Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Using a Longitudinally Aware Segmentation Network.
AI interpretation is pending for this paper.
Open original publication →What the AI sees
Not AI summarized yet.
Research significance
Pending deeper interpretation.
Source abstract
Purpose To develop a longitudinally aware segmentation network (LAS-Net) that can quantify serial PET/CT images for pediatric patients with Hodgkin lymphoma. Materials and Methods This retrospective study included baseline (PET1) and interim (PET2) PET/CT images from 297 pediatric patients enrolled in two Children's Oncology Group clinical trials (AHOD1331 and AHOD0831). The internal dataset included 200 patients (enrolled between March 2015 and August 2019; median age, 15.4 years [range, 5.6-22.0 years]; 107 male), and the external testing dataset included 97 patients (enrolled between December 2009 and January 2012; median age, 15.8 years [range, 5.2-21.4 years]; 59 male). LAS-Net incorporates longitudinal cross-attention, allowing relevant features from PET1 to inform the analysis of PET2. The model's lesion segmentation performance on PET1 images was evaluated using Dice coefficients, and lesion detection performance on PET2 images was evaluated using F1 scores. In addition, quantitative PET metrics, including metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in PET1, as well as qPET and percentage difference between baseline and interim maximum standardized uptake value (∆SUVmax) in PET2, were extracted and compared against physician-derived measurements. Agreement between model and physician-derived measurements was quantified using Spearman correlation, and bootstrap resampling was used for statistical analysis. Results LAS-Net detected residual lymphoma on PET2 scans with an F1 score of 0.61 (precision/recall: 0.62/0.60), outperforming all comparator methods (P < .01). For baseline segmentation, LAS-Net achieved a mean Dice score of 0.77. In PET quantification, LAS-Net's measurements of qPET, ∆SUVmax, MTV, and TLG were strongly correlated with physician measurements, with Spearman ρ values of 0.78, 0.80, 0.93, and 0.96, respectively. The quantification performance remained high, with a slight decrease, in an external testing cohort. Conclusion LAS-Net demonstrated significant improvements in quantifying PET metrics across serial scans in pediatric patients with Hodgkin lymphoma, highlighting the value of longitudinal awareness in evaluating multi-time-point imaging datasets. Keywords: Pediatrics, PET/CT, Lymphoma, Segmentation, Quantification, Supervised Learning, Convolutional Neural Network (CNN), Quantitative PET, Longitudinal Analysis, Deep Learning, Image Segmentation Supplemental material is available for this article. Clinical trial registration no. NCT02166463 and NCT01026220 © RSNA, 2025 See also commentary by Khosravi and Gichoya in this issue.