2024/04/12 by Xin Tie, Muheon Shin, Tie, Xin +23 · 1 citation
Medicine · #Medical Imaging Techniques and Applications #Radiomics and Machine Learning in Medical Imaging #Lymphoma Diagnosis and Treatment
paper · doi:10.48550/arxiv.2404.08611
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. Materials and Methods: correlations and employed bootstrap resampling for statistical analysis. Results: 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, highlighting the value of longitudinal awareness in evaluating multi-time-point imaging datasets.