Eur J Nucl Med Mol Imaging. 2026 May 16. doi: 10.1007/s00259-026-07918-y. Online ahead of print.
ABSTRACT
PURPOSE: To investigate the feasibility of non-invasively identifying bone marrow involvement (BMI) in follicular lymphoma (FL) using baseline 18F-FDG PET/CT combined with multidimensional feature fusion, and to compare the impact of different bone marrow volume-of-interest (VOI) frameworks on model performance.
METHODS: This retrospective study included 187 patients with newly diagnosed FL, 93 of whom had BMI. Based on baseline 18F-FDG PET/CT, two bone marrow VOI frameworks were constructed: a pelvic VOI framework and a spine-pelvis VOI framework. Clinical features, conventional imaging features, radiomic features, and deep learning features were extracted. A hierarchical feature screening strategy was employed: clinical and conventional imaging features were screened using univariate logistic regression, Spearman’s correlation analysis, and multivariate logistic regression, whereas high-dimensional radiomic and deep learning features were screened using LASSO regression combined with the Boruta algorithm. Based on the selected features, six different modelling schemes were developed. The optimal scheme was selected using the area under the receiver operating characteristic curve (AUC) in the independent validation set as the primary metric. Under the optimal scheme, the performance of seven machine learning models-logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), neural network (NN), random forest (RF), k-nearest neighbours (KNN), and adaptive boosting (AdaBoost)-was further compared. SHAP analysis was used to interpret the key features of the final model and the direction of their contributions.
RESULTS: Compared with the non-BMI group, the BMI group was more likely to present with widespread regional lymph node involvement, B symptoms, larger lymph node lesions, as well as lower Hb, higher LDH, lower Apo A, lower eGFR, and higher β2-MG levels (all P < 0.05). Under both VOI frameworks, the BMI group exhibited higher bone marrow FDG uptake intensity and metabolic burden, as reflected by higher values of conventional PET/CT features, including SUVmean, Standard Deviation (PET), RMS, 25th Percentile Value, Median, 75th Percentile Value, TLG, Glycolysis Q2-Q4, SAM, and SUVpeak (all P < 0.05). Multivariate logistic regression analysis indicated that regional lymph node involvement and β2-MG consistently remained independent predictors across both VOI frameworks, whereas SUVmean retained statistical significance only within the pelvic VOI framework. A comparison of six modelling schemes revealed that the scheme integrating the spine-pelvis VOI framework with clinical features, conventional imaging features, and radiomic features performed best. Under this scheme, the GBM model achieved the best overall performance on the independent validation set (AUC = 0.906, Accuracy = 0.877, Precision = 0.926, Sensitivity = 0.833, Specificity = 0.926, F1 score = 0.877). SHAP analysis revealed that, in addition to LNr (≥ 5) and β2-MG, first-order statistical features such as PET-Orig-FO-IQR, as well as texture features derived from wavelet/LBP transformations-including PET-Wav-HLL-NGTDM-Strength, PET-Wav-HLL-GLRLM-SRHGLE, CT-LBP3D-m1-GLCM-MCC, and PET-LBP3D-m2-GLSZM-SAHGLE-also made significant contributions. These findings suggest that BMI-associated imaging phenotypes are characterised not only by increased bone marrow metabolism but also by remodelling of the grey-level distribution and spatial heterogeneity within the bone marrow.
CONCLUSION: Bone marrow involvement in follicular lymphoma is associated with higher tumour burden and altered metabolic heterogeneity within the bone marrow. A PET/CT-based radiomic-clinical model showed good performance for non-invasive BMI prediction, and the spine-pelvis VOI framework outperformed the pelvic VOI framework alone. The final GBM model may provide a feasible imaging biomarker for complementary baseline assessment of BMI in FL.
PMID:42142271 | DOI:10.1007/s00259-026-07918-y
