Abstract
Breast cancer is one of the most prevalent cancers affecting women globally. Early diagnosis is crucial for effective treatment and improved survival rates. Imaging techniques such as mammography and ultrasound are widely used conventional diagnostic methods. However, these methods have limitations, including low sensitivity and specificity, especially in patients with dense breast tissue. For instance, mammograms miss approximately 20% of breast cancer cases, leading to false negatives and delayed treatment that can have fatal consequences. To address these challenges, artificial intelligence (AI)-based diagnostic tools have been developed to assist healthcare professionals in accurately detecting breast cancer. These tools work in conjunction with human radiologists to improve diagnostic outcomes. In addition, biomarkers present a promising non-invasive, more convenient alternative for the early detection of breast cancer, potentially overcoming the limitations of traditional screening methods. Various biomarkers, such as circulating tumor cells, cell-free tumor nucleic acids, and microRNAs, have shown promise in early breast cancer diagnosis. A systematic literature review is needed to consolidate ongoing efforts in molecular biology and biomedical sciences aimed at achieving early breast cancer diagnosis. One of the limitations of previously published research is the heterogeneity of methodologies, which can compromise the credibility of comparisons due to potential inaccuracies in the original data. Hence, future studies should prioritize using consistent datasets and developing robust techniques to manage missing values, outliers, and class imbalances to improve the reliability of breast cancer detection models. This literature review seeks to bridge the knowledge gap by reporting recent high-performing AI models and effective biomarkers that can serve as diagnostic tools in clinical practice.
Published Version
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