Abstract

Breast cancer’s minor residual lesions post-treatment have posed challenges in accurate diagnosis and prognosis. This article explores recent advancements in the field that have revolutionized the assessment of these lesions. Multipara metric imaging techniques, liquid biopsies, artificial intelligence (AI), genomic profiling, and predictive models are reshaping the landscape of post-treatment management. Multipara metric imaging combines functional and morphological data for precise lesion characterization. Liquid biopsies offer non-invasive monitoring of treatment response and detection of residual cancer cells. AI-driven algorithms analyze imaging and clinical data, aiding in diagnosis and outcome prediction. Genomic profiling identifies genetic alterations influencing residual cancer cell behavior. Predictive models integrate data for recurrence likelihood estimation. While challenges persist, such as standardization and ethical considerations, these innovations hold great promise for personalized medicine and improved patient outcomes