ArticleFrontiers in oncology2021
Predictive Value of a Combined Model Based on Pre-Treatment and Mid-Treatment MRI-Radiomics for Disease Progression or Death in Locally Advanced Nasopharyngeal Carcinoma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- Recent Advances in Surveillance Strategies for Nasopharyngeal Carcinoma.Current oncology reports · 2026Review
- Multi-regional radiomics based on planning CT and information complementarity between GTVp and GTVn: an explainable model for predicting 5-year recurrence risk in HPV-positive oropharyngeal cancer.Radiation oncology (London, England) · 2026Article
- Integrating Radiomics and Deep-Learning for Prognostic Evaluation in Nasopharyngeal Carcinoma.Medicina (Kaunas, Lithuania) · 2025Review
- Radiomics-based lymph nodes prognostic models from three MRI regions in nasopharyngeal carcinoma.Heliyon · 2024Article
- Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- A nomogram model based on pre-treatment and post-treatment MR imaging radiomics signatures: application to predict progression-free survival for nasopharyngeal carcinoma.Radiation oncology (London, England) · 2023Article
- Machine Learning Based on MRI DWI Radiomics Features for Prognostic Prediction in Nasopharyngeal Carcinoma.Cancers · 2022Article
- Article
- Longitudinal MRI Temporal Transformer Fusion Model for Predicting Induction Chemotherapy Efficacy in Locally Advanced Nasopharyngeal Carcinoma.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeA combined model was established based on the MRI-radiomics of pre- and mid-treatment to assess the risk of disease progression or death in locally advanced nasopharyngeal carcinoma. MATERIALS AND
methodsA total of 243 patients were analyzed. We extracted 10,400 radiomics features from the primary nasopharyngeal tumors and largest metastatic lymph nodes on the axial contrast-enhanced T1 weighted and T2 weighted in pre- and mid-treatment MRI, respectively. We used the SMOTE algorithm, center and scale and box-cox, Pearson correlation coefficient, and LASSO regression to construct the pre- and mid-treatment MRI-radiomics prediction model, respectively, and the risk scores named P score and M score were calculated. Finally, univariate and multivariate analyses were used for P score, M score, and clinical data to build the combined model and grouped the patients into two risk levels, namely, high and low.
resultA combined model of pre- and mid-treatment MRI-radiomics successfully categorized patients into high- and low-risk groups. The log-rank test showed that the high- and low-risk groups had good prognostic performance in PFS (P<0.0001, HR: 19.71, 95% CI: 12.77-30.41), which was better than TNM stage (P=0.004, HR:1.913, 95% CI:1.250-2.926), and also had an excellent predictive effect in LRFS, DMFS, and OS.
conclusionRisk grouping of LA-NPC using a combined model of pre- and mid-treatment MRI-radiomics can better predict disease progression or death.
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