# Performance of Automated Oral Cancer Screening Algorithm in Tobacco Users vs. Non-Tobacco Users
## 4 Discussion  
The primary goal of this study was to gain a better understanding of the significance of tobacco smoking in the performance of an imaging- and risk-factor-based screening algorithm for OC risk. To date, the algorithms that are under development all use the same algorithm pathway and determinants in all subjects undergoing OC risk screening, regardless of the extrinsic factors which may mask or modify cancer risk-related changes in the visual appearance of the oral soft tissues. Our concerns about the impact of the obvious visual changes to the appearance and texture of the oral mucosa of tobacco smokers provided impetus for this study. Because our prototype screening algorithm combines image-based evaluations with clinical signs and symptoms and risk factors, we were concerned that smoking-related mucosal changes might reduce the accuracy of the screening algorithm overall. However, the study findings did not support this premise, actually demonstrating greater algorithm-based screening accuracy in smokers than in non-smokers. A second impetus for this study was the entire topic of how to address \"weighting\" each individual variable in the screening algorithm. For example, how much importance (\"weight\") does the algorithm attribute to tobacco use vs. alcohol use as an indicator of oral risk, and how should the algorithm-based classification of risk levels be modified when a subject indulges in both behaviors, which potentiate each other to a degree that is not just additive? It is only if each variable is assigned the appropriate level of significance within the algorithm architecture that the maximum amount of information can be mined from the data and the images, to achieve the most accurate screening outcome possible. Because tobacco smoking is such a powerful predictor of OC risk, an optimized screening algorithm will need to reflect an appropriate \"weighting\" of this risk factor without overwhelming the additional significance of other risk factors. From the results of this first study, it is apparent that our OC risk screening algorithm achieved considerably higher accuracy and agreement with the specialist screening outcome in the smoking group vs the non-smoking group. These findings suggest that the weighting of tobacco smoking as a variable within our first-generation algorithm needs to be fine-tuned to prevent this variable from canceling out useful information obtained from other variables. The current level of weighting for tobacco use is clearly too high, whereas the weighting of the imaging-based criteria needs to be increased. Similarly, future work is needed to allocate the appropriate level of significance to other non-tobacco-smoking-related risk factors such as ulceration, texture, and color change within the oral cavity, high-risk behaviors, or a concerning health history. \begin{table}
\begin{tabular}{c c c c c} \hline  & **VALUE** & **SE** & **LOWER CI** & **UPPER CI** \\ \hline \multirow{8}{*}{**Non-Smoker**} & Sensitivity & 0.622 & 0.022 & 0.600 & 0.644 \\ \cline{2-5}  & Specificity & 0.835 & 0.023 & 0.812 & 0.858 \\ \cline{2-5}  & False Positive Rate & 0.165 & 0.024 & 0.142 & 0.188 \\ \cline{2-5}  & False Negative Rate & 0.378 & 0.022 & 0.356 & 0.400 \\ \cline{2-5}  & Agreement with Specialist & 0.733 & 0.010 & 0.726 & 0.742 \\ \cline{2-5}  & Positive predictive values & 0.774 & 0.019 & 0.755 & 0.793 \\ \cline{2-5}  & Negative predictive values & 0.710 & 0.012 & 0.700 & 0.720 \\ \hline \multirow{8}{*}{**Tobacco-Smoker**} & Sensitivity & 0.800 & 0.033 & 0.768 & 0.832 \\ \cline{2-5}  & Specificity & 0.864 & 0.041 & 0.824 & 0.904 \\ \cline{2-5}  & False Positive Rate & 0.135 & 0.040 & 0.0959 & 0.174 \\ \cline{2-5}  & False Negative Rate & 0.200 & 0.033 & 0.168 & 0.232 \\ \cline{2-5}  & Agreement with Specialist & 0.833 & 0.026 & 0.807 & 0.858 \\ \cline{2-5}  & Positive predictive values & 0.861 & 0.037 & 0.825 & 0.897 \\ \cline{2-5}  & Negative predictive values & 0.805 & 0.026 & 0.780 & 0.830 \\ \hline \end{tabular}
\end{table}