ArticleEuropean radiology2019
Radiomics on multi-modalities MR sequences can subtype patients with non-metastatic nasopharyngeal carcinoma (NPC) into distinct survival subgroups.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 37 papers.
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37 citing papers in PubMed, 69 citations in OpenAlex.
- Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images.BMC medical imaging · 2021Trial
- Prognostic Analysis of Patients With N1 Nasopharyngeal Carcinoma With Different Lymph Node Characteristics.Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery · 2026Article
- Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma.Computational and structural biotechnology journal · 2024Article
- Novel prediction model combining PET/CT metabolic parameters, inflammation markers, and TNM stage: prospects for personalizing prognosis in nasopharyngeal carcinoma.Annals of nuclear medicine · 2024Article
- 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
- Performance of radiomics in the differential diagnosis of parotid tumors: a systematic review.Frontiers in oncology · 2024Review
- Multimodality radiomics analysis based on [European radiology · 2023Article
- Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma.Journal of clinical medicine · 2023Review
- Survival rate prediction of nasopharyngeal carcinoma patients based on MRI and gene expression using a deep neural network.Cancer science · 2023Article
- Article
- Review
- Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Evolutionary route of nasopharyngeal carcinoma metastasis and its clinical significance.Nature communications · 2023Article
- Article
- Extraction parameter optimized radiomics for neoadjuvant chemotherapy response prognosis in advanced nasopharyngeal carcinoma.Clinical and translational radiation oncology · 2022Article
- Review
- Radiomics based on pretreatment MRI for predicting distant metastasis of nasopharyngeal carcinoma: A preliminary study.Frontiers in oncology · 2022Article
- Prognostic value of PET/CT and MR-based baseline radiomics among patients with non-metastatic nasopharyngeal carcinoma.Frontiers in oncology · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo explore and evaluate the feasibility of radiomics in stratifying nasopharyngeal carcinoma (NPC) into distinct survival subgroups through multi-modalities MRI.
methodsA total of 658 patients (training cohort: 424; validation cohort: 234) with non-metastatic NPC were enrolled in the retrospective analysis. Each slice was considered as a sample and 4863 radiomics features on the tumor region were extracted from T1-weighted, T2-weighted, and contrast-enhanced T1-weighted MRI. Consensus clustering and manual aggregation were performed on the training cohort to generate a baseline model and classification reference used to train a support vector machine classifier. The risk of each patient was defined as the maximum risk among the slices. Each patient in the validation cohort was assigned to the risk model using the trained classifier. Harrell's concordance index (C-index) was used to measure the prognosis performance, and differences between subgroups were compared using the log-rank test.
resultsThe training cohort was clustered into four groups with distinct survival patterns. Each patient was assigned to one of the four groups according to the estimated risk. Our method gave a performance (C-index = 0.827, p < .004 and C-index = 0.814, p < .002) better than the T-stage (C-index = 0.815, p = .002 and C-index = 0.803, p = .024), competitive to and more stable than the TNM staging system (C-index = 0.842, p = .003 and C-index = 0.765, p = .050) in the training cohort and the validation cohort.
conclusionsThrough investigating a large one-institutional cohort, the quantitative multi-modalities MRI image phenotypes reveal distinct survival subtypes. KEY POINTS: • Radiomics phenotype of MRI revealed the subtype of nasopharyngeal carcinoma (NPC) patients with distinct survival patterns. • The slice-wise analysis method on MRI helps to stratify patients and provides superior prognostic performance over the TNM staging method. • Risk estimation using the highest risk among slices performed better than using the majority risk in prognosis.
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