An MLOps Framework for Reproducible Radiomics Experiments

Translating machine learning (ML) models from research prototypes into clinical use requires development processes that are fully traceable and reproducible. Meeting these requirements is challenging for research laboratories, where radiomics experiments often rely on ad-hoc scripts and manual steps. Machine Learning Operations (MLOps) offers a set of practices and tools widely adopted in industry to scale ML workflows while ensuring end-to-end traceability and reproducibility. This study presents an MLOps-enabled experimentation framework assembled from open-source tools and specifically designed to support radiomics experiments. The framework integrates key MLOps practices, including data versioning, experiment tracking, workflow automation, and environment reproducibility. We evaluate it through a case study focused on automated frontotemporal dementia (FTD) detection from MRI brain scans. The framework removed the manual effort of orchestrating, tracking, and re-running experiments across classification tasks and feature-selection strategies, while supporting deterministic re-execution under fixed configurations and containerized environments. Although demonstrated on FTD detection, the framework is designed to support adaptation to other radiomics studies seeking reproducible and automated experimentation.