MRI-Based Bone Marrow Radiomics Nomogram for Prediction of Overall Survival in Patients With Multiple Myeloma

Yang Li; Yang Liu; Ping Yin; Chuanxi Hao; Chao Sun; Lei Chen; Sicong Wang; Nan Hong

doi:10.3389/fonc.2021.709813

MRI-Based Bone Marrow Radiomics Nomogram for Prediction of Overall Survival in Patients With Multiple Myeloma

Front Oncol. 2021 Dec 1:11:709813. doi: 10.3389/fonc.2021.709813. eCollection 2021.

Authors

Yang Li¹, Yang Liu^{2

3

4}, Ping Yin¹, Chuanxi Hao¹, Chao Sun¹, Lei Chen¹, Sicong Wang⁵, Nan Hong¹

Affiliations

¹ Department of Radiology, Peking University People's Hospital, Beijing, China.
² Peking University Institute of Hematology, Peking University People's Hospital, Beijing, China.
³ Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Peking University, Beijing, China.
⁴ Collaborative Innovation Center of Hematology, Peking University, Beijing, China.
⁵ Pharmaceutical Diagnostics, GE Healthcare, Shanghai, China.

Abstract

Purpose: To develop and validate a radiomics nomogram for predicting overall survival (OS) in multiple myeloma (MM) patients.

Material and methods: A total of 121 MM patients was enrolled and divided into training (n=84) and validation (n=37) sets. The radiomics signature was established by the selected radiomics features from lumbar MRI. The radiomics signature and clinical risk factors were integrated in multivariate Cox regression model for constructing radiomics nomogram to predict MM OS. The predictive ability and accuracy of the nomogram were evaluated by the index of concordance (C-index) and calibration curves, and compared with other four models including the clinical model, radiomics signature model, the Durie-Salmon staging system (D-S) and the International Staging System (ISS). The potential association between the radiomics signature and progression-free survival (PFS) was also explored.

Results: The radiomics signature, 1q21 gain, del (17p), and β2-MG≥5.5 mg/L showed significant association with MM OS. The predictive ability of radiomics nomogram was better than the clinical model, radiomics signature model, the D-S and the ISS (C-index: 0.793 vs. 0.733 vs. 0.742 vs. 0.554 vs. 0.671 in training set, and 0.812 vs. 0.799 vs.0.717 vs. 0.512 vs. 0.761 in validation set). The radiomics signature lacked the predictive ability for PFS (log-rank P=0.001 in training set and log-rank P=0.103 in validation set), whereas the 1-, 2- and 3-year PFS rates all showed significant difference between the high and low risk groups (P ≤ 0.05).

Conclusion: The MRI-based bone marrow radiomics may be an additional useful tool for MM OS prediction.

Keywords: magnetic resonance imaging; multiple myeloma; nomogram; radiomics; survival.