# Machine-Learning Applications in Oral Cancer: A Systematic Review
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Clinical Context** & **Applied Algorithm** & **Input Features** & **n** & **ROB** & **Concluding Remarks** \\ \hline
[50] & 2015 & Prognosis & LR & Gene expression profiles & 486 & + & The proposed network-driven integrative analytical approach can identify multiple genes significantly related to an OSCC stage \\ \hline
[51] & 2019 & Prognosis & SVM, GB, LR, DTREE & Clinicopathologic data & 782 & + & Machine learning improves prediction of pathologic nodal metastasis in patients with clinical T1-2N0 OSCC compared to methods based on DOI \\ \hline
[52] & 2020 & Prognosis & PLS-DA, OPLS-DA & Spectral data & 180 & + & Spectral data on 180 tissues comprising tumor, margin, and bed from 43 OSCC patients were used to perform machine-learning models to identify malignancy status \\ \hline
[53] & 2018 & Therapy and quality of life & DFG & \
\begin{tabular}{c} Family gene, \\ compound, bile \\ mutation, GWAS \\ phenotype, OMIM \\ rhotype, kidney \\ mutation, and oral \\ \end{tabular} & 400 & + &
\begin{tabular}{c} Algorithm Medusa in parallel with binary \\ classification was used in order to find \\ potential compounds to inhibit oral cancer \\ \end{tabular} \\ \hline
[54] & 2013 & Prognosis & ANFIS, ANN, SVM, LR & Clinicopathologic and genomic data & 31 & + & The results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers \\ \hline
[55] & 2006 & \begin{tabular}{c} Potentially \\ malignant oral \\ lesions (pre-cancer) \\ \end{tabular} & WNN & \
\begin{tabular}{c} TEM images of \\ collagen fibers \\ from oral \\ subepithelial \\ region \\ \end{tabular} & 145 & + &
\begin{tabular}{c} The trained network could classify normal \\ fibers from less advanced and advanced \\ stages of OSF successfully \\ \end{tabular} \\ \hline
[56] & 1996 & \
\begin{tabular}{c} Diagnosis and \\ Prevention \\ \end{tabular} & ANN & Intra-oral smears & 348 & + &
\begin{tabular}{c} A neural network differentiated between \\ normal/non-dyslysmic mucosa \\ dysplastic/malignant mucosa \\ \end{tabular} \\ \hline
[57] & 2011 & Prognosis & LR, ANN & Medical history & 211 &? & Suggests the importance of routinely investigating PTI in OSCCs as useful marker of tumor behavior and prognosis \\ \hline
[58] & 2003 & \
\begin{tabular}{c} Diagnosis and \\ Prevention \\ \end{tabular} & PLS, ANN & Spectral data & 97 & + &
\begin{tabular}{c} The PLS-ANN classification algorithm based \\ on autofluorescence spectroscopy at 330 nm \\ excitation is useful for in vivo diagnosis of \\ OSF, as well as oral pre-malignant \\ malignant lesions \\ \end{tabular} \\ \hline
[59] & 2020 & \begin{tabular}{c} Potentially \\ malignant oral \\ lesions (pre-cancer) \\ \end{tabular} & KNN & \
\begin{tabular}{c} Demographics, \\ lesion \\ characteristics, and \\ cell phenotypes \\ \end{tabular} & 999 & + &
\begin{tabular}{c} Cytopathology tools represent a practical \\ solution for rapid PMOL assessment, with \\ the potential to facilitate screening and \\ longitudinal monitoring in primary, \\ secondary, and tertiary clinical care settings \\ \end{tabular} \\ \hline
[60] & 2018 & \
\begin{tabular}{c} Potentially \\ malignant oral \\ lesions (pre-cancer) \\ \end{tabular} & CNN & Images oral cavity & 170 & + &
\begin{tabular}{c} CNN was implemented in the cloud and \\ used for automatic image analysis and \\ classification of pairs of images into \\ \"suspicious\" and \"non-suspect\" \\ \end{tabular} \\ \hline
[61] & 2017 & \begin{tabular}{c} Potentially \\ malignant oral \\ lesions (pre-cancer) \\ \end{tabular} & SVM & \
\begin{tabular}{c} DIC images of oral \\ exfoliative cells \\ \end{tabular} & 119 & + &
\begin{tabular}{c} The selected morphological and textural \\ features of epithelial cells are compared with \\ the non-smoker (\(v\)e control group) group \\ and clinically diagnosed pre-cancer patients \\ (+ve control group) using SVM classifier \\ \end{tabular} \\ \hline
[62] & 2020 & Prognosis & \begin{tabular}{c} DTREE, ANN, \\ NB, KNN \\ \end{tabular} & \
\begin{tabular}{c} Contrast-enhanced \\ CT images \\ \end{tabular} & 40 & + &
\begin{tabular}{c} A radiomic ML approach applied to PTLs is \\ able to predict TC and NS in patients with \\ OC and OP SCC \\ \end{tabular} \\ \hline
[63] & 2006 & \
\begin{tabular}{c} Diagnosis and \\ Prevention \\ \end{tabular} & ANN, PCA & Spectral data & 143 & + &
\begin{tabular}{c} Spectral analyses were used to classify and \\ discriminate among normal, pre-malignant, \\ and malignant conditions on oral tissue. \\ Sensitivity and specificity gave results of \(>\) \\ 92\% in PCA and ANN \\ \end{tabular} \\ \hline
[64] & 2017 & \begin{tabular}{c} Potentially \\ malignant oral \\ lesions (pre-cancer) \\ \end{tabular} & \
\begin{tabular}{c} Exoliative \\ cytology, \\ histology, \\ KNN \\ and clinical \\ follow-up data \\ \end{tabular} & 364 &? &
\begin{tabular}{c} Developed an exfoliative cytology-based \\ method for quantitative prediction of cancer \\ risk in patients with oral leukoplakia \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}