Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%–52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.

中文翻译：

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MR 和 DCE-MR 放射组学模型对鼻咽癌预后预测的综合集成分析

尽管鼻咽癌（NPC）的预后预测仍然是一个关键的研究领域，但动态对比增强磁共振（DCE-MR）的作用却很少被探索。本研究旨在使用基于磁共振 (MR) 和 DCE-MR 的放射组学模型探讨 DCR-MR 在预测 NPC 患者无进展生存期 (PFS) 中的作用。共有 434 名具有两个 MR 扫描序列的患者被纳入。基于 MR 和 DCE-MR 的放射组学模型是基于 289 名仅使用 MR 扫描序列的患者和 145 名具有四个附加药代动力学参数（血管外细胞外空间体积分数 (ve)、血浆空间体积分数 (vp)、采用相关分析、最小绝对收缩和选择算子回归、多元Cox等方法，构建了MR和DCE-MR的组合模型。比例风险回归，建立放射组学模型，最后计算净重分类指数和C指数来评估和比较放射组学模型的预后性能，并进行Kaplan-Meier生存曲线分析以考察模型对风险进行分层的能力与基于 MR 和 DCE-MR 的模型相比，MR 和 DCE-MR 放射学特征的整合显着增强了预后预测性能，测试集 C 指数分别为 0.808、0.729 和 0.731。组合放射组学模型将净重分类提高了 22.9%–52.6%，并且可以显着对 NPC 患者的风险水平进行分层（p = 0.036）。此外，基于 MR 的放射组学特征图在反映 NPC 中潜在的血管生成信息方面取得了与 DCE-MR 药代动力学参数相似的结果。与传统的基于 MR 的放射组学模型相比，整合 MR 和 DCE-MR 的组合放射组学模型在提供更准确的预后预测方面显示出良好的结果，并在量化和监测与 NPC 预后相关的表型变化方面提供了更多的临床益处。