Bastien Jamet1, Ludivine Morvan2,3, Diana Mateus3, Thomas Carlier4,5, Cristina Nanni6, Anne-Victoire Michaud1, Clément Bailly1,2, Stéphane Chauvie7, Philippe Moreau8, Cyrille Touzeau8, Elena Zamagni9, Caroline Bodet-Milin1,2, Françoise Kraeber-Bodéré1,2,10. 1. Nuclear Medicine Department, University Hospital, 1 place Ricordeau, 44093, Nantes, France. 2. CRCINA, INSERM, CNRS, Université d'Angers, Université de Nantes, Nantes, France. 3. LS2N, CNRS UMR 6004, Ecole Centrale de Nantes, Nantes, France. 4. Nuclear Medicine Department, University Hospital, 1 place Ricordeau, 44093, Nantes, France. thomas.carlier@chu-nantes.fr. 5. CRCINA, INSERM, CNRS, Université d'Angers, Université de Nantes, Nantes, France. thomas.carlier@chu-nantes.fr. 6. Nuclear Medicine, AOU Policlinico S. Orsola-Malpighi, Bologna, Italy. 7. Medical Physics Division, Santa Croce e Carle Hospital, Cuneo, Italy. 8. Haematology Department, University Hospital, Nantes, France. 9. Seragnoli Institute of Hematology, Bologna University School of Medicine, Bologna, Italy. 10. Nuclear Medicine, ICO Cancer Center, Saint-Herblain, France.
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 transplant-eligible 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 and a mean log-rank p value of 0.01 ± 0.01. The additional RSF (nested cross-validation) analysis highlighted the robustness of the proposed model across different splits of the dataset. Indeed, the first features selected using the train/validation + test approach remained the first ones over the folds with the nested approach. CONCLUSION: We proposed a new prognosis model for TEND MM patients at diagnosis based on two RSF approaches. TRIAL REGISTRATION: IMAJEM: NCT01309334 and EMN02/HO95: NCT01134484.
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 transplant-eligible 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 and a mean log-rank p value of 0.01 ± 0.01. The additional RSF (nested cross-validation) analysis highlighted the robustness of the proposed model across different splits of the dataset. Indeed, the first features selected using the train/validation + test approach remained the first ones over the folds with the nested approach. CONCLUSION: We proposed a new prognosis model for TEND MM patients at diagnosis based on two RSF approaches. TRIAL REGISTRATION: IMAJEM: NCT01309334 and EMN02/HO95: NCT01134484.
Entities:
Keywords:
FDG-PET/CT; Multiple myeloma; Prognostic value; Radiomics; Random survival forest
Authors: Yang Li; Yang Liu; Ping Yin; Chuanxi Hao; Chao Sun; Lei Chen; Sicong Wang; Nan Hong Journal: Front Oncol Date: 2021-12-01 Impact factor: 6.244