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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Breast cancer risk prediction using machine learning: a systematic review.

Sadam Hussain^1,2, Mansoor Ali¹, Usman Naseem³

¹School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico.

Frontiers in Oncology

|April 4, 2024

Summary

Deep learning (DL) enhances breast cancer risk prediction using imaging, radiomics, genomics, and clinical data. This AI approach offers personalized strategies for improved screening and management.

Keywords:

breast cancer clinical factors deep learning digital mammography risk prediction

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Breast cancer remains a leading cause of cancer mortality in women globally.
Traditional risk models rely on demographic and clinical history, limiting personalized prediction.
Artificial intelligence (AI), especially deep learning (DL), shows potential for personalized breast cancer risk assessment.

Approach:

This systematic review investigated DL applications in digital mammography, radiomics, genomics, and clinical data for breast cancer risk.
The study analyzed existing literature, critically evaluating findings and DL methods.
Natural language processing (NLP) was considered alongside DL for feature analysis.

Key Points:

Twenty studies were selected from 600 articles, focusing on DL for breast cancer risk prediction.
DL models leverage diverse data, including imaging, radiomics, genomics, and clinical information.
AI integration offers a novel perspective for risk assessment and model development.

Conclusions:

Deep learning techniques show significant promise for improving breast cancer risk prediction.
AI-driven approaches can facilitate more effective screening and personalized risk management.
This review provides a comprehensive guide to AI in breast cancer risk assessment.