ArticleBMC cancer2020
Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.
Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 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
- Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist.Frontiers in oncology · 2021Pooled it
- Quantitative study of the changes in brain white matter before and after radiotherapy by applying multi-sequence MR radiomics.BMC medical imaging · 2022Trial
- Multimodal deep learning-based prediction of radiation-induced temporal lobe injury via anatomical imaging and spatial dose: A multicenter study.BMC medicine · 2026Article
- Use of gadolinium-based contrast agents in head and neck cancer diagnosis, staging, and monitoring: current applications and future perspectives.European radiology · 2026Review
- Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- Composite quantitative structural magnetic resonance imaging-based risk scoring model for predicting radiation-induced temporal lobe necrosis in nasopharyngeal carcinoma: a novel risk stratification model.Radiation oncology (London, England) · 2025Article
- Automated deep learning-assisted early detection of radiation-induced temporal lobe injury on MRI: a multicenter retrospective analysis.European radiology · 2025Article
- A risk model for predicting partial late radiation-induced injuries in T3-4 nasopharyngeal carcinoma based on dosimetric parameters and clinical factors.Discover oncology · 2025Article
- Comparison of the effects of IMRT and IMPT on MRI features of normal brain tissue: a retrospective study.BMC medical imaging · 2025Article
- 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
- Enhancement of the nontumor component in newly diagnosed glioblastoma as a more accurate predictor of local recurrence location: a multicenter study.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence in radiation therapy: from imaging to delivery-a comprehensive review.BJR artificial intelligence · 2025Review
- Construction of a Clinical Prediction Model for Complications After Femoral Head Replacement Surgery.Journal of clinical medicine research · 2024Article
- Review
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models.European radiology · 2024Article
- A bibliometric analysis of radiation-induced brain injury: a research of the literature from 1998 to 2023.Discover oncology · 2024Article
- Review
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundEarly radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI.
methodsWe retrospectively included a total of 242 NPC patients who underwent regular follow-up magnetic resonance imaging (MRI) examinations, including contrast-enhanced T1-weighted and T2-weighted imaging. For each MRI sequence, four non-texture and 10,320 texture features were extracted from medial temporal lobe, gray matter, and white matter, respectively. The relief and 0.632 + bootstrap algorithms were applied for initial and subsequent feature selection, respectively. Random forest method was used to construct the prediction model. Three models, 1, 2 and 3, were developed for predicting the results of the last three follow-up MRI scans at different times before RTLI onset, respectively. The area under the curve (AUC) was used to evaluate the performance of models.
resultsOf the 242 patients, 171 (70.7%) were men, and the mean age of all the patients was 48.5 ± 10.4 years. The median follow-up and latency from radiotherapy until RTLI were 46 and 41 months, respectively. In the testing cohort, models 1, 2, and 3, with 20 texture features derived from the medial temporal lobe, yielded mean AUCs of 0.830 (95% CI: 0.823-0.837), 0.773 (95% CI: 0.763-0.782), and 0.716 (95% CI: 0.699-0.733), respectively.
conclusionThe three developed radiomic models can dynamically predict RTLI in advance, enabling early detection and allowing clinicians to take preventive measures to stop or slow down the deterioration of RTLI.
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