MedNeXt-Based Automated Tumor Segmentation and TMTV Prognostication in Hodgkin Lymphoma PET Images: A Retrospective...

Background/Objectives: To evaluate a deep learning-based method for automatic segmentation of Hodgkin lymphoma (HL) on PET images and to explore the prognostic value of total metabolic tumor volume (TMTV) derived from segmentation. Methods: PET datasets from two independent institutions were retrospectively collected and divided into training and validation cohorts. MedNeXt was trained for HL PET segmentation and benchmarked against five representative segmentation architectures under the same data split, preprocessing pipeline, and evaluation framework. Segmentation performance was assessed using the Dice similarity coefficient (DSC), Jaccard index (JSC), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD). Bland-Altman and linear regression analyses evaluated agreement between ground-truth TMTV (gtTMTV) and predicted TMTV (pTMTV). Cox proportional hazards regression analysis was applied to assess the prognostic value of pTMTV. Results: In the validation cohort, the mean values (±standard deviation) of DSC, HD95, JSC, and ASSD were 0.702 ± 0.191, 67.998 ± 99.220, 0.561 ± 0.203, and 16.992 ± 31.659, respectively. Among the evaluated segmentation models, MedNeXt showed the best segmentation performance. Mean gtTMTV and pTMTV were 424.681 ± 498.811 cm³ and 405.871 ± 466.984 cm³, showing good agreement. pTMTV was significantly associated with progression-free survival (PFS) (p = 0.048), but not overall survival (OS) (p = 0.163). Conclusions: MedNeXt achieved reliable HL segmentation, although tumor boundary delineation requires further improvement. The derived pTMTV may serve as a prognostic indicator for PFS but showed no significant association with OS. Further validation in larger cohorts is warranted.