A screening model for oral cancer using risk scores

Development and validation

Sreevidya Krishna Rao, Gloria C. Mejia, Richard M. Logan, Muralidhar Kulkarni, Veena Kamath, Donald J. Fernandes, Satadru Ray, Kaye Roberts-Thomson

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Objective A study was conducted to develop and validate a screening model using risk scores to identify individuals at high risk for developing oral cancer in an Indian population. Methods Life-course data collected from a multicentre case-control study in India were used. Interview was conducted to collect information on predictors limited to the time before the onset of symptoms or cancer diagnosis. Predictors included statistically significant risk factors in the multivariable model. A risk score for each predictor was derived from respective odds ratios (OR). Discrimination of the final model, risk scores and various risk score cut-offs was examined using the c statistic. The optimal cut-off was determined as the one with good area under curve (AUC) and high sensitivity. Predictive ability of the regression model and cut-off risk score was determined by calculating sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Models were validated from a bootstrap sample. Results Smoking, chewing quid and/or tobacco, alcohol, a family history of upper aero-digestive tract cancer, diet and oral hygiene behaviour were the predictors. Risk scores ranged from 0 to 28. Area under the receiver operating characteristic (ROC) curve for risk scores was good (0.866). The sensitivity (0.928) and negative predictive value (0.927) were high, while specificity (0.603) and positive predictive value (0.607) were low for a risk score cut-off of 6. Conclusion A risk score model to screen for individuals with high risk of oral cancer with satisfactory predictive ability was developed in the Indian population. Validation of the model in other populations is necessary before it can be recommended to identify subgroups of the population to be directed towards more extensive clinical evaluation.

Original languageEnglish
Pages (from-to)76-84
Number of pages9
JournalCommunity Dentistry and Oral Epidemiology
Volume44
Issue number1
DOIs
Publication statusPublished - 01-02-2016

Fingerprint

Mouth Neoplasms
Aptitude
Population
Oral Hygiene
Mastication
ROC Curve
Area Under Curve
Tobacco
Gastrointestinal Tract
Case-Control Studies
India
Smoking
Odds Ratio
Alcohols
Interviews
Diet
Sensitivity and Specificity

All Science Journal Classification (ASJC) codes

  • Dentistry(all)
  • Public Health, Environmental and Occupational Health

Cite this

Krishna Rao, Sreevidya ; Mejia, Gloria C. ; Logan, Richard M. ; Kulkarni, Muralidhar ; Kamath, Veena ; Fernandes, Donald J. ; Ray, Satadru ; Roberts-Thomson, Kaye. / A screening model for oral cancer using risk scores : Development and validation. In: Community Dentistry and Oral Epidemiology. 2016 ; Vol. 44, No. 1. pp. 76-84.
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A screening model for oral cancer using risk scores : Development and validation. / Krishna Rao, Sreevidya; Mejia, Gloria C.; Logan, Richard M.; Kulkarni, Muralidhar; Kamath, Veena; Fernandes, Donald J.; Ray, Satadru; Roberts-Thomson, Kaye.

In: Community Dentistry and Oral Epidemiology, Vol. 44, No. 1, 01.02.2016, p. 76-84.

Research output: Contribution to journalArticle

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T1 - A screening model for oral cancer using risk scores

T2 - Development and validation

AU - Krishna Rao, Sreevidya

AU - Mejia, Gloria C.

AU - Logan, Richard M.

AU - Kulkarni, Muralidhar

AU - Kamath, Veena

AU - Fernandes, Donald J.

AU - Ray, Satadru

AU - Roberts-Thomson, Kaye

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N2 - Objective A study was conducted to develop and validate a screening model using risk scores to identify individuals at high risk for developing oral cancer in an Indian population. Methods Life-course data collected from a multicentre case-control study in India were used. Interview was conducted to collect information on predictors limited to the time before the onset of symptoms or cancer diagnosis. Predictors included statistically significant risk factors in the multivariable model. A risk score for each predictor was derived from respective odds ratios (OR). Discrimination of the final model, risk scores and various risk score cut-offs was examined using the c statistic. The optimal cut-off was determined as the one with good area under curve (AUC) and high sensitivity. Predictive ability of the regression model and cut-off risk score was determined by calculating sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Models were validated from a bootstrap sample. Results Smoking, chewing quid and/or tobacco, alcohol, a family history of upper aero-digestive tract cancer, diet and oral hygiene behaviour were the predictors. Risk scores ranged from 0 to 28. Area under the receiver operating characteristic (ROC) curve for risk scores was good (0.866). The sensitivity (0.928) and negative predictive value (0.927) were high, while specificity (0.603) and positive predictive value (0.607) were low for a risk score cut-off of 6. Conclusion A risk score model to screen for individuals with high risk of oral cancer with satisfactory predictive ability was developed in the Indian population. Validation of the model in other populations is necessary before it can be recommended to identify subgroups of the population to be directed towards more extensive clinical evaluation.

AB - Objective A study was conducted to develop and validate a screening model using risk scores to identify individuals at high risk for developing oral cancer in an Indian population. Methods Life-course data collected from a multicentre case-control study in India were used. Interview was conducted to collect information on predictors limited to the time before the onset of symptoms or cancer diagnosis. Predictors included statistically significant risk factors in the multivariable model. A risk score for each predictor was derived from respective odds ratios (OR). Discrimination of the final model, risk scores and various risk score cut-offs was examined using the c statistic. The optimal cut-off was determined as the one with good area under curve (AUC) and high sensitivity. Predictive ability of the regression model and cut-off risk score was determined by calculating sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Models were validated from a bootstrap sample. Results Smoking, chewing quid and/or tobacco, alcohol, a family history of upper aero-digestive tract cancer, diet and oral hygiene behaviour were the predictors. Risk scores ranged from 0 to 28. Area under the receiver operating characteristic (ROC) curve for risk scores was good (0.866). The sensitivity (0.928) and negative predictive value (0.927) were high, while specificity (0.603) and positive predictive value (0.607) were low for a risk score cut-off of 6. Conclusion A risk score model to screen for individuals with high risk of oral cancer with satisfactory predictive ability was developed in the Indian population. Validation of the model in other populations is necessary before it can be recommended to identify subgroups of the population to be directed towards more extensive clinical evaluation.

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