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◆ Frontiers in oncology2026-01-01

Investigation of clinical applicability of deep learning-based virtual contrast-enhanced MRI for nasopharyngeal carcinoma patients.

Guangping Zeng, Wen Li, Xue Li, Rui Sun, Saikit Lam, Tianyu Xiong, Ho-Yin Cheung, Ho-Fun Lee, Kar-Ho Lee, Lai-Yin Cheung, Chenbin Liu, Zhou Liu, Xiaping Wei, Jing Cai

一句话结论 · In one sentence

MMgSN-Net-based VCE-MRI demonstrates favorable clinical feasibility in selected patient subgroups, T1/T2-stage NPC and T3/T4-stage NPC with regular tumor morphology, potentially reducing GBCAs administration in 67.3% of eligible patients when applying these stratification criteria.

原始摘要(英文原文)· Original abstract
PURPOSE: There is a lack of research evaluating the clinical performance of virtual contrast-enhanced MRI (VCE-MRI). This study aims to assess the clinical utility of an established VCE-MRI technique in NPC patients and to establish patient selection criteria. MATERIALS AND METHODS: We retrospectively collected data from 333 NPC patients across six institutions (2012-2023). VCE-MRI was synthesized from T1-weighted and T2-weighted MRI for each patient using the multimodality-guided synergistic neural network (MMgSN-Net), which was pre-trained on a large cohort of 1682 NPC patients from 14 institutions. Three experienced radiologists independently assessed image quality using a 5-point Likert scale, with scores below 4 deemed clinically unacceptable. The association between assessment results and patient clinical characteristics of corresponding patients (tumor diameter, T-stage, shape, gender, age) was analyzed to stratify the patients suitable for MMgSN-Net-based VCE-MRI. RESULTS: Clinically acceptability of VCE-MRI significantly decreased with larger tumor diameters (p=0.001), advanced T-stage (T1: 100%, T2: 95.1%, T3: 90.0%, T4: 69.6%; p<0.001), and complex tumor shape (regular: 94.8% vs. complex: 74.0%; p<0.001). Age and gender had no significant impact (p=0.327, 0.773). 13 unacceptable patients were found due to severe VCE-MRI artifacts. VCE-MRI achieved high clinical acceptability in T1/T2 patients (109/114, 95.6%) and T3/T4 patients with regular tumor shapes (115/120, 95.8%). In contrast, acceptability significantly decreased to 73.0% (65/89) for T3/T4 tumors with complex shapes. The T-staging result discordance rate between VCE-MRI and CE-MRI is 8.4%, with overstaging in 15 cases. CONCLUSION: MMgSN-Net-based VCE-MRI demonstrates favorable clinical feasibility in selected patient subgroups, T1/T2-stage NPC and T3/T4-stage NPC with regular tumor morphology, potentially reducing GBCAs administration in 67.3% of eligible patients when applying these stratification criteria.
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Investigation of clinical applicability of deep learning-based virtual contrast-enhanced MRI for nasopharyngeal carcinoma patients. — 科研速览 Science Skim