ArticleRadiology. Artificial intelligence2025
Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Using a Longitudinally Aware Segmentation Network.
Article in Radiology. Artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic role of PET/CT volumetric parameters in paediatric Hodgkin lymphoma: A systematic review and expert recommendations from the International SEARCH for CAYAHL Group.British journal of haematology · 2026Pooled it
- Evaluation of Baseline and Interim-Therapy PET Features for Prognostication in High-Risk Pediatric Hodgkin Lymphoma: A Retrospective Analysis of the AHOD1331 Trial.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Multimodality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma.International journal of radiation oncology, biology, physics · 2026Article
- CNN-based detection of pediatric lymphoma on whole body [American journal of nuclear medicine and molecular imaging · 2026Article
- Temporal Hindsight, Clinical Foresight: Longitudinal Lymphoma Analysis at PET/CT.Radiology. Artificial intelligence · 2025Article
- Deep Learning for Longitudinal Gross Tumor Volume Segmentation in MRI-Guided Adaptive Radiotherapy for Head and Neck Cancer.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges.Digital healthReview
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13 authors.
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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 (∆SUV
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