Purpose 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. Methods 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. Results 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. Conclusion 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.

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

期刊论文
发表日期：

2025年10月30日

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Zanting Ye,
Jieqin Lv,
Xuanbin Wu,
Hui Li,
Xiaolong Niu,
Xu Han,
Baoyuan Li,
Lu Gao,
Jiaqi Yang,
Fu Ouyang,
Lijun Lu,
Pengcheng Ran

摘要

Purpose

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.

Methods

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.

Results

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.

Conclusion

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.