Thoracolumbar MRI-Based Spinal Radiomics Model Predict Risk Stratification of Multiple Myeloma: A Pilot Machine Learning Study

Motivation: The diagnosis and staging of multiple myeloma is complicated so that universal healthcare usually cannot support in developing countries. The machine learning models based on MRI radiomics performs great application potential. Goal(s): Find effective radiomics prediction model based on conventional MRI sequences for MM early diagnosis and risk staging. Approach: Extract and select radiomics feathures from newly diagnosed MM patients' 胸腰椎 T1WI and T2-STIR. Choose the best machine learning model to predict the ISS and R-ISS stage. Results: The models based on T1WI perform better than the models based on T2-STIR. The RF model performs better than other machine learning models. Impact: This piolt study is the fundation to prove the dependency of thoracolumbar spine models with MM prognosis. We will combined different clinical data based on the chosen models for further multiple center research.