ArticleFrontiers in oncology2022
Establishment and Validation of a Novel MRI Radiomics Feature-Based Prognostic Model to Predict Distant Metastasis in Endemic Nasopharyngeal Carcinoma.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Recent Advances in Surveillance Strategies for Nasopharyngeal Carcinoma.Current oncology reports · 2026Review
- Deep learning-radiomics-SUVmax integration fromFrontiers in oncology · 2026Article
- Integrating Radiomics and Deep-Learning for Prognostic Evaluation in Nasopharyngeal Carcinoma.Medicina (Kaunas, Lithuania) · 2025Review
- Performance of MRI-based radiomics for prediction of residual disease status in patients with nasopharyngeal carcinoma after radical radiotherapy.Scientific reports · 2025Article
- Using high-repeatable radiomic features improves the cross-institutional generalization of prognostic model in esophageal squamous cell cancer receiving definitive chemoradiotherapy.Insights into imaging · 2024Article
- Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Article
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Abstract
Purpose: We aimed to establish a prognostic model based on magnetic resonance imaging (MRI) radiomics features for individual distant metastasis risk prediction in patients with nasopharyngeal carcinoma (NPC). Methods: Regression analysis was applied to select radiomics features from T1-weighted (T1-w), contrast-enhanced T1-weighted (T1C-w), and T2-weighted (T2-w) MRI scans. All prognostic models were established using a primary cohort of 518 patients with NPC. The prognostic ability of the radiomics, clinical (based on clinical factors), and merged prognostic models (integrating clinical factors with radiomics) were identified using a concordance index (C-index). Models were tested using a validation cohort of 260 NPC patients. Distant metastasis-free survival (DMFS) were calculated by using the Kaplan-Meier method and compared by using the log-rank test. Results: In the primary cohort, seven radiomics prognostic models showed similar discrimination ability for DMFS to the clinical prognostic model (P=0.070-0.708), while seven merged prognostic models displayed better discrimination ability than the clinical prognostic model or corresponding radiomics prognostic models (all P<0.001). In the validation cohort, the C-indices of seven radiomics prognostic models (0.645-0.722) for DMFS prediction were higher than in the clinical prognostic model (0.552) (P=0.016 or <0.001) or in corresponding merged prognostic models (0.605-0.678) (P=0.297 to 0.857), with T1+T1C prognostic model (based on Radscore combinations of T1 and T1C Radiomics models) showing the highest C-index (0.722). In the decision curve analysis of the validation cohort for all prognostic models, the T1+T1C prognostic model displayed the best performance. Conclusions: Radiomics models, especially the T1+T1C prognostic model, provided better prognostic ability for DMFS in patients with NPC.
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