$\textbf{Purpose}$: Automatic quantification of longitudinal changes in PET
scans for lymphoma patients has proven challenging, as residual disease in
interim-therapy scans is often subtle and difficult to detect. Our goal was to
develop a longitudinally-aware segmentation network (LAS-Net) that can quantify
serial PET/CT images for pediatric Hodgkin lymphoma patients.
$\textbf{Materials and Methods}$: This retrospective study included baseline
(PET1) and interim (PET2) PET/CT images from 297 patients enrolled in two
Children's Oncology Group clinical trials (AHOD1331 and AHOD0831). LAS-Net
incorporates longitudinal cross-attention, allowing relevant features from PET1
to inform the analysis of PET2. Model performance was evaluated using Dice
coefficients for PET1 and detection F1 scores for PET2. Additionally, we
extracted and compared quantitative PET metrics, including metabolic tumor
volume (MTV) and total lesion glycolysis (TLG) in PET1, as well as qPET and
$\Delta$SUVmax in PET2, against physician measurements. We quantified their
agreement using Spearman's $\rho$ correlations and employed bootstrap
resampling for statistical analysis. $\textbf{Results}$: LAS-Net detected
residual lymphoma in PET2 with an F1 score of 0.606 (precision/recall:
0.615/0.600), outperforming all comparator methods (P<0.01). For baseline
segmentation, LAS-Net achieved a mean Dice score of 0.772. In PET
quantification, LAS-Net's measurements of qPET, $\Delta$SUVmax, MTV and TLG
were strongly correlated with physician measurements, with Spearman's $\rho$ of
0.78, 0.80, 0.93 and 0.96, respectively. The performance remained high, with a
slight decrease, in an external testing cohort. $\textbf{Conclusion}$: LAS-Net
achieved high performance in quantifying PET metrics across serial scans,
highlighting the value of longitudinal awareness in evaluating multi-time-point
imaging datasets.

LAS-Net 在评估多时间点成像数据集中，取得了对 PET 指标的高度性能，凸显了纵向认知的价值。