## 4 Discussion  
Here, we report LC-MS/MS metabolomic analyses aiming to improve the diagnosis of TCs among ITNs. We show that this approach enabled the identification of new metabolomic signatures for more accurate TN diagnosis. We demonstrated that in the case of ITNs, a metabolomic signature using liquid-based cytology could distinguish benign from malignant lesions. Thus, the LC-MS/MS-based metabolomic approach offers a new ancillary technique for the evaluation of ITNs, providing a rapid and cost-efficient alternative to the currently available molecular tests [9]. In the era of the search for a therapeutic de-escalation of TC, the metabolomic approach is an alternative option to reduce the number of unnecessary surgeries and the global medico-economic impact of ITN management. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{Ion}} & \multirow{2}{*}{Row} & \multirow{2}{*}{RT} & \multicolumn{2}{c}{Row} & \multirow{2}{*}{Molecular} & \multirow{2}{*}{SAE} & \multirow{2}{*}{B/M} \\  & & & & & & & \\ \multicolumn{1}{c}{Mode} & & & & & & & \\ \hline Neg & 434.872 & 1.41 & - & - & C11H3O13P3 & 0.9726 & 7.002 \\ Pos & 307.083 & 2.56 & (-)-Epigallocatechin & 0038361 & C15H14O7 & 0.7123 & 4.831 \\ Pos & 229.041 & 10.88 & Indolymethylthiohydroxinate & - & C10H10N2 OS & 0.3305 & 3.067 \\ Pos & 217.08 & 2.56 & - & - & - & 0.3208 & 3.125 \\ Pos & 229.954 & 2.01 & - & - & - & 0.2623 & 0.554 \\ Pos & 247.965 & 2.02 & - & - & - & 0.1823 & 0.565 \\ Pos & 257.949 & 2 & - & - & - & 0.1790 & 0.573 \\ Pos & 311.9 & 1.78 & - & - & - & 0.1590 & 0.602 \\ Pos & 325.942 & 2 & - & - & - & 0.1495 & 0.590 \\ Pos & 210.933 & 1.7 & - & - & - & 0.1353 & 0.686 \\ Pos & 253.91 & 1.51 & - & - & - & 0.1336 & 0.612 \\ Pos & 162.907 & 1.49 & Phosphoroselenