AIMC Topic: Breast Neoplasms

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DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data.

Genome medicine
BACKGROUND: Identifying breast cancer patients with DNA repair pathway-related germline pathogenic variants (GPVs) is important for effectively employing systemic treatment strategies and risk-reducing interventions. However, current criteria and ris...

Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.

Breast cancer research : BCR
BACKGROUND: Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines...

Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset.

BMC research notes
OBJECTIVE: Breast cancer is a critical public health issue and a leading cause of cancer-related deaths among women worldwide. Its early diagnosis and detection can effectively help in increasing the chances of survival rate. For this reason, the dia...

An Optimized Framework for Breast Cancer Classification Using Machine Learning.

BioMed research international
Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies have shown that a larger number of ultrasound images are generated every day, and the number of radiologists able to analyze this medical data is very limit...

The importance of multi-modal imaging and clinical information for humans and AI-based algorithms to classify breast masses (INSPiRED 003): an international, multicenter analysis.

European radiology
OBJECTIVES: AI-based algorithms for medical image analysis showed comparable performance to human image readers. However, in practice, diagnoses are made using multiple imaging modalities alongside other data sources. We determined the importance of ...

Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Background parenchymal enhancement (BPE) is assessed on breast MRI reports as mandated by the Breast Imaging Reporting and Data System (BI-RADS) but is prone to inter and intrareader variation. Semiautomated and fully automated BPE assess...

Deep Learning Capabilities for the Categorization of Microcalcification.

International journal of environmental research and public health
Breast cancer is the most common cancer in women worldwide. It is the most frequently diagnosed cancer among women in 140 countries out of 184 reporting countries. Lesions of breast cancer are abnormal areas in the breast tissues. Various types of br...

A non-invasive method for concurrent detection of early-stage women-specific cancers.

Scientific reports
We integrated untargeted serum metabolomics using high-resolution mass spectrometry with data analysis using machine learning algorithms to accurately detect early stages of the women specific cancers of breast, endometrium, cervix, and ovary across ...

Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy.

Scientific reports
In this study, a novel deep learning-based methodology was investigated to predict breast cancer response to neo-adjuvant chemotherapy (NAC) using the quantitative ultrasound (QUS) multi-parametric imaging at pre-treatment. QUS multi-parametric image...

A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images.

Ultrasonic imaging
Breast cancer is one of the most fatal diseases leading to the death of several women across the world. But early diagnosis of breast cancer can help to reduce the mortality rate. So an efficient multi-task learning approach is proposed in this work ...