Improving Breast Cancer Diagnosis and Precision Treatment with Multi-Omics Integration and Artificial Intelligence

Abstract

The most common malignancy in women is breast cancer (BC), which frequently poses a number of several challenges in molecular subtypes identification and early diagnosis. Additionally, approximately one-third of cases are detected at an advanced stage, owing to the fact that early-stage cancer is frequently asymptomatic, and there are presently no efficient early detection tools or particular biomarkers. This directly causes patients to miss the optimal treatment window. Unfortunately, breast cancer is highly heterogeneous and characterized by substantial heterogeneity and its various molecular typing have a direct impact on the effectiveness of treatments such as chemotherapy and immunotherapy as well as having a strong correlation with patients' rates of five-year survival rates. The aim of this review is to show how these skills help to identify novel medications and build individualized therapeutics. By creating early diagnostic models utilizing multi-omics data, identifying molecular subtypes of breast cancer, measuring the tumor immune microenvironment, and forecasting immunotherapy responses, it illustrates the successful use of AI technology in breast cancer diagnosis. These breakthroughs rely heavily on the discovery of new medications and the creation of specialized treatment regimens. The study also looks into the development of novel therapeutic agents and the mechanisms of drug resistance in breast cancer. This review examines recent developments in multimodal AI, which combines many imaging modalities such digital pathology, digital breast tomography (DBT), MRI, ultrasound, mammography, and multi-omics. Accurately identifying cancer subtypes, evaluating the tumor immune milieu, predicting immunotherapy responses, and assessing possible treatment resistance are all made possible by this integration. Artificial intelligence (AI) has progressed rapidly from experimental research to therapeutic applications. It can now achieve diagnosis accuracy equivalent to that of radiologists, improve specificity, and reduce workloads by 44% to 68%, all while maintaining cancer detection rates. Some research indicates that artificial intelligence might help radiologists identify more cancer patients. These advantages result in more effective screening procedures, less needless recalls, and quicker diagnosis. In the future, artificial intelligence technology is likely to give more accurate and individualized diagnostic and therapy alternatives for patients with BC. However, analyzing multi-omics data in cancer involves a number of problems, including complexity, sparsity, transparency, and ethical concerns.