ArticleFrontiers in neurology2023
MRI-based radiomics models for the early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Prognostic models for radiation-induced complications after radiotherapy in head and neck cancer patients.The Cochrane database of systematic reviews · 2025Pooled it
- High-Recall Biomedical Language Models for Radiation Oncology Evidence Synthesis: An Integrated Systematic Review of Prediction Models for Radiotherapy-Induced Complications in Nasopharyngeal Carcinoma.Life (Basel, Switzerland) · 2026Review
- Phenotypic comorbidity network analysis of nasopharyngeal carcinoma: a multicenter study based on hospital discharge records.Frontiers in oncology · 2026Article
- Article
- Deep learning MRI-based radiomic models for predicting recurrence in locally advanced nasopharyngeal carcinoma after neoadjuvant chemoradiotherapy: a multi-center study.Clinical & experimental metastasis · 2025Article
- MRI-based radiomics models predict cystic brain radionecrosis of nasopharyngeal carcinoma after intensity modulated radiotherapy.Frontiers in neurology · 2024Article
- Case report: intracranial lesions in a patient with anxiety and depression: tumor recurrence or radiation encephalopathy?Frontiers in oncology · 2024Article
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study was conducted to develop and validate a radiomics-clinics combined model-based magnetic resonance imaging (MRI) radiomics and clinical features for the early prediction of radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC). Methods: This retrospective study was conducted using data from 130 patients with NPC (80 patients with and 50 patients without RTLI) who received radiotherapy. Cases were assigned randomly to training ( Results: Six texture features and three independent clinical factors associated significantly with RTLI were used to build the combined model. The AUCs for the combined and radiomics models were 0.962 [95% confidence interval (CI), 0.9306-0.9939] and 0.904 (95% CI, 0.8431-0.9651), respectively, for the training cohort and 0.947 (95% CI, 0.8841-1.0000) and 0.891 (95% CI, 0.7903-0.9930), respectively, for the testing cohort. All of these values exceeded those for the clinics model (AUC = 0.809 and 0.713 for the training and testing cohorts, respectively). Decision curve analysis showed that the combined model had a good corrective effect. Conclusion: The radiomics-clinics combined model developed in this study showed good performance for predicting RTLI in patients with NPC.
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