Integrative Analysis of Medical Images and Genomic Profiles using Machine Learning for Enhanced Diagnostic Accuracy

Metastasis remains the leading cause of mortality in breast cancer patients, making early identification of metastatic potential critical for improving prognosis and enabling timely, personalized treatment. This study proposes an advanced machine learning (ML) framework for the early prediction of breast cancer metastasis by integrating multimodal data, including clinical parameters, histopathological profiles, and imaging features. A curated dataset comprising patient records, tissue characteristics, and diagnostic images was preprocessed and analyzed using a range of ML algorithms, including ensemble methods, deep neural networks, and support vector machines. Feature selection and dimensionality reduction techniques were applied to enhance model performance and interpretability. The resulting models demonstrated high accuracy, sensitivity, and specificity in predicting early-stage metastasis. These findings highlight the utility of ML-based decision support systems in augmenting traditional diagnostics, reducing time-to-diagnosis, and ultimately improving clinical outcomes in breast cancer care [3].