AIMC Topic: Ultrasonography, Mammary

Clear Filters Showing 1 to 10 of 227 articles

Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study.

Scientific reports
The purpose of this study was to create and validate an ultrasound-based graph convolutional network (US-based GCN) model for the prediction of axillary lymph node metastasis (ALNM) in patients with breast cancer. A total of 820 eligible patients wit...

Enhanced HER-2 prediction in breast cancer through synergistic integration of deep learning, ultrasound radiomics, and clinical data.

Scientific reports
This study integrates ultrasound Radiomics with clinical data to enhance the diagnostic accuracy of HER-2 expression status in breast cancer, aiming to provide more reliable treatment strategies for this aggressive disease. We included ultrasound ima...

New hybrid features extracted from US images for breast cancer classification.

Scientific reports
Artificial intelligence (AI), and image processing fields play a vital role in classifying benign and malignant breast cancer (BC). The novelty of this paper lies in computing original hybrid features (HF) from textural and shape features of BC integ...

BIScreener: enhancing breast cancer ultrasound diagnosis through integrated deep learning with interpretability.

Analytical methods : advancing methods and applications
Breast cancer is the leading cause of death among women worldwide, and early detection through the standardized BI-RADS framework helps physicians assess the risk of malignancy and guide appropriate diagnostic and treatment decisions. In this study, ...

Integrative multimodal ultrasound and radiomics for early prediction of neoadjuvant therapy response in breast cancer: a clinical study.

BMC cancer
PURPOSE: This study aimed to develop an early predictive model for neoadjuvant therapy (NAT) response in breast cancer by integrating multimodal ultrasound (conventional B-mode, shear-wave elastography, and contrast-enhanced ultrasound) and radiomics...

Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography.

BMC medical imaging
OBJECTIVE: To develop a multi-modality machine learning-based radiomics model utilizing Magnetic Resonance Imaging (MRI), Ultrasound (US), and Mammography (MMG) for the differentiation of benign and malignant breast nodules.

Whole-lesion-aware network based on freehand ultrasound video for breast cancer assessment: a prospective multicenter study.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: The clinical application of artificial intelligence (AI) models based on breast ultrasound static images has been hindered in real-world workflows due to operator-dependence of standardized image acquisition and incomplete view of breast ...

Application Value of Deep Learning-Based AI Model in the Classification of Breast Nodules.

British journal of hospital medicine (London, England : 2005)
Breast nodules are highly prevalent among women, and ultrasound is a widely used screening tool. However, single ultrasound examinations often result in high false-positive rates, leading to unnecessary biopsies. Artificial intelligence (AI) has dem...

The value of intratumoral and peritumoral ultrasound radiomics model constructed using multiple machine learning algorithms for non-mass breast cancer.

Scientific reports
To investigate the diagnostic capability of multiple machine learning algorithms combined with intratumoral and peritumoral ultrasound radiomics models for non-massive breast cancer in dense breast backgrounds. Manual segmentation of ultrasound image...

Intelligent and precise auxiliary diagnosis of breast tumors using deep learning and radiomics.

PloS one
BACKGROUND: Breast cancer is the most common malignant tumor among women worldwide, and early diagnosis is crucial for reducing mortality rates. Traditional diagnostic methods have significant limitations in terms of accuracy and consistency. Imaging...