AIMC Topic: Breast Neoplasms

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MZB1-Driven Endoplasmic reticulum stress model as a predictor of breast cancer progression and survival.

Functional & integrative genomics
Endoplasmic reticulum (ER) stress and its associated unfolded protein response (UPR) have been demonstrated to play a crucial role in cancer's progression, but their prognostic significance in breast cancer (BC) remains unclear. In this study, a reli...

Cox proportional hazards model with Bayesian neural network for survival prediction.

Scientific reports
Survival analysis plays a crucial aspect in medical research and other domains where understanding the time-to-events is paramount. In this study, we present a novel approach for estimating survival outcomes that combines Bayesian neural networks wit...

Chatbot-Based Version of a World Health Organization-Validated Intervention for Stress Management in Patients With Breast Cancer (Self-Help Plus): Protocol for a Pilot Feasibility Study.

JMIR research protocols
BACKGROUND: Emerging digital tools play an innovative and key role in supporting women's psychological well-being throughout the different stages and challenges of cancer. The development and adoption of digital interventions, including chatbots and ...

Harnessing artificial intelligence to identify Bufalin as a molecular glue degrader of estrogen receptor alpha.

Nature communications
Target identification in natural products plays a critical role in the development of innovative drugs. Bufalin, a compound derived from traditional medicines, has shown promising anti-cancer activity; however, its precise molecular mechanism of acti...

Deep Learning for the Early Detection of Invasive Ductal Carcinoma in Histopathological Images: Convolutional Neural Network Approach With Transfer Learning.

JMIR formative research
BACKGROUND: Invasive ductal carcinoma (IDC) is considered the most common form of breast cancer, accounting for a significant percentage of mortality worldwide. Therefore, its early detection is vital to further improve patients' outcomes and surviva...

Enhancing B-mode-based breast cancer diagnosis via cross-attention fusion of H-scan and Nakagami imaging with multi-CAM-QUS-Driven XAI.

Physics in medicine and biology
B-mode ultrasound is widely employed for breast lesion diagnosis due to its affordability, widespread availability, and effectiveness, particularly in cases of dense breast tissue where mammography may be less sensitive. However, it disregards critic...

External validation of precisebreast, a digital prognostic test for predicting breast cancer recurrence, in an early-stage cohort from the Netherlands.

Breast cancer research : BCR
BACKGROUND: Current clinical guidelines recommend gene expression profiling to guide treatment in early-stage breast cancer. PreciseBreast (PDxBR) is a digital prognostic tool that integrates artificial intelligence (AI)-derived features from hematox...

Machine learning model for early diagnosis of breast cancer based on PiRNA expression with CA153.

Scientific reports
PIWI-interacting RNAs (piRNAs) have been implicated in the biological processes of various cancers. This study aimed to investigate the diagnostic potential of circulating piRNAs in breast cancer (BC) using machine learning (ML) frameworks. A serum t...

Multimodal analysis of cell-free DNA enhances differentiation of early-stage breast cancer from benign lesions and healthy individuals.

BMC biology
BACKGROUND: Breast cancer (BC) remains the second leading cause of cancer-related mortality among women worldwide. Liquid biopsy based on circulating tumor DNA (ctDNA) offers a promising noninvasive approach for early detection; however, differentiat...

A machine learning approach to predict self-efficacy in breast cancer survivors.

BMC medical informatics and decision making
PURPOSE: To determine predictors of self-efficacy in breast cancer survivors and identify vulnerable groups.