MedNext-Insight Model for Automated Metabolic Tumor Volume Delineation on Computed Tomography and Prognostic Value in Nasopharyngeal Carcinoma.
Authors
Affiliations (6)
Affiliations (6)
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510060, China. Electronic address: [email protected].
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China.
- Shanghai United Imaging Healthcare Co., Ltd. Shanghai, 201807, China.
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510060, China.
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China. Electronic address: [email protected].
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China. Electronic address: [email protected].
Abstract
To develop a deep learning model for automated metabolic tumor volume (MTV) delineation on routine computed tomography (CT) without positron emission tomography (PET) and validate its prognostic value in nasopharyngeal carcinoma (NPC). A retrospective cohort of 392 NPC patients undergoing pre-radiotherapy <sup>18</sup>F-FDG PET/CT in 2021 was enrolled and randomly divided into training (n = 314, including 63 for validation) and test (n = 78) cohorts. Ground truth MTV (GT_MTV) was generated from PET-registered CT using standardized uptake value (SUV) > 2.5 within the primary gross tumor volume (GTVp). A seven-layer MedNext-Insight model with dual-window CT inputs and Dice-Focal loss was trained to predict MTV using CT alone. Segmentation performance was compared with nnUNetV2, Pix2Pix, and 3D-CycleGAN primarily using Dice similarity coefficient (DSC) and sensitivity. Radiomic features were extracted from predicted MTV (Pred_MTV) and GT_MTV to construct Cox proportional hazards models for event-free survival (EFS), evaluated by concordance index (C-index). Robustness was further assessed in an internal temporal validation cohort from 2022 with different scanners (n = 135) using planning CT as the sole input. MedNext-Insight achieved the highest MTV delineation DSC (Mean ± SD: 0.808 ± 0.110 versus 0.740-0.782, all P < 0.05) with improved sensitivity. After excluding 12 patients with baseline distant metastasis from EFS analysis, Pred_MTV-derived radiomics showed strong reproducibility (median intraclass correlation coefficient: 0.816) and comparable prognostic performance to GT_MTV (C-index [95% CI]: 0.712 [0.516-0.899] vs 0.744 [0.601-0.884]; P = 0.730). MTV-based radiomics outperformed GTVp-derived features, particularly when combined with clinical variables (C-index: 0.809 [0.678-0.919]). In the internal temporal validation cohort, CT-only Pred_MTV maintained stable segmentation accuracy and prognostic discrimination (log-rank test P < 0.05). MedNext-Insight enables accurate PET-free MTV delineation on routine CT with prognostic value, supporting a resource-efficient approach for risk stratification and informing potential future biology-guided adaptive radiotherapy in NPC.