Research graph
References from High-Recall Biomedical Language Models for Radiation Oncology Evidence Synthesis: An Integrated Systematic Review of Prediction Models for Radiotherapy-Induced Complications in Nasopharyngeal Carcinoma. Local targets link to admitted publications; unresolved targets remain external evidence.
Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry
10.1136/bmjopen-2016-012545 · 2017 · External reference
Enhancing Text Classification Performance: A Comparative Study of RNN and GRU Architectures with Attention Mechanisms
2024 · External reference
Ensemble pretrained language models to extract biomedical knowledge from literature
10.1093/jamia/ocae061 · 2024 · External reference
BioBERT: A pre-trained biomedical language representation model for biomedical text mining
10.1093/bioinformatics/btz682 · 2020 · External reference
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
10.1145/3458754 · 2022 · External reference
Non-endemic non-keratinizing nasopharyngeal carcinoma: Long-term toxicity following chemoradiation
2025 · External reference
Quality of life of nasopharyngeal carcinoma survivors in Mainland China
10.1007/s11136-006-9113-0 · 2007 · External reference
Prognostic models for radiation-induced complications after radiotherapy in head and neck cancer patients
2025 · External reference
Dosimetric parameters predict radiation-induced temporal lobe necrosis in nasopharyngeal carcinoma patients: A systematic review and meta-analysis
10.1016/j.radonc.2024.110258 · 2024 · External reference
A systematic review of the normal tissue complication probability models and parameters: Head and neck cancers treated with conformal radiotherapy
10.1002/hed.27469 · 2023 · External reference
An analysis of work saved over sampling in the evaluation of automated citation screening in systematic literature reviews
2023 · External reference
Evaluation of semi-automated record screening methods for systematic reviews of prognosis studies and intervention studies
10.1017/rsm.2025.10025 · 2025 · External reference
Automated systematic reviews using machine learning and large language models in clinical practice guideline development: A perspective
10.1002/hkj2.70085 · 2026 · External reference
10.3390/reports9010090
10.3390/reports9010090 · External reference
10.18653/v1/n19-1423
10.18653/v1/n19-1423 · External reference
How should meta-regression analyses be undertaken and interpreted?
10.1002/sim.1187 · 2002 · External reference
Trim and Fill: A Simple Funnel-Plot–Based Method of Testing and Adjusting for Publication Bias in Meta-Analysis
10.1111/j.0006-341x.2000.00455.x · 2000 · External reference
10.3389/fneur.2024.1344324
10.3389/fneur.2024.1344324 · External reference
A MRI-based radiomics model predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma
10.1007/s00330-022-08853-w · 2022 · External reference
Nomogram Based on Clinical and Radiomics Data for Predicting Radiation-induced Temporal Lobe Injury in Patients with Non-metastatic Stage T4 Nasopharyngeal Carcinoma
10.1016/j.clon.2022.07.007 · 2022 · External reference
10.3389/fonc.2024.1394111
10.3389/fonc.2024.1394111 · External reference
10.1186/s12885-025-13906-6
10.1186/s12885-025-13906-6 · External reference
A deep learning-based method for the prediction of temporal lobe injury in patients with nasopharyngeal carcinoma
10.1016/j.ejmp.2024.103362 · 2024 · External reference
Development and validation of a model for temporal lobe necrosis for nasopharyngeal carcinoma patients with intensity modulated radiation therapy
10.1186/s13014-019-1250-z · 2019 · External reference
Normal tissue complication probability (NTCP) models for predicting temporal lobe injury after intensity-modulated radiotherapy in nasopharyngeal carcinoma: A large registry-based retrospective study from China
10.1016/j.radonc.2021.01.008 · 2021 · External reference
Ensemble learning-based radiomics model for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma
10.1016/j.oraloncology.2025.107841 · 2026 · External reference
10.3389/fonc.2023.1168995
10.3389/fonc.2023.1168995 · External reference
A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma
10.1002/hed.27830 · 2024 · External reference
Performance of multi-modality and multi-classifier fusion models for predicting radiation-induced oral mucositis in patients with nasopharyngeal carcinoma
2024 · External reference
10.3389/fonc.2020.596822
10.3389/fonc.2020.596822 · External reference
Predicting Nomogram for Severe Oral Mucositis in Patients with Nasopharyngeal Carcinoma during Intensity-Modulated Radiation Therapy: A Retrospective Cohort Study
10.3390/curroncol30010017 · 2023 · External reference
A multi-center, multi-organ, multi-omic prediction model for treatment-induced severe oral mucositis in nasopharyngeal carcinoma
10.1007/s11547-024-01901-z · 2025 · External reference
Improved Efficacy of a Predictive Model for Swallowing-Induced Breakthrough Pain Based on a Redefined Delineation Method in Locally Advanced Nasopharyngeal Carcinoma
10.1016/j.adro.2024.101690 · 2025 · External reference
