Journal Article

A feasibility study of 18F FDG PET/CT radiomics in predicting high-risk cytogenetic abnormalities in multiple myeloma

Ding, Xuehai, Han, Jingxin, Chen, Hong, Chang, Chunkang, Ren, Xinqi, Luo, Quanyong, He, Qi, Huang, Haozhe, Yu, Fan

2025

Overview

Multiple myeloma (MM) is a heterogeneous malignancy with prognosis significantly affected by high-risk cytogenetic abnormalities (HRCAs). Traditional detection using fluorescence in situ hybridisation is invasive and limited in capturing disease heterogeneity. We aimed to develop and validate radiomics model based on pretreatment [18F] fluoro-deoxyglucose (FDG) positron emission tomography/computed tomographic (18F-FDG PET/CT) imaging to non-invasively predict HRCAs in newly diagnosed MM patients.BACKGROUNDMultiple myeloma (MM) is a heterogeneous malignancy with prognosis significantly affected by high-risk cytogenetic abnormalities (HRCAs). Traditional detection using fluorescence in situ hybridisation is invasive and limited in capturing disease heterogeneity. We aimed to develop and validate radiomics model based on pretreatment [18F] fluoro-deoxyglucose (FDG) positron emission tomography/computed tomographic (18F-FDG PET/CT) imaging to non-invasively predict HRCAs in newly diagnosed MM patients.Among the 42 candidate models, the Decision Tree classifier utilizing PET active lesions features demonstrated optimal performance in the validation cohort, exhibiting excellent predictive ability (Area Under the Curve (AUC) = 0.89), significantly outperforming the PET metrics model (AUC = 0.84) and clinical model (AUC = 0.74). SHapley Additive exPlanations analysis identified the PET-derived feature as the most important contributor to the model's predictive capacity. The model stratified patients into high-risk and low-risk groups, with the high-risk group exhibiting significantly worse PFS and OS (median PFS: high-risk 24.5 months vs. low-risk 29 months; p = 0.0360; median OS: high-risk 33.5 months vs. low-risk 50 months; p = 0.0023).RESULTSAmong the 42 candidate models, the Decision Tree classifier utilizing PET active lesions features demonstrated optimal performance in the validation cohort, exhibiting excellent predictive ability (Area Under the Curve (AUC) = 0.89), significantly outperforming the PET metrics model (AUC = 0.84) and clinical model (AUC = 0.74). SHapley Additive exPlanations analysis identified the PET-derived feature as the most important contributor to the model's predictive capacity. The model stratified patients into high-risk and low-risk groups, with the high-risk group exhibiting significantly worse PFS and OS (median PFS: high-risk 24.5 months vs. low-risk 29 months; p = 0.0360; median OS: high-risk 33.5 months vs. low-risk 50 months; p = 0.0023).As a non-invasive imaging biomarker, PET/CT radiomics holds potential for predicting high-risk cytogenetic status and facilitating patient prognosis stratification Further large-scale, multi-center prospective validations are essential to confirm its utility for personalized therapeutic decision-making in MM.CONCLUSIONAs a non-invasive imaging biomarker, PET/CT radiomics holds potential for predicting high-risk cytogenetic status and facilitating patient prognosis stratification Further large-scale, multi-center prospective validations are essential to confirm its utility for personalized therapeutic decision-making in MM.