AIMC Topic: Breast Neoplasms

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Artificial intelligence-based computer-aided diagnosis for breast cancer detection on digital mammography in Hong Kong.

Hong Kong medical journal = Xianggang yi xue za zhi
INTRODUCTION: Research concerning artificial intelligence in breast cancer detection has primarily focused on population screening. However, Hong Kong lacks a population-based screening programme. This study aimed to evaluate the potential of artific...

Cubosomes as Delivery System to Repositioning Nitrofurantoin in Breast Cancer Management.

Drug design, development and therapy
PURPOSE: Nitrofurantoin (NITRO), a long-standing antibiotic to treat urinary tract infections, is activated by Nitro reductases. This activation mechanism has led to its exploration for repositioning applications in controlling and treating breast ca...

Segmentation for mammography classification utilizing deep convolutional neural network.

BMC medical imaging
BACKGROUND: Mammography for the diagnosis of early breast cancer (BC) relies heavily on the identification of breast masses. However, in the early stages, it might be challenging to ascertain whether a breast mass is benign or malignant. Consequently...

Attention-based Fusion Network for Breast Cancer Segmentation and Classification Using Multi-modal Ultrasound Images.

Ultrasound in medicine & biology
OBJECTIVE: Breast cancer is one of the most commonly occurring cancers in women. Thus, early detection and treatment of cancer lead to a better outcome for the patient. Ultrasound (US) imaging plays a crucial role in the early detection of breast can...

Freeze-Thaw-Induced Patterning of Extracellular Vesicles with Artificial Intelligence for Breast Cancers Identifications.

Small (Weinheim an der Bergstrasse, Germany)
Extracellular vesicles (EVs) play a crucial role in the occurrence and progression of cancer. The efficient isolation and analysis of EVs for early cancer diagnosis and prognosis have gained significant attention. In this study, for the first time, a...

Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model.

Frontiers in immunology
OBJECTIVE: To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast c...

Artificial intelligence improves mammography-based breast cancer risk prediction.

Trends in cancer
Artificial intelligence (AI) is enabling us to delve deeply into the information inherent in a mammogram and identify novel features associated with high risk of a future breast cancer diagnosis. Here, we discuss how AI is improving mammographic dens...

CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound.

Tomography (Ann Arbor, Mich.)
Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagn...

Novel models based on machine learning to predict the prognosis of metaplastic breast cancer.

Breast (Edinburgh, Scotland)
BACKGROUND: Metaplastic breast cancer (MBC) is a rare and highly aggressive histological subtype of breast cancer. There remains a significant lack of precise predictive models available for use in clinical practice.