A Lesion-adaptive Segmentation Approach for Tumor Delineation on FDG PET/CT in Diffuse Large B-cell Lymphoma Patients

SUV4.0-based thresholding is widely used for baseline [¹⁸F]FDG PET-based metabolic tumor volume (MTV) assessment in diffuse large B-cell lymphoma (DLBCL), but its suitability at interim and end-of-treatment (EoT) PET, when residual uptake is heterogeneous and tumor-to-background contrast is lower, is uncertain. We aimed to define a lesion-adaptive decision rule approach for selecting the optimal segmentation method based on lesion-level features and treatment phase and, exploratorily, to compare its performance with ML-based selection models.

A total of 598 lesions from 33 DLBCL patients (HOVON-84 trial) were segmented at baseline, interim, and EoT [¹⁸F]FDG PET/CT using six semi-automated methods: SUV2.5, SUV4.0, 41%max, A50peak, MV2, and MV3. Segmentation quality was independently rated for each lesion by two observers (scale 1-5; 3 = preferred), with adjudication by a third reviewer. The influence of lesional SUVpeak, tumor-to-background ratio (TBRpeak), background uptake (SUVbg), treatment phase, and location on segmentation quality was assessed. Over six million rule-based combinations of key features were evaluated to derive a lesion-adaptive decision rule for preferred method selection. Exploratorily, ML classifiers were trained and compared with the decision-rule strategy.

Segmentation quality varied across lesions and methods. SUVpeak, TBRpeak, and SUVbg were key predictors of method performance. The final lesion-adaptive rule, applying SUV4.0 if SUVpeak > 8, MV3 if SUVbg > 0.8, and otherwise MV2, achieved a lesion-wise accuracy of 0.82 for preferred method selection, matching the best-performing ML models. Versus SUV4.0 alone (benchmark), the Decision Rule improved lesion-level MTV agreement with the reference (ρ = 0.85 vs. 0.82 vs. best ML ρ = 0.81) and reduced the proportion of lesions with > 10% MTV deviation (46.2% vs. 63.5%; best ML 50.2%). Total-MTV agreements with the reference were uniformly high across all strategies (all ρ ≥ 0.94), with modest gains for the decision rule at interim and EoT PET.

A straightforward decision-rule approach using SUVpeak and SUVbg successfully selects the preferred method for individual DLBCL lesions across treatment phases and matches ML performance with greater simplicity and clinical applicability. Although supervision remains necessary, this approach helps address the current gap in segmentation methodology for interim and EoT PET, where SUV4.0 may not always be appropriate.