AIMC Topic: Receptor, ErbB-2

Clear Filters Showing 11 to 20 of 99 articles

From text to insight: A natural language processing-based analysis of burst and research trends in HER2-low breast cancer patients.

Ageing research reviews
With the intensification of population aging, the proportion of elderly breast cancer patients is continuously increasing, among which those with low HER2 expression account for approximately 45 %-55 % of all cases within traditional HER2-negative br...

Monitoring Immunohistochemical Staining Variations Using Artificial Intelligence on Standardized Controls.

Laboratory investigation; a journal of technical methods and pathology
Quality control of immunohistochemistry (IHC) slides is crucial to ascertain accurate patient management. Visual assessment is the most commonly used method to assess the quality of IHC slides from patient samples in daily pathology routines. Control...

An assessment of breast cancer HER2, ER, and PR expressions based on mammography using deep learning with convolutional neural networks.

Scientific reports
Mammography is the recommended imaging modality for breast cancer screening. Expressions of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) are critical to the development of therapeutic strateg...

Next-generation sequencing based deep learning model for prediction of HER2 status and response to HER2-targeted neoadjuvant chemotherapy.

Journal of cancer research and clinical oncology
INTRODUCTION: For patients with breast cancer, the amplification of Human Epidermal Growth Factor 2 (HER2) is closely related to their prognosis and treatment decisions. This study aimed to further improve the accuracy and efficiency of HER2 amplific...

Weakly supervised multi-modal contrastive learning framework for predicting the HER2 scores in breast cancer.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Human epidermal growth factor receptor 2 (HER2) is an important biomarker for prognosis and prediction of treatment response in breast cancer (BC). HER2 scoring is typically evaluated by pathologist microscopic observation on immunohistochemistry (IH...

Non-invasive Assessment of Human Epidermal Growth Factor Receptor 2 Expression in Gastric Cancer Based on Deep Learning: A Computed Tomography-based Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: The expression of human epidermal growth factor receptor 2 (HER2) in gastric cancer is closely associated with its treatment outcomes and prognosis. This study aims to develop and validate a HER2 prediction model based on co...

Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features.

Breast cancer research and treatment
PURPOSE: Identification of the molecular subtypes in breast cancer allows to optimize treatment strategies, but usually requires invasive needle biopsy. Recently, non-invasive imaging has emerged as promising means to classify them. Magnetic resonanc...

Multimodal integration using a machine learning approach facilitates risk stratification in HR+/HER2- breast cancer.

Cell reports. Medicine
Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common type of breast cancer, with continuous recurrence remaining an important clinical issue. Current relapse predictive models in H...

A Machine Learning Model for Predicting the HER2 Positive Expression of Breast Cancer Based on Clinicopathological and Imaging Features.

Academic radiology
RATIONALE AND OBJECTIVES: To develop a machine learning (ML) model based on clinicopathological and imaging features to predict the Human Epidermal Growth Factor Receptor 2 (HER2) positive expression (HER2-p) of breast cancer (BC), and to compare its...