Purpose: Multiple myeloma (MM) is a bone marrow cancer that accounts for 10% of all hematological malignancies. It has been reported that FDG PET imaging provides prognostic information for both baseline and therapeutic follow-up of MM patients using visual analysis. In this study, we aim to develop a computer-assisted method based on PET quantitative image features to assist diagnoses and treatment decisions for MM patients.
Methods: Our proposed model relies on a two-stage method with Random Survival Forest (RFS) and variable importance (VIMP) for both feature selection and prediction. The targeted variable for prediction is the progression-free survival (PFS). We consider texture-based (radiomics), conventional (e.g., SUVmax) and clinical biomarkers. We evaluate PFS predictions in terms of C-index and final prognosis separation in two risk groups, from a database of 66 patients who were part of the prospective multi-centric french IMAJEM study.
Results: Our method (VIMP + RSF) provides better results (1-C-index of 0.36) than conventional methods such as Lasso-Cox and gradient-boosting Cox (0.48 and 0.56, respectively). We experimentally proved the interest of using selection (0.61 for RSF without selection) and showed that VIMP selection is more stable and gives better results than minimal depth and variable hunting (0.47 and 0.43). The approach gives better prognosis group separation (a p value of 0.05 against 0.11 to 0.4 for others).
Conclusion: Our results confirm the predictive value of radiomics for MM patients, in particular, they demonstrate that quantitative/heterogeneity image-based features reduce the error of the predicted progression. To our knowledge, this is the first work using RFS on PET images for the progression prediction of MM patients. Moreover, we provide an analysis of the feature selection process, which points toward the identification of clinically relevant biomarkers.
Keywords: Multiple myeloma; PET imaging; Radiomics; Random survival forest; Variable selection.