AIMC Topic: Breast Neoplasms
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The impact of updated imaging software on the performance of machine learning models for breast cancer diagnosis: a multi-center, retrospective study.
Archives of gynecology and obstetrics
Jan 30, 2025
PURPOSE: Artificial Intelligence models based on medical (imaging) data are increasingly developed. However, the imaging software on which the original data is generated is frequently updated. The impact of updated imaging software on the performance...
BCT-Net: semantic-guided breast cancer segmentation on BUS.
Medical & biological engineering & computing
Jan 30, 2025
Accurately and swiftly segmenting breast tumors is significant for cancer diagnosis and treatment. Ultrasound imaging stands as one of the widely employed methods in clinical practice. However, due to challenges such as low contrast, blurred boundari...
Hybrid transformer-based model for mammogram classification by integrating prior and current images.
Medical physics
Jan 30, 2025
BACKGROUND: Breast cancer screening via mammography plays a crucial role in early detection, significantly impacting women's health outcomes worldwide. However, the manual analysis of mammographic images is time-consuming and requires specialized exp...
Operational Advantages of Novel Strategies Supported by Portability and Artificial Intelligence for Breast Cancer Screening in Low-Resource Rural Areas: Opportunities to Address Health Inequities and Vulnerability.
Medicina (Kaunas, Lithuania)
Jan 30, 2025
Early detection of breast cancer plays a crucial role in reducing the number of cases diagnosed at advanced stages, thereby lowering the high healthcare costs required to achieve disease-free survival and helping to prevent avoidable premature deaths...
Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images.
Scientific reports
Jan 30, 2025
The most common carcinoma-related cause of death among women is breast cancer. Early detection is crucial, and the manual screening method may lead to a delayed diagnosis, which would delay treatment and put lives at risk. Mammography imaging is advi...
Triaging mammography with artificial intelligence: an implementation study.
Breast cancer research and treatment
Jan 29, 2025
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 accelera...
Improved early detection accuracy for breast cancer using a deep learning framework in medical imaging.
Computers in biology and medicine
Jan 29, 2025
PROBLEM: The most prevalent cancer in women is breast cancer (BC), and effective treatment depends on being detected early. Many people seek medical imaging techniques to help in the early detection of problems, but results often need to be corrected...
Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis.
Scientific reports
Jan 29, 2025
Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasi...
Unveiling the role of PANoptosis-related genes in breast cancer: an integrated study by multi-omics analysis and machine learning algorithms.
Breast cancer research and treatment
Jan 28, 2025
BACKGROUND: The heterogeneity of breast cancer (BC) necessitates the identification of novel subtypes and prognostic models to enhance patient stratification and treatment strategies. This study aims to identify novel BC subtypes based on PANoptosis-...
F-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients.
La Radiologia medica
Jan 28, 2025
PURPOSE: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [F]FDG PET/CT.
Predictive Value of Tests
Positron Emission Tomography Computed Tomography
Radiopharmaceuticals
Treatment Outcome
Neoadjuvant Therapy
Lymphatic Metastasis
Fluorodeoxyglucose F18
Chemotherapy, Adjuvant
Female
Aged
Adult
Breast Neoplasms
Radiomics
Middle Aged
Artificial Intelligence
Machine Learning
Retrospective Studies
Humans
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