AI-Quantified ¹¹C-MET PET/CT bone marrow metabolic activity for prognostic assessment in newly diagnosed multiple myeloma.
Authors
Affiliations (9)
Affiliations (9)
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Departments of Nuclear Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.
- Department of Hematology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China. [email protected].
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China. [email protected].
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China. [email protected].
- Pazhou Lab, Guangzhou, 510330, China. [email protected].
- Departments of Nuclear Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China. [email protected].
Abstract
To develop and validate an AI method for automated quantification of whole-skeleton bone marrow (BM) metabolic activity using Carbon 11 (<sup>11</sup>C)-methionine (MET) PET/CT and to evaluate its prognostic value compared with Fluorine 18 (<sup>18</sup>F)-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 <sup>11</sup>C-MET and <sup>18</sup>F-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 <sup>11</sup>C-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 <sup>11</sup>C-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, <sup>11</sup>C-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 <sup>11</sup>C-MET MTV and TLMU act as objective biomarkers of disease burden, they independently predict MM prognosis more effectively than <sup>18</sup>F-FDG parameters and may enhance risk stratification.