Risk Prediction Models for Oral Cancer: A Systematic Review
In the last 30 years, there has been an increasing incidence of oral cancer worldwide. Earlier detection of oral cancer has been shown to improve survival rates. However, given the relatively low prevalence of this disease, population-wide screening is likely to be inefficient. Risk prediction model...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/16/3/617 |
| _version_ | 1797318951215038464 |
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| author | Aufia Espressivo Z. Sienna Pan Juliet A. Usher-Smith Hannah Harrison |
| author_facet | Aufia Espressivo Z. Sienna Pan Juliet A. Usher-Smith Hannah Harrison |
| author_sort | Aufia Espressivo |
| collection | DOAJ |
| description | In the last 30 years, there has been an increasing incidence of oral cancer worldwide. Earlier detection of oral cancer has been shown to improve survival rates. However, given the relatively low prevalence of this disease, population-wide screening is likely to be inefficient. Risk prediction models could be used to target screening to those at highest risk or to select individuals for preventative interventions. This review (a) systematically identified published models that predict the development of oral cancer and are suitable for use in the general population and (b) described and compared the identified models, focusing on their development, including risk factors, performance and applicability to risk-stratified screening. A search was carried out in November 2022 in the Medline, Embase and Cochrane Library databases to identify primary research papers that report the development or validation of models predicting the risk of developing oral cancer (cancers of the oral cavity or oropharynx). The PROBAST tool was used to evaluate the risk of bias in the identified studies and the applicability of the models they describe. The search identified 11,222 articles, of which 14 studies (describing 23 models), satisfied the eligibility criteria of this review. The most commonly included risk factors were age (<i>n</i> = 20), alcohol consumption (<i>n</i> = 18) and smoking (<i>n</i> = 17). Six of the included models incorporated genetic information and three used biomarkers as predictors. Including information on human papillomavirus status was shown to improve model performance; however, this was only included in a small number of models. Most of the identified models (<i>n</i> = 13) showed good or excellent discrimination (AUROC > 0.7). Only fourteen models had been validated and only two of these validations were carried out in populations distinct from the model development population (external validation). Conclusions: Several risk prediction models have been identified that could be used to identify individuals at the highest risk of oral cancer within the context of screening programmes. However, external validation of these models in the target population is required, and, subsequently, an assessment of the feasibility of implementation with a risk-stratified screening programme for oral cancer. |
| first_indexed | 2024-03-08T03:59:49Z |
| format | Article |
| id | doaj.art-9e284abae64942238ddc0c9fd2f44c4d |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-08T03:59:49Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-9e284abae64942238ddc0c9fd2f44c4d2024-02-09T15:09:20ZengMDPI AGCancers2072-66942024-01-0116361710.3390/cancers16030617Risk Prediction Models for Oral Cancer: A Systematic ReviewAufia Espressivo0Z. Sienna Pan1Juliet A. Usher-Smith2Hannah Harrison3Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UKDepartment of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UKDepartment of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UKDepartment of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UKIn the last 30 years, there has been an increasing incidence of oral cancer worldwide. Earlier detection of oral cancer has been shown to improve survival rates. However, given the relatively low prevalence of this disease, population-wide screening is likely to be inefficient. Risk prediction models could be used to target screening to those at highest risk or to select individuals for preventative interventions. This review (a) systematically identified published models that predict the development of oral cancer and are suitable for use in the general population and (b) described and compared the identified models, focusing on their development, including risk factors, performance and applicability to risk-stratified screening. A search was carried out in November 2022 in the Medline, Embase and Cochrane Library databases to identify primary research papers that report the development or validation of models predicting the risk of developing oral cancer (cancers of the oral cavity or oropharynx). The PROBAST tool was used to evaluate the risk of bias in the identified studies and the applicability of the models they describe. The search identified 11,222 articles, of which 14 studies (describing 23 models), satisfied the eligibility criteria of this review. The most commonly included risk factors were age (<i>n</i> = 20), alcohol consumption (<i>n</i> = 18) and smoking (<i>n</i> = 17). Six of the included models incorporated genetic information and three used biomarkers as predictors. Including information on human papillomavirus status was shown to improve model performance; however, this was only included in a small number of models. Most of the identified models (<i>n</i> = 13) showed good or excellent discrimination (AUROC > 0.7). Only fourteen models had been validated and only two of these validations were carried out in populations distinct from the model development population (external validation). Conclusions: Several risk prediction models have been identified that could be used to identify individuals at the highest risk of oral cancer within the context of screening programmes. However, external validation of these models in the target population is required, and, subsequently, an assessment of the feasibility of implementation with a risk-stratified screening programme for oral cancer.https://www.mdpi.com/2072-6694/16/3/617oral cancerrisk predictionrisk modelsscreeningprimary care |
| spellingShingle | Aufia Espressivo Z. Sienna Pan Juliet A. Usher-Smith Hannah Harrison Risk Prediction Models for Oral Cancer: A Systematic Review Cancers oral cancer risk prediction risk models screening primary care |
| title | Risk Prediction Models for Oral Cancer: A Systematic Review |
| title_full | Risk Prediction Models for Oral Cancer: A Systematic Review |
| title_fullStr | Risk Prediction Models for Oral Cancer: A Systematic Review |
| title_full_unstemmed | Risk Prediction Models for Oral Cancer: A Systematic Review |
| title_short | Risk Prediction Models for Oral Cancer: A Systematic Review |
| title_sort | risk prediction models for oral cancer a systematic review |
| topic | oral cancer risk prediction risk models screening primary care |
| url | https://www.mdpi.com/2072-6694/16/3/617 |
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