AI-Driven Detection of Breast Cancer using Machine Learning and Mathematical Modeling of Tumor Growth

Abstract

Breast cancer remains one of the most prevalent and life-threatening malignancies among women worldwide, demanding timely detection and accurate prediction of tumor progression. Recent advances in artificial intelligence (AI) and machine learning (ML) have revolutionized breast cancer imaging by enabling automated detection, risk stratification, and prognostic forecasting from mammography, ultrasound, and MRI data. Deep learning approaches have shown superior performance in identifying malignant lesions and stratifying patients compared to traditional radiological assessment, while radiomics and hybrid ML models have enhanced staging and treatment planning. Parallel to these developments, mathematical modeling of tumor growth, particularly using Gompertzian, logistic, and Bayesian population-based frameworks, provides mechanistic insights into tumor kinetics and predictive estimates of tumor age, size, and treatment response. Integrating AI-driven image analysis with tumor growth modeling offers a powerful interdisciplinary approach for early diagnosis, personalized treatment, and clinical decision support. This paper presents a comprehensive study that bridges these two paradigms, exploring case studies of AI-based detection systems alongside mathematical tumor-growth predictions. The novelty lies in demonstrating how synergistic use of computational intelligence and mechanistic modeling can improve diagnostic accuracy, reduce clinical workload, and enable personalized care pathways.