Feature-Centric AI for Breast Cancer Metastasis Analytics: A Systematic Review and Evidence-Based Framework for Feature Engineering and Multimodal Integration

Abstract

The importance of understanding how feature engineering contributes to breast cancer metastasis analytics is growing as artificial intelligence (AI) technologies rapidly transform the field of precision oncology. This systematic review collated, thematically synthesised, and analysed the evidence related to feature engineering processes, multimodal integration techniques and explainability mechanisms used in AI-driven breast cancer metastasis analytics. Following PRISMA 2020, studies from 2020 to 2026 were retrieved from MEDLINE, Scopus, Web of Science, Embase and IEEE Xplore. A total of 50 empirical studies investigating AI, feature engineering, radiomics and multimodal approaches for breast cancer metastasis prediction were included. The research findings reveal that contemporary AI-based breast cancer metastasis prediction is predominantly based on imaging-derived features, while clinical, pathological, biomarker and molecular variables increasingly enhance multimodal model development. Frequently applied techniques were observed across feature extraction, preprocessing, reproducibility assessment, dimensionality reduction, feature integration and explainability, all of which helped to create more interpretable, robust and clinically meaningful predictive systems. The study crafts an evidence-informed conceptual 5Fs framework comprising feature extraction or generation, feature preparation and transformation, feature selection and dimensionality reduction, feature construction and integration, and feature interpretability and explainability. The framework highlights the feature-centric nature of AI-driven breast cancer metastasis analytics and emphasises the importance of interpretable, multimodal and potentially clinically translatable predictive systems.