Artificial intelligence (AI) applications in mammography have gained significant popular attention; however, AI has the potential to revolutionize other aspects of breast imaging beyond simple lesion detection. AI has the potential to enhance risk assessment by combining conventional factors with imaging and improve lesion detection through a comparison with prior studies and considerations of symmetry. It also holds promise in ultrasound analysis and automated whole breast ultrasound, areas marked by unique challenges. AI's potential utility also extends to administrative tasks such as MQSA compliance, scheduling, and protocoling, which can reduce the radiologists' workload. However, adoption in breast imaging faces limitations in terms of data quality and standardization, generalizability, benchmarking performance, and integration into clinical workflows. Developing methods for radiologists to interpret AI decisions, and understanding patient perspectives to build trust in AI results, will be key future endeavors, with the ultimate aim of fostering more efficient radiology practices and better patient care.
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http://dx.doi.org/10.3390/diagnostics13132133 | DOI Listing |
Breast Cancer Res Treat
January 2025
Google Health, 1600 Amphitheatre Pkwy, Mountain View, CA, 94043, USA.
Purpose: Many breast centers are unable to provide immediate results at the time of screening mammography which results in delayed patient care. Implementing artificial intelligence (AI) could identify patients who may have breast cancer and accelerate the time to diagnostic imaging and biopsy diagnosis.
Methods: In this prospective randomized, unblinded, controlled implementation study we enrolled 1000 screening participants between March 2021 and May 2022.
Radiother Oncol
January 2025
Department of Radiation Oncology Olivia Newton-John Cancer Wellness & Research Centre Austin Health Victoria Australia; Department of Medical Imaging and Radiation Sciences, Monash University Clayton Victoria Australia; Genesis Care, Ringwood Private Hospital Victoria Australia.
Background And Purpose: Compare breast cancer tumour bed (TB) delineation using stabilised hyaluronic acid (sHA) gel and MRI-simulation versus surgical clips and CT-simulation within same patient cohort.
Materials And Methods: Prospective single arm study of patients undergoing breast conserving surgery. Patients had both clips (≥5) and sHA gel markers inserted to define the TB and underwent MRI and CT simulation scans.
Magn Reson Imaging
January 2025
GE Healthcare, Guangzhou 510623, China.
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 effectiveness of integrating MRI radiomic features with clinical data using a deep learning approach to predict vascular invasion in breast cancer patients.
View Article and Find Full Text PDFInt J Pharm
January 2025
The Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, No.157 Xiwu Road, Xi'an, Shaanxi 710004, China. Electronic address:
Both photothermal therapy (PTT) and chemodynamic therapy (CDT) are designed to focus their antitumor effect on only the tumor site, thereby minimizing unwanted severe damage to healthy tissue outside the tumor. However, each monotherapy is limited in achieving complete tumor eradication, resulting in tumor recurrence. The combination of multiple therapies may help to overcome the limitations of single therapy, improve the chances of complete tumor eradication, and reduce the risk of recurrence.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Cancer Screening, American Cancer Society, Atlanta, GA, United States.
Background: The online nature of decision aids (DAs) and related e-tools supporting women's decision-making regarding breast cancer screening (BCS) through mammography may facilitate broader access, making them a valuable addition to BCS programs.
Objective: This systematic review and meta-analysis aims to evaluate the scientific evidence on the impacts of these e-tools and to provide a comprehensive assessment of the factors associated with their increased utility and efficacy.
Methods: We followed the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted a search of MEDLINE, PsycINFO, Embase, CINAHL, and Web of Science databases from August 2010 to April 2023.
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