Leveraging Random Survival Forest (RSF) and PET images for prognosis of Multiple Myeloma at diagnosis
Résumé
Context Multiple myeloma (MM) is a bone marrow cancer that accounts for 10% of all haematological malignancies. It was reported that full-body FDG PET imaging provides prognostic information for both baseline and therapeutic follow-up of MM patients (MM). Aims Predict Progression-Free Survival (PFS). Provide predictive features (Clinics and Radiomics). Contribution There is yet much to discover in the survival analysis of MM. However, the Random Survival Forest (RSF)[2] has demonstrated robustness but is not studied in the PET imaging and MM context.We developed a two-stage computer-assisted method based on PET imaging features towards assisting current diagnosis and treatment decisions for MM patients, with RSF and "Variable importance" (VIMP) [2]. Definitions Right censoring: When no event (death/relapse) has taken place at the end of the evaluation period. C-index: The concordance probability is the frequency of concordant pairs among all pairs of subjects. Error prediction = 1-C-index Survival curve: Survival rates of a specific population, over a period of time.
Domaines
Machine Learning [stat.ML]| Origine | Fichiers produits par l'(les) auteur(s) |
|---|
Loading...