ArticleEBioMedicine2019
Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: A retrospective cohort study.
Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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68 citing papers in PubMed.
- Nomograms predicting prognosis for locally advanced hypopharyngeal squamous cell carcinoma.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 · 2022Trial
- Identification of high-risk carotid plaque with MRI-based radiomics and machine learning.European radiology · 2021Trial
- The prognostic value of intra- and peritumoral habitat analysis in nasopharyngeal carcinoma: combining IVIM and ASL.European radiology · 2026Article
- Development and validation of a prognostic prediction model for lumbar-disc herniation based on machine learning and fusion of clinical text data and radiomic features.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Non-Gaussian diffusion MRI using CTRW and FROC models for early treatment response assessment in nasopharyngeal carcinoma.BMC medical imaging · 2026Article
- Deep learning-radiomics-SUVmax integration fromFrontiers in oncology · 2026Article
- A Clinical-Radiomics Nomogram Based on Pretreatment Magnetic Resonance Imaging Predicting Tumor Residual at the End of Radiotherapy in Patients With Nasopharyngeal Carcinoma.International journal of biomedical imaging · 2026Article
- [Nasopharyngeal carcinoma in children: clinical analysis of 6 cases and literature review].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2025Review
- [Effect of AI-assisted compressed sensing acceleration on MRI radiomic feature extraction and staging model performance for nasopharyngeal carcinoma].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Treatment response-adapted risk index model for survival prediction and adjuvant chemotherapy selection in nonmetastatic nasopharyngeal carcinoma.NPJ digital medicine · 2025Article
- Early prediction of progression-free survival of patients with locally advanced nasopharyngeal carcinoma using multi-parametric MRI radiomics.BMC cancer · 2025Article
- Integrating mitochondrial and lysosomal gene analysis for breast cancer prognosis using machine learning.Scientific reports · 2025Article
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- Enhancing Nasopharyngeal Carcinoma Survival Prediction: Integrating Pre- and Post-Treatment MRI Radiomics with Clinical Data.Journal of imaging informatics in medicine · 2024Article
- Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics.European journal of radiology open · 2024Article
- Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach.Biomedicines · 2024Article
- Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments.Cancer reports (Hoboken, N.J.) · 2024Review
- Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature.Annals of hepato-biliary-pancreatic surgery · 2024Review
- A contrast-enhanced CT radiomics-based model to identify candidates for deintensified chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma patients.European radiology · 2024Article
8 more citing papers are in PubMed but not listed here.
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15 authors.
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Abstract
backgroundWe aimed to identify a magnetic resonance imaging (MRI)-based model for assessment of the risk of individual distant metastasis (DM) before initial treatment of nasopharyngeal carcinoma (NPC).
methodsThis retrospective cohort analysis included 176 patients with NPC. Using the PyRadiomics platform, we extracted the imaging features of primary tumors in all patients who did not exhibit DM before treatment. Subsequently, we used minimum redundancy-maximum relevance and least absolute shrinkage and selection operator algorithms to select the strongest features and build a logistic model for DM prediction. The independent statistical significance of multiple clinical variables was tested using multivariate logistic regression analysis.
findingsIn total, 2780 radiomic features were extracted. A DM MRI-based model (DMMM) comprising seven features was constructed for the classification of patients into high- and low-risk groups in a training cohort and validated in an independent cohort. Overall survival was significantly shorter in the high-risk group than in the low-risk group (P < 0·001). A radiomics nomogram based on radiomic features and clinical variables was developed for DM risk assessment in each patient, and it showed a significant predictive ability in the training [area under the curve (AUC), 0·827; 95% confidence interval (CI), 0.754-0.900] and validation (AUC, 0.792; 95% CI, 0.633-0.952) cohorts.
interpretationDMMM can serve as a visual prognostic tool for DM prediction in NPC, and it can improve treatment decisions by aiding in the differentiation of patients with high and low risks of DM. FUND: This research received financial support from the National Natural Science Foundation of China (81571664, 81871323, 81801665, 81771924, 81501616, 81671851, and 81527805); the National Natural Science Foundation of Guangdong Province (2018B030311024); the Science and Technology Planning Project of Guangdong Province (2016A020216020); the Scientific Research General Project of Guangzhou Science Technology and Innovation Commission (201707010328); the China Postdoctoral Science Foundation (2016M600145); and the National Key R&D Program of China (2017YFA0205200, 2017YFC1308700, and 2017YFC1309100).
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