AIMC Topic: Breast Neoplasms

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Strategies for integrating artificial intelligence into mammography screening programmes: a retrospective simulation analysis.

The Lancet. Digital health
BACKGROUND: Integrating artificial intelligence (AI) into mammography screening can support radiologists and improve programme metrics, yet the potential of different strategies for integrating the technology remains understudied. We compared program...

Deep learning-assisted distinguishing breast phyllodes tumours from fibroadenomas based on ultrasound images: a diagnostic study.

The British journal of radiology
OBJECTIVES: To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumours (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences.

A holistic physics-informed neural network solution for precise destruction of breast tumors using focused ultrasound on a realistic breast model.

Mathematical biosciences and engineering : MBE
This study presented a novel approach for the precise ablation of breast tumors using focused ultrasound (FUS), leveraging a physics-informed neural network (PINN) integrated with a realistic breast model. FUS has shown significant promise in treatin...

Breast cancer prognosis through the use of multi-modal classifiers: current state of the art and the way forward.

Briefings in functional genomics
We present a survey of the current state-of-the-art in breast cancer detection and prognosis. We analyze the evolution of Artificial Intelligence-based approaches from using just uni-modal information to multi-modality for detection and how such para...

Machine Learning-Driven Phenogrouping and Cardiorespiratory Fitness Response in Metastatic Breast Cancer.

JCO clinical cancer informatics
PURPOSE: The magnitude of cardiorespiratory fitness (CRF) impairment during anticancer treatment and CRF response to aerobic exercise training (AT) are highly variable. The aim of this ancillary analysis was to leverage machine learning approaches to...

Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs.

Bioinformatics (Oxford, England)
MOTIVATION: Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologie...

Improving Computer-aided Detection for Digital Breast Tomosynthesis by Incorporating Temporal Change.

Radiology. Artificial intelligence
Purpose To develop a deep learning algorithm that uses temporal information to improve the performance of a previously published framework of cancer lesion detection for digital breast tomosynthesis. Materials and Methods This retrospective study ana...

Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers.

The British journal of radiology
OBJECTIVES: To develop and validate machine learning models for human epidermal growth factor receptor 2 (HER2)-zero and HER2-low using MRI features pre-neoadjuvant therapy (NAT).