MedNeXt-Based Automated Tumor Segmentation and TMTV Prognostication in Hodgkin Lymphoma PET Images: A Retrospective Study.
6 affiliations
- 1Department of Hematology, Nanjing Drum Tower Hospital, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210008, China.
- 2School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210008, China.
- 3Department of Hematology, Nanjing Drum Tower Hospital, Gulou Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing 210008, China.
- 4Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China.
- 5Department of Nuclear Medicine, Jiangsu Provincial Hospital, Nanjing Medical University, Nanjing 210029, China.
- 6Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
Abstract
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.
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BibTeX
@article{zhang2026mednextbased,
title = {{MedNeXt-Based Automated Tumor Segmentation and TMTV Prognostication in Hodgkin Lymphoma PET Images: A Retrospective Study.}},
author = {Haoan Zhang and Fuming Sun and Hang Yu and Xinyuan Chen and Yixuan Shi and Yue Teng and Wenze He and Yu Jin and Shangdong Liu and Chongyang Ding and Chong Jiang and Jingyan Xu},
journal = {Cancers},
year = {2026},
month = oct,
doi = {10.3390/cancers18193196},
url = {https://europepmc.org/article/MED/42882889},
note = {CC BY},
}
RIS
TY - JOUR
TI - MedNeXt-Based Automated Tumor Segmentation and TMTV Prognostication in Hodgkin Lymphoma PET Images: A Retrospective Study.
AU - Haoan Zhang
AU - Fuming Sun
AU - Hang Yu
AU - Xinyuan Chen
AU - Yixuan Shi
AU - Yue Teng
AU - Wenze He
AU - Yu Jin
AU - Shangdong Liu
AU - Chongyang Ding
AU - Chong Jiang
AU - Jingyan Xu
PY - 2026
DA - 2026/10/03
JO - Cancers
DO - 10.3390/cancers18193196
UR - https://europepmc.org/article/MED/42882889
AB - 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.
SN - 2072-6694
ER -