Objective:To evaluate MedNeXt, a deep learning network, for automated segmentation of 18F-FDG PET images in angioimmunoblastic T-cell lymphoma (AITL) and validate the prognostic utility of predicted total metabolic tumor volume (pTMTV).

Methods:A multicenter cohort of 84 AITL patients was stratified into training (n=68) and validation (n=16) sets (8:2 ratio). MedNeXt was trained using a Dice-cross-entropy dual-loss function. Segmentation accuracy was quantified through seven metrics: Dice similarity coefficient (DSC), Jaccard index (JSC), sensitivity (SEN), positive predictive value (PPV), false discovery rate (FDR), 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD). Prognostic significance of pTMTV was assessed via Cox proportional hazards models.

Results:Validation cohort performance metrics (mean±SD): DSC=0.769±0.153, JSC=0.645±0.173, PPV=0.773±0.201, FDR=0.227±0.201, SEN=0.799±0.110, ASSD=1.903±2.288 mm, HD95=32.183±44.664 mm. Ground truth TMTV (gtTMTV=949.4±734.5 cm³) and pTMTV (934.1±617.7 cm³) demonstrated strong agreement in Bland-Altman analysis and linear regression (R²=0.868, p<0.001). Elevated pTMTV (≥606.9 cm³) independently predicted reduced progression-free survival (HR=1.781, 95% CI:1.057-3.002; p=0.030) and overall survival (HR=2.190, 95% CI:1.042-4.600; p=0.038).

Conclusion:MedNeXt achieves precise lesion delineation and automated quantification of TMTV in AITL PET imaging. pTMTV emerges as an independent prognostic predictor for survival outcomes, demonstrating clinical viability for lymphoma management.

This content is only available as a PDF.
2025
Sign in via your Institution