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

Abstract

Simple Summary Hodgkin’s lymphoma (HL) is a blood cancer that mainly affects young adults and older individuals. Although most patients are cured, some experience treatment failure or relapse, making it important to identify high-risk patients. The total metabolic tumor volume (TMTV) on PET/CT scans is a useful prognostic indicator, but manual measurement is time-consuming and operator-dependent. In this study, we used a deep learning model (MedNeXt) to automatically segment HL lesions on PET images from 192 patients across two centers. The model achieved moderate-to-high accuracy (DSC = 0.702), and the predicted TMTV was potentially associated with progression-free survival (p = 0.048), though not with overall survival (p = 0.163). These findings suggest that predicted TMTV has the potential to serve as a practical tool for standardized risk stratification in HL, pending further validation in larger cohorts.