AI-Quantified ¹¹C-MET PET/CT bone marrow metabolic activity for prognostic assessment in newly diagnosed multiple myeloma.

Abstract

To develop and validate an AI method for automated quantification of whole-skeleton bone marrow (BM) metabolic activity using Carbon 11 (¹¹C)-methionine (MET) PET/CT and to evaluate its prognostic value compared with Fluorine 18 (¹⁸F)-fluorodeoxyglucose (FDG) PET/CT in patients with newly diagnosed MM. This prospective study included 49 patients (median age, 68 years; 29 males) with newly diagnosed MM. All patients underwent both ¹¹C-MET and ¹⁸F-FDG PET/CT. An AI algorithm initially segments the skeleton on CT images, then propagates the resulting mask to the standardized uptake value (SUV) PET images for automated PET/CT segmentation and quantitative volumetric assessment of BM metabolism. By applying a series of SUV thresholds, the algorithm calculates ¹¹C-MET metabolic tumor volume (MTV) and total lesion methionine uptake (TLMU). Associations with clinical markers (bone marrow plasma cell [BMPC] percentage, serum β₂-microglobulin, International Staging System [ISS]/Revised ISS [R-ISS] stage) and progression-free survival (PFS) were assessed. AI-quantified ¹¹C-MET MTV and TLMU showed significant correlations with BMPC percentage (MTV: r = 0.32, p = 0.02; TLMU: r = 0.31, p = 0.03), serum β₂-microglobulin (MTV: r = 0.29, p = 0.05; TLMU: r = 0.29, p = 0.05), ISS stage (MTV: r = 0.31, p = 0.03; TLMU: r = 0.32, p = 0.03), and R-ISS stage (MTV: r = 0.40, p = 0.02; TLMU: r = 0.37, p = 0.03). In multivariable Cox analysis, both ¹¹C-MET MTV (HR = 1.0023; [95% CI: 1.0004-1.0042]; p = 0.02) and TLMU (HR = 1.0003; [95% CI: 1.0001-1.0005]; p = 0.01) independently predicted PFS. For PFS prediction, ¹¹C-MET MTV (Area Under the Receiver Operating Characteristic Curve [AUC] = 0.743; [95% CI: 0.563-0.903]; p < 0.01) and TLMU (AUC = 0.749; [95% CI: 0.576-0.904]; p < 0.01) outperformed ¹⁸F-FDG PET/CT total lesion glycolysis (TLG) (AUC = 0.713, p < 0.01) and MTV (AUC = 0.719, p < 0.01) using the proposed thresholds. AI-quantified ¹¹C-MET MTV and TLMU act as objective biomarkers of disease burden, they independently predict MM prognosis more effectively than ¹⁸F-FDG parameters and may enhance risk stratification.