Revisiting multi-nodal radiomics with advanced feature learning for lymphoma classification: a multi-center study.

Objective.Develop and evaluate a patient-level framework for discriminating Hodgkin lymphoma (HL) from non-HL (NHL) on baseline18F-FDG PET by aggregating radiomic descriptors from a variable number of involved lymph nodes in a multi-center setting.Approach.Each patient was modeled as an unordered set of lesion-level PET radiomic feature vectors. PET images were converted to standardized uptake values (SUV), resampled to a uniform voxel spacing of2×2×2mm3, and radiomic features were extracted using PyRadiomics from the original SUV images with a fixed bin width of 0.25 SUV units, following definitions consistent with the Image Biomarker Standardisation Initiative. A total of 107 first-order, texture, and shape features were extracted per lesion and optionally augmented with demographic variables and normalized node center-of-mass coordinates after rigid registration to a common reference space. We compared statistical pooled tabular baselines with learnable set-based aggregation models, including Transformer, Deep Sets, multiple instance learning, Set Transformer, and a graph-based model. Models were trained using five-fold cross-validation on Center 1 (151 patients; 85 HL, 66 NHL) and evaluated on an independent external Center 2 cohort (80 patients; 36 HL, 44 NHL) under direct transfer and unsupervised test-time adaptation (TTA).Main results.Combining radiomics with demographic and spatial features consistently improved discrimination. On Center 1, the best-performing deep aggregation models achieved ROC-AUC (mean±standard deviation) up to0.91±0.05and accuracy up to0.84±0.07. External evaluation on Center 2 revealed a measurable domain shift, with reduced performance under direct transfer. Unsupervised TTA, particularly TENT, improved cross-center generalization for deep aggregation models; for example, the Transformer improved from ROC-AUC0.81±0.05andF1-score0.60±0.23to ROC-AUC0.87±0.01andF1-score0.75±0.01. Feature attribution analysis indicated that both demographic and radiomic descriptors contributed to the predictions.Significance.These results show that multi-nodal PET radiomics combined with demographic and spatial dissemination cues is a promising strategy for patient-level HL versus NHL prediction. The findings also highlight the importance of robustness considerations in multi-center PET radiomics and suggest that TTA may help mitigate cross-center variability.