Random survival forest to predict transplant-eligible newly diagnosed multiple myeloma outcome including FDG-PET radiomi
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ORIGINAL ARTICLE
Random survival forest to predict transplant-eligible newly diagnosed multiple myeloma outcome including FDG-PET radiomics: a combined analysis of two independent prospective European trials Bastien Jamet 1 & Ludivine Morvan 2,3 & Cristina Nanni 4 & Anne-Victoire Michaud 1 & Clément Bailly 1,2 & Stéphane Chauvie 5 & Philippe Moreau 6 & Cyrille Touzeau 6 & Elena Zamagni 7 & Caroline Bodet-Milin 1,2 & Françoise Kraeber-Bodéré 1,2,8 & Diana Mateus 3 & Thomas Carlier 1,2 Received: 6 July 2020 / Accepted: 20 September 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Purpose Fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) is included in the International Myeloma Working Group (IMWG) imaging guidelines for the work-up at diagnosis and the follow-up of multiple myeloma (MM) notably because it is a reliable tool as a predictor of prognosis. Nevertheless, none of the published studies focusing on the prognostic value of PET-derived features at baseline consider tumor heterogeneity, which could be of high importance in MM. The aim of this study was to evaluate the prognostic value of baseline PET-derived features in transplanteligible newly diagnosed (TEND) MM patients enrolled in two prospective independent European randomized phase III trials using an innovative statistical random survival forest (RSF) approach. Methods Imaging ancillary studies of IFM/DFCI2009 and EMN02/HO95 trials formed part of the present analysis (IMAJEM and EMN02/HO95, respectively). Among all patients initially enrolled in these studies, those with a positive baseline FDG-PET/ CT imaging and focal bone lesions (FLs) and/or extramedullary disease (EMD) were included in the present analysis. A total of 17 image features (visual and quantitative, reflecting whole imaging characteristics) and 5 clinical/histopathological parameters were collected. The statistical analysis was conducted using two RSF approaches (train/validation + test and additional nested cross-validation) to predict progression-free survival (PFS). Results One hundred thirty-nine patients were considered for this study. The final model based on the first RSF (train/validation + test) approach selected 3 features (treatment arm, hemoglobin, and SUVmaxBone Marrow (BM)) among the 22 involved initially, and two risk groups of patients (good and poor prognosis) could be defined with a mean hazard ratio of 4.3 ± 1.5
Bastien Jamet, Ludivine Morvan, Diana Mateus and Thomas Carlier contributed equally to this work. This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence) Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00259-020-05049-6) contains supplementary material, which is available to authorized users. * Thomas Carlier [email protected] Bastien Jamet [email protected] 1
Nuclear Medicine Department, University Hospital, 1 place Ricordeau, 44093 Nantes, France
2
CRCINA, INSERM, CNRS, Uni
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