AIMC Topic: Breast Neoplasms

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Consistency of Artificial Intelligence (AI)-based Diagnostic Support Software in Short-term Digital Mammography Reimaging After Core Needle Biopsy.

Journal of digital imaging
To evaluate the consistency in the performance of Artificial Intelligence (AI)-based diagnostic support software in short-term digital mammography reimaging after core needle biopsy. Of 276 women who underwent short-term (<3 mo) serial digital mammog...

Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images.

Scientific reports
Human epidermal growth factor receptor 2 (HER2) gene amplification helps identify breast cancer patients who may respond to targeted anti-HER2 therapy. This study aims to develop an automated method for quantifying HER2 fluorescence in situ hybridiza...

Interpretable HER2 scoring by evaluating clinical guidelines through a weakly supervised, constrained deep learning approach.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The evaluation of the Human Epidermal growth factor Receptor-2 (HER2) expression is an important prognostic biomarker for breast cancer treatment selection. However, HER2 scoring has notoriously high interobserver variability due to stain variations ...

Controversies and strengths of robot-assisted mastectomy.

European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP)
Nipple-sparing mastectomy (NSM) is used to improve cosmetic outcomes while maintaining oncological safety in patients with early breast cancer; however, NSM requires a higher level of skill and workload than mastectomy and is associated with long, vi...

CONFIDENT-trial protocol: a pragmatic template for clinical implementation of artificial intelligence assistance in pathology.

BMJ open
INTRODUCTION: Artificial intelligence (AI) has been on the rise in the field of pathology. Despite promising results in retrospective studies, and several CE-IVD certified algorithms on the market, prospective clinical implementation studies of AI ha...

Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study.

European radiology experimental
BACKGROUND: International societies have issued guidelines for high-risk breast cancer (BC) screening, recommending contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast as a supplemental diagnostic tool. In our study, we tested the app...

Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies.

Journal of cancer research and clinical oncology
BACKGROUND: Breast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine learning and deep learning techniques have shown great promise in the classification and diagnosis of bre...

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review.

Tomography (Ann Arbor, Mich.)
Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess ris...

A deep-learning-based clinical risk stratification for overall survival in adolescent and young adult women with breast cancer.

Journal of cancer research and clinical oncology
OBJECTIVE: The objective of this study is to construct a novel clinical risk stratification for overall survival (OS) prediction in adolescent and young adult (AYA) women with breast cancer.