ArticleEuropean radiology2024
A contrast-enhanced CT radiomics-based model to identify candidates for deintensified chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma patients.
Article in European radiology, 2024. 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, 7 citations in OpenAlex.
- CD38 degrades MAVS through mitophagy to inhibit type I interferon secretion in nasopharyngeal carcinoma cells and impairs CD8Nature communications · 2026Article
- Nasopharyngeal Carcinoma at the Virology Precision Oncology Nexus: Decoding Molecular Alterations for Early Intervention and Therapeutic Innovation.Biomolecules · 2026Review
- Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for predicting programmed death ligand 1 expression in nasopharyngeal carcinoma.BMC medical imaging · 2025Article
- Integrated clinical-radiomic model for predicting treatment response of concurrent chemo-radiotherapy and radiotherapy alone in controversial subgroup of AJCC/UICC ninth edition stage I nasopharyngeal cancer.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Leveraging Artificial Intelligence and Radiomics for Improved Nasopharyngeal Carcinoma Prognostication.Cancer medicine · 2025Article
- Predicting the efficacy of chemoradiotherapy in advanced nasopharyngeal carcinoma patients: an MRI radiomics and machine learning approach.Frontiers in oncology · 2025Article
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8 authors at 1 institution in 1 country.
Funding
Abstract
objectivesTo develop a contrast-enhanced CT (CECT) radiomics-based model to identify locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients who would benefit from deintensified chemoradiotherapy.
methodsLA-NPC patients who received low-dose concurrent cisplatin therapy (cumulative: 150 mg/m
resultsTraining and validation groups consisted of 66 and 33 patients, respectively. Three significant independent radiomics features (flatness, mean, and gray level non-uniformity in gray level dependence matrix (GLDM-GLN)) were found. The radiomics score showed better predictive ability than the T stage (concordance index (C-index): 0.67 vs. 0.61, AUC: 0.75 vs. 0.60). The R + N model had better predictive performance and more effective risk stratification than the T + N model (C-index: 0.77 vs. 0.68, AUC: 0.80 vs. 0.70). The R + N model identified a low-risk group as deintensified chemoradiotherapy candidates in which no patient developed progression within 3 years, with 5-year progression-free survival (PFS) and overall survival (OS) both 90.7% (hazard ratio (HR) = 4.132, p = 0.018).
conclusionOur radiomics-based model combining radiomics score and N stage can identify specific LA-NPC candidates for whom de-escalation therapy can be performed without compromising therapeutic efficacy. CLINICAL RELEVANCE STATEMENT: Our study shows that the radiomics-based model (R + N) can accurately stratify patients into different risk groups, with satisfactory prognosis in the low-risk group when treated with low-dose concurrent chemotherapy, providing new options for individualized de-escalation strategies. KEY POINTS: • A radiomics score, consisting of 3 predictive radiomics features (flatness, mean, and GLDM-GLN) integrated with the N stage, can identify specific LA-NPC populations for deintensified treatment. • In the selection of LA-NPC candidates for de-intensified treatment, radiomics score extracted from primary nasopharyngeal tumors based on CECT can be superior to traditional T stage classification as a predictor.
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