Control of dental calculus Prevents severe Radiation-Induced oral mucositis in patients undergoing radiotherapy for nasopharyngeal carcinoma
10.1016/j.radonc.2025.110872 · 2025 · External reference
Dynamic joint prediction model of severe radiation-induced oral mucositis among nasopharyngeal carcinoma: A prospective longitudinal study
10.1016/j.radonc.2025.110993 · 2025 · External reference
Risk factors and prediction models for severe radiation-induced oral mucositis in patients with nasopharyngeal carcinoma undergoing chemoradiotherapy
10.1007/s12672-025-02458-7 · 2025 · External reference
Multivariable model for predicting acute oral mucositis during combined IMRT and chemotherapy for locally advanced nasopharyngeal cancer patients
10.1016/j.oraloncology.2018.10.006 · 2018 · External reference
LASSO NTCP predictors for the incidence of xerostomia in patients with head and neck squamous cell carcinoma and nasopharyngeal carcinoma
10.1038/srep06217 · 2014 · External reference
10.1186/s12903-022-02269-0
10.1186/s12903-022-02269-0 · External reference
A nomogram for predicting late radiation-induced xerostomia among locoregionally advanced nasopharyngeal carcinoma in intensity modulated radiation therapy era
10.18632/aging.203308 · 2021 · External reference
10.3389/fonc.2020.551255
10.3389/fonc.2020.551255 · External reference
A machine learning approach for predicting radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma undergoing tomotherapy
10.1038/s41598-024-59249-3 · 2024 · External reference
A multivariable normal tissue complication probability model for predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma patients in the modern radiotherapy era
10.1093/jrr/rrad091 · 2024 · External reference
Development and validation of a machine learning-based model for predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma
10.1186/s13014-025-02725-5 · 2025 · External reference
10.3389/fonc.2025.1601493
10.3389/fonc.2025.1601493 · External reference
10.3389/fneur.2023.1135978
10.3389/fneur.2023.1135978 · External reference
Predicting late radiation-induced xerostomia in nasopharyngeal carcinoma based on radiomics features extracted from T2WI images of parotids
10.1016/j.radmp.2023.06.002 · 2023 · External reference
10.3389/fonc.2021.633556
10.3389/fonc.2021.633556 · External reference
Predictive value of delta radiomics in xerostomia after chemoradiotherapy in patients with stage III-IV nasopharyngeal carcinoma
10.1186/s13014-024-02417-6 · 2024 · External reference
Development and validation of Prediction models for radiation-induced hypoglossal neuropathy in patients with nasopharyngeal carcinoma
10.1016/j.radonc.2025.110887 · 2025 · External reference
Hearing Loss and Middle Ear Effusion in Nasopharyngeal Carcinoma Following Radiotherapy: Dose–Response Relationship and Normal Tissue Complication Probability Modeling
2025 · External reference
Deep learning dosiomics for the pretreatment prediction of radiation dermatitis in nasopharyngeal carcinoma patients treated with radiotherapy
10.1016/j.radonc.2025.110951 · 2025 · External reference
Development and validation of a normal tissue complication probability model for acquired nasal cavity stenosis and atresia after radical radiotherapy for nasopharyngeal carcinoma
10.1016/j.radonc.2021.03.040 · 2021 · External reference
A predictive model based on the dosimetric parameters of the parotid stem cell region to assess the recovery of radiation-induced xerostomia in long-term survivors of nasopharyngeal carcinoma after radical radiotherapy
10.1016/j.radonc.2025.111163 · 2025 · External reference
A risk model for predicting partial late radiation-induced injuries in T3-4 nasopharyngeal carcinoma based on dosimetric parameters and clinical factors
10.1007/s12672-025-03401-6 · 2025 · External reference
Semi-automated title-abstract screening using natural language processing and machine learning
10.1186/s13643-024-02688-w · 2024 · External reference
Machine learning for screening prioritization in systematic reviews: Comparative performance of Abstrackr and EPPI-Reviewer
10.1186/s13643-020-01324-7 · 2020 · External reference
Cross-validation failure: Small sample sizes lead to large error bars
10.1016/j.neuroimage.2017.06.061 · 2018 · External reference
Machine learning vs. rule-based methods for document classification of electronic health records within mental health care—A systematic literature review
10.1016/j.nlp.2025.100129 · 2025 · External reference
Transforming literature screening: The emerging role of large language models in systematic reviews
10.1073/pnas.2411962122 · 2025 · External reference
A systematic review of the normal tissue complication probability models and parameters: Head and neck cancers treated with conformal radiotherapy
10.1002/hed.27469 · ExternalCitation · doi-reference
A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma
10.1002/hed.27830 · ExternalCitation · doi-reference
Automated systematic reviews using machine learning and large language models in clinical practice guideline development: A perspective
10.1002/hkj2.70085 · ExternalCitation · doi-reference
How should meta-regression analyses be undertaken and interpreted?
