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

Abstract

PurposeTo develop and validate a radiomics nomogram for predicting overall survival (OS) in multiple myeloma (MM) patients.Material and MethodsA 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.ResultsThe 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).ConclusionThe MRI-based bone marrow radiomics may be an additional useful tool for MM OS prediction.

In Vivo Repeatability and Multiscanner Reproducibility of MRI Radiomics Features in Patients With Monoclonal Plasma Cell Disorders

“…13 Few studies have applied radiomics to analyze MRIs from multiple myeloma patients so far. [39][40][41][42] Knowledge on robust RFs in multiple myeloma could be of use to reduce the RF set before modeling, especially given the limited cohort sizes of these studies, and help to improve the robustness of radiomics models for multicentric application.…”

Application of clinical nomograms to predicting overall survival and event-free survival in multiple myeloma patients: Visualization tools for prognostic stratification

“…Recently (31). Nevertheless, most of the time, the definitive etiology of PE could not be identified due to the low thoracentesis rate.…”

“…Hou et al adopted chest computed tomography (CT) scans to detect pleural effusion (PE) and constructed a PE-based nomogram to predict survival outcomes for unselected MM patients ( 30 ). Li et al developed a magnetic resonance imaging (MRI)-based radiomics nomogram to predict OS in MM patients ( 31 ). Nevertheless, most of the time, the definitive etiology of PE could not be identified due to the low thoracentesis rate.…”

Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review

“…Out of the nine articles and four abstracts included, 6/14 (43%) used CT images [23][24][25][26][27][28], 3/14 (21%) used PET-CT scans [29][30][31], and 5/14 (36%) used MRI as their imaging modality [17,[32][33][34]. A total of 5/14 (36%) studies used WB imaging or imaging from affected body parts, 1/14 (%) focused on the femur bone, 1/14 (7%) focused on the spine and pelvic bones, and 3/14 (%) focused on the spine alone.…”

“…The role of artificial intelligence (AI) in oncological imaging has been growing over the last decade and has been studied in other disease states [13][14][15]. For example, radiomics is a rapidly emerging research field, with several studies suggesting its potential to assist with the accuracy of disease diagnosis as well as the estimation of survival [14,[16][17][18]. Previous systematic reviews of various oncological diseases have also provided insight into the progression of AI by highlighting challenges with validity [13,19].…”