Using Machine Learning to Predict Early Recurrence in Adults with Multiple Myeloma

What this Trial Is About

Researchers are studying multiple myeloma MM, a complex blood cancer, to improve how well doctors can predict which patients might experience early disease return after treatment. This research uses advanced machine learning methods to analyze clinical, genomic, and imaging information from MM patients, aiming to develop models that better forecast treatment response and risk of early relapse. The study is observational and conducted across multiple centers with a focus on improving personalized care for MM patients. The study collects and processes a wide range of data from patients with active multiple myeloma, including clinical details, tumor characteristics, and immune markers. Machine learning tools will combine this data to identify patient groups with similar disease features related to early progression. There are no experimental treatments given, as this is a non-interventional study that observes and analyzes existing patient data to enhance risk prediction models. Participants aged 18 years or older with a diagnosis of active multiple myeloma who agree to join the study will have their clinical and biological data collected and analyzed. The main outcome measured is the overall response rate to therapy within 12 months from the start of treatment. This study aims to generate new insights to support personalized treatment decisions and better management of MM. The study starts in October 2024 and will continue until August 2026, with no additional treatments or procedures beyond regular care and data collection.

A Machine Learning Approach to Connect Multiple Myeloma Complexity to Early Disease Recurrence