10.1002/sim.1187 · ExternalCitation · doi-reference
A MRI-based radiomics model predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma
10.1007/s00330-022-08853-w · ExternalCitation · doi-reference
Quality of life of nasopharyngeal carcinoma survivors in Mainland China
10.1007/s11136-006-9113-0 · ExternalCitation · doi-reference
A multi-center, multi-organ, multi-omic prediction model for treatment-induced severe oral mucositis in nasopharyngeal carcinoma
10.1007/s11547-024-01901-z · ExternalCitation · doi-reference
Risk factors and prediction models for severe radiation-induced oral mucositis in patients with nasopharyngeal carcinoma undergoing chemoradiotherapy
10.1007/s12672-025-02458-7 · ExternalCitation · doi-reference
A risk model for predicting partial late radiation-induced injuries in T3-4 nasopharyngeal carcinoma based on dosimetric parameters and clinical factors
10.1007/s12672-025-03401-6 · ExternalCitation · doi-reference
Improved Efficacy of a Predictive Model for Swallowing-Induced Breakthrough Pain Based on a Redefined Delineation Method in Locally Advanced Nasopharyngeal Carcinoma
10.1016/j.adro.2024.101690 · ExternalCitation · doi-reference
Nomogram Based on Clinical and Radiomics Data for Predicting Radiation-induced Temporal Lobe Injury in Patients with Non-metastatic Stage T4 Nasopharyngeal Carcinoma
10.1016/j.clon.2022.07.007 · ExternalCitation · doi-reference
A deep learning-based method for the prediction of temporal lobe injury in patients with nasopharyngeal carcinoma
10.1016/j.ejmp.2024.103362 · ExternalCitation · doi-reference
Cross-validation failure: Small sample sizes lead to large error bars
10.1016/j.neuroimage.2017.06.061 · ExternalCitation · doi-reference
Machine learning vs. rule-based methods for document classification of electronic health records within mental health care—A systematic literature review
10.1016/j.nlp.2025.100129 · ExternalCitation · doi-reference
Multivariable model for predicting acute oral mucositis during combined IMRT and chemotherapy for locally advanced nasopharyngeal cancer patients
10.1016/j.oraloncology.2018.10.006 · ExternalCitation · doi-reference
Ensemble learning-based radiomics model for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma
10.1016/j.oraloncology.2025.107841 · ExternalCitation · doi-reference
Predicting late radiation-induced xerostomia in nasopharyngeal carcinoma based on radiomics features extracted from T2WI images of parotids
10.1016/j.radmp.2023.06.002 · ExternalCitation · doi-reference
Normal tissue complication probability (NTCP) models for predicting temporal lobe injury after intensity-modulated radiotherapy in nasopharyngeal carcinoma: A large registry-based retrospective study from China
10.1016/j.radonc.2021.01.008 · ExternalCitation · doi-reference
Development and validation of a normal tissue complication probability model for acquired nasal cavity stenosis and atresia after radical radiotherapy for nasopharyngeal carcinoma
10.1016/j.radonc.2021.03.040 · ExternalCitation · doi-reference
Dosimetric parameters predict radiation-induced temporal lobe necrosis in nasopharyngeal carcinoma patients: A systematic review and meta-analysis
10.1016/j.radonc.2024.110258 · ExternalCitation · doi-reference
Control of dental calculus Prevents severe Radiation-Induced oral mucositis in patients undergoing radiotherapy for nasopharyngeal carcinoma
10.1016/j.radonc.2025.110872 · ExternalCitation · doi-reference
Development and validation of Prediction models for radiation-induced hypoglossal neuropathy in patients with nasopharyngeal carcinoma
10.1016/j.radonc.2025.110887 · ExternalCitation · doi-reference
Deep learning dosiomics for the pretreatment prediction of radiation dermatitis in nasopharyngeal carcinoma patients treated with radiotherapy
10.1016/j.radonc.2025.110951 · ExternalCitation · doi-reference
Dynamic joint prediction model of severe radiation-induced oral mucositis among nasopharyngeal carcinoma: A prospective longitudinal study
10.1016/j.radonc.2025.110993 · ExternalCitation · doi-reference
A predictive model based on the dosimetric parameters of the parotid stem cell region to assess the recovery of radiation-induced xerostomia in long-term survivors of nasopharyngeal carcinoma after radical radiotherapy
