictive of malignancy in the LASSO analysis were used to construct a nomogram. Female patients (n = 111) with BI-RADS 4 BLs detected via routine ultrasound at Ma'anshan People's Hospital underwent SWE, CEUS, and histopathological examinations were enrolled in this study.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.
Advancements in high-throughput screenings enable the exploration of rich phenotypic readouts through high-content microscopy, expediting the development of phenotype-based drug discovery. However, analyzing large and complex high-content imaging screenings remains challenging due to incomplete sampling of perturbations and the presence of technical variations between experiments. To tackle these shortcomings, we present IMage Perturbation Autoencoder (IMPA), a generative style-transfer model predicting morphological changes of perturbations across genetic and chemical interventions.
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