Risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer: a systematic review and meta-analysis
BackgroundPredictive models can identify patients at risk and thus enable personalized interventions. Despite the increasing number of prediction models used to predict the risk of dysphagia after radiotherapy in patients with head and neck cancer (HNC), there is still uncertainty about the effectiv...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1502404/full |
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| author | You Pu Jin Yang Lian Shui Qianlong Tang Xianqin Zhang Guangguo Liu |
| author_facet | You Pu Jin Yang Lian Shui Qianlong Tang Xianqin Zhang Guangguo Liu |
| author_sort | You Pu |
| collection | DOAJ |
| description | BackgroundPredictive models can identify patients at risk and thus enable personalized interventions. Despite the increasing number of prediction models used to predict the risk of dysphagia after radiotherapy in patients with head and neck cancer (HNC), there is still uncertainty about the effectiveness of these models in clinical practice and about the quality and applicability of future studies. The aim of this study was to systematically evaluate and analyze all predictive models used to predict dysphagia in patients with HNC after radiotherapy.MethodsPubMed, Cochrane Library, EMbase and Web of Science databases were searched from database establishment to August 31, 2024. Data from selected studies were extracted using predefined tables and the quality of the predictive modelling studies was assessed using the PROBAST tool. Meta-analysis of the predictive performance of the model was performed using the “metafor” package in R software.ResultsTwenty-five models predicting the risk of dysphagia after radiotherapy in patients with HNC were included, covering a total of 8,024 patients. Common predictors include mean dose to pharyngeal constrictor muscles, treatment setting, and tumor site. Of these models, most were constructed based on logistic regression, while only two studies used machine learning methods. The area under the receiver operating characteristic curve (AUC) reported values for these models ranged from 0.57 to 0.909, with 13 studies having a combined AUC value of 0.78 (95% CI: 0.74-0.81). All studies showed a high risk of bias as assessed by the PROBAST tool.ConclusionMost of the published prediction models in this study have good discrimination. However, all studies were considered to have a high risk of bias based on PROBAST assessments. Future studies should focus on large sample size and rigorously designed multicenter external validation to improve the reliability and clinical applicability of prediction models for dysphagia after radiotherapy for HNC.Systematic review registrationhttps://www.crd.york.ac.uk/prospero, identifier CRD42024587252. |
| format | Article |
| id | doaj-art-eaad980a33e24301b1e58bb3cd50d2c6 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-eaad980a33e24301b1e58bb3cd50d2c62025-02-07T06:49:22ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-02-011510.3389/fonc.2025.15024041502404Risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer: a systematic review and meta-analysisYou Pu0Jin Yang1Lian Shui2Qianlong Tang3Xianqin Zhang4Guangguo Liu5Department of Oncology, Sichuan Mianyang 404 Hospital, Mianyang, Sichuan, ChinaDepartment of Cardiology, Sichuan Mianyang 404 Hospital, Mianyang, Sichuan, ChinaDepartment of Oncology, Sichuan Mianyang 404 Hospital, Mianyang, Sichuan, ChinaDepartment of Oncology, Sichuan Mianyang 404 Hospital, Mianyang, Sichuan, ChinaSchool of Basic Medical Sciences, Chengdu Medical College, Chengdu, Sichuan, ChinaDepartment of Oncology, Sichuan Mianyang 404 Hospital, Mianyang, Sichuan, ChinaBackgroundPredictive models can identify patients at risk and thus enable personalized interventions. Despite the increasing number of prediction models used to predict the risk of dysphagia after radiotherapy in patients with head and neck cancer (HNC), there is still uncertainty about the effectiveness of these models in clinical practice and about the quality and applicability of future studies. The aim of this study was to systematically evaluate and analyze all predictive models used to predict dysphagia in patients with HNC after radiotherapy.MethodsPubMed, Cochrane Library, EMbase and Web of Science databases were searched from database establishment to August 31, 2024. Data from selected studies were extracted using predefined tables and the quality of the predictive modelling studies was assessed using the PROBAST tool. Meta-analysis of the predictive performance of the model was performed using the “metafor” package in R software.ResultsTwenty-five models predicting the risk of dysphagia after radiotherapy in patients with HNC were included, covering a total of 8,024 patients. Common predictors include mean dose to pharyngeal constrictor muscles, treatment setting, and tumor site. Of these models, most were constructed based on logistic regression, while only two studies used machine learning methods. The area under the receiver operating characteristic curve (AUC) reported values for these models ranged from 0.57 to 0.909, with 13 studies having a combined AUC value of 0.78 (95% CI: 0.74-0.81). All studies showed a high risk of bias as assessed by the PROBAST tool.ConclusionMost of the published prediction models in this study have good discrimination. However, all studies were considered to have a high risk of bias based on PROBAST assessments. Future studies should focus on large sample size and rigorously designed multicenter external validation to improve the reliability and clinical applicability of prediction models for dysphagia after radiotherapy for HNC.Systematic review registrationhttps://www.crd.york.ac.uk/prospero, identifier CRD42024587252.https://www.frontiersin.org/articles/10.3389/fonc.2025.1502404/fulldysphagiahead and neck cancerradiotherapypredictive modelsmeta-analysis |
| spellingShingle | You Pu Jin Yang Lian Shui Qianlong Tang Xianqin Zhang Guangguo Liu Risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer: a systematic review and meta-analysis Frontiers in Oncology dysphagia head and neck cancer radiotherapy predictive models meta-analysis |
| title | Risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer: a systematic review and meta-analysis |
| title_full | Risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer: a systematic review and meta-analysis |
| title_fullStr | Risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer: a systematic review and meta-analysis |
| title_full_unstemmed | Risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer: a systematic review and meta-analysis |
| title_short | Risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer: a systematic review and meta-analysis |
| title_sort | risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer a systematic review and meta analysis |
| topic | dysphagia head and neck cancer radiotherapy predictive models meta-analysis |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1502404/full |
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