10.1016/j.radonc.2025.111163 · ExternalCitation · doi-reference
Evaluation of semi-automated record screening methods for systematic reviews of prognosis studies and intervention studies
10.1017/rsm.2025.10025 · ExternalCitation · doi-reference
A machine learning approach for predicting radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma undergoing tomotherapy
10.1038/s41598-024-59249-3 · ExternalCitation · doi-reference
LASSO NTCP predictors for the incidence of xerostomia in patients with head and neck squamous cell carcinoma and nasopharyngeal carcinoma
10.1038/srep06217 · ExternalCitation · doi-reference
Transforming literature screening: The emerging role of large language models in systematic reviews
10.1073/pnas.2411962122 · ExternalCitation · doi-reference
BioBERT: A pre-trained biomedical language representation model for biomedical text mining
10.1093/bioinformatics/btz682 · ExternalCitation · doi-reference
Ensemble pretrained language models to extract biomedical knowledge from literature
10.1093/jamia/ocae061 · ExternalCitation · doi-reference
A multivariable normal tissue complication probability model for predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma patients in the modern radiotherapy era
10.1093/jrr/rrad091 · ExternalCitation · doi-reference
Trim and Fill: A Simple Funnel-Plot–Based Method of Testing and Adjusting for Publication Bias in Meta-Analysis
10.1111/j.0006-341x.2000.00455.x · ExternalCitation · doi-reference
Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry
10.1136/bmjopen-2016-012545 · ExternalCitation · doi-reference
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
10.1145/3458754 · ExternalCitation · doi-reference
10.1186/s12885-025-13906-6
10.1186/s12885-025-13906-6 · ExternalCitation · doi-reference
10.1186/s12903-022-02269-0
10.1186/s12903-022-02269-0 · ExternalCitation · doi-reference
Development and validation of a model for temporal lobe necrosis for nasopharyngeal carcinoma patients with intensity modulated radiation therapy
10.1186/s13014-019-1250-z · ExternalCitation · doi-reference
Predictive value of delta radiomics in xerostomia after chemoradiotherapy in patients with stage III-IV nasopharyngeal carcinoma
10.1186/s13014-024-02417-6 · ExternalCitation · doi-reference
Development and validation of a machine learning-based model for predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma
10.1186/s13014-025-02725-5 · ExternalCitation · doi-reference
Machine learning for screening prioritization in systematic reviews: Comparative performance of Abstrackr and EPPI-Reviewer
10.1186/s13643-020-01324-7 · ExternalCitation · doi-reference
Semi-automated title-abstract screening using natural language processing and machine learning
10.1186/s13643-024-02688-w · ExternalCitation · doi-reference
A nomogram for predicting late radiation-induced xerostomia among locoregionally advanced nasopharyngeal carcinoma in intensity modulated radiation therapy era
10.18632/aging.203308 · ExternalCitation · doi-reference
10.18653/v1/n19-1423
10.18653/v1/n19-1423 · ExternalCitation · doi-reference
10.3389/fneur.2023.1135978
10.3389/fneur.2023.1135978 · ExternalCitation · doi-reference
10.3389/fneur.2024.1344324
10.3389/fneur.2024.1344324 · ExternalCitation · doi-reference
10.3389/fonc.2020.551255
10.3389/fonc.2020.551255 · ExternalCitation · doi-reference
10.3389/fonc.2020.596822
10.3389/fonc.2020.596822 · ExternalCitation · doi-reference
10.3389/fonc.2021.633556
10.3389/fonc.2021.633556 · ExternalCitation · doi-reference
10.3389/fonc.2023.1168995
10.3389/fonc.2023.1168995 · ExternalCitation · doi-reference
10.3389/fonc.2024.1394111
10.3389/fonc.2024.1394111 · ExternalCitation · doi-reference
10.3389/fonc.2025.1601493
10.3389/fonc.2025.1601493 · ExternalCitation · doi-reference
Predicting Nomogram for Severe Oral Mucositis in Patients with Nasopharyngeal Carcinoma during Intensity-Modulated Radiation Therapy: A Retrospective Cohort Study
10.3390/curroncol30010017 · ExternalCitation · doi-reference
10.3390/reports9010090
10.3390/reports9010090 · ExternalCitation · doi-reference
