ArticleBMC medical imaging2020
Cervical spine osteoradionecrosis or bone metastasis after radiotherapy for nasopharyngeal carcinoma? The MRI-based radiomics for characterization.
Article in BMC medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 26 citations in OpenAlex.
- Multi-Sequence Fusion MRI Deep Learning Models for Discrimination of Recurrent Nasopharyngeal Carcinoma and Osteoradionecrosis.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review.Cancers · 2026Review
- Use of Radiomics in Characterizing Tumor Hypoxia.International journal of molecular sciences · 2025Review
- Enhancing Nasopharyngeal Carcinoma Survival Prediction: Integrating Pre- and Post-Treatment MRI Radiomics with Clinical Data.Journal of imaging informatics in medicine · 2024Article
- Computational Modeling, Augmented Reality, and Artificial Intelligence in Spine Surgery.Advances in experimental medicine and biology · 2024Review
- Risk factor analysis and development of predictive models for osteoradionecrosis in patients with nasopharyngeal carcinoma after concurrent chemoradiotherapy.American journal of cancer research · 2024Article
- Radiomics‑Clinical model based onJournal of cancer research and clinical oncology · 2023Article
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
- Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review.Cancers · 2023Review
- Deep Learning for Predicting Distant Metastasis in Patients with Nasopharyngeal Carcinoma Based on Pre-Radiotherapy Magnetic Resonance Imaging.Combinatorial chemistry & high throughput screening · 2023Article
- Differentiation of predominantly osteolytic from osteoblastic spinal metastases based on standard magnetic resonance imaging sequences: a comparison of radiomics model versus semantic features logistic regression model findings.Quantitative imaging in medicine and surgery · 2022Article
- Review
- Artificial Intelligence in Bone Metastases: An MRI and CT Imaging Review.International journal of environmental research and public health · 2022Review
- Magnetic Resonance Imaging Features on Deep Learning Algorithm for the Diagnosis of Nasopharyngeal Carcinoma.Contrast media & molecular imaging · 2022Article
- Prediction of Response to Induction Chemotherapy Plus Concurrent Chemoradiotherapy for Nasopharyngeal Carcinoma Based on MRI Radiomics and Delta Radiomics: A Two-Center Retrospective Study.Frontiers in oncology · 2022Article
- Human/eukaryotic ribosomal protein L14 (RPL14/eL14) overexpression represses proliferation, migration, invasion and EMT process in nasopharyngeal carcinoma.Bioengineered · 2021Article
- A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging.Diagnostics (Basel, Switzerland) · 2021Review
- Radiomics for Diagnosis and Radiotherapy of Nasopharyngeal Carcinoma.Frontiers in oncology · 2021Review
- Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups.PloS one · 2021Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundTo develop and validate an MRI-based radiomics nomogram for differentiation of cervical spine ORN from metastasis after radiotherapy (RT) in nasopharyngeal carcinoma (NPC).
methodsA radiomics nomogram was developed in a training set that comprised 46 NPC patients after RT with 95 cervical spine lesions (ORN, n = 51; metastasis, n = 44), and data were gathered from January 2008 to December 2012. 279 radiomics features were extracted from the axial contrast-enhanced T1-weighted image (CE-T1WI). A radiomics signature was created by using the least absolute shrinkage and selection operator (LASSO) algorithm. A nomogram model was developed based on the radiomics scores. The performance of the nomogram was determined in terms of its discrimination, calibration, and clinical utility. An independent validation set contained 25 consecutive patients with 47 lesions (ORN, n = 25; metastasis, n = 22) from January 2013 to December 2015.
resultsThe radiomics signature that comprised eight selected features was significantly associated with the differentiation of cervical spine ORN and metastasis. The nomogram model demonstrated good calibration and discrimination in the training set [AUC, 0.725; 95% confidence interval (CI), 0.622-0.828] and the validation set (AUC, 0.720; 95% CI, 0.573-0.867). The decision curve analysis indicated that the radiomics nomogram was clinically useful.
conclusionsMRI-based radiomics nomogram shows potential value to differentiate cervical spine ORN from metastasis after RT in NPC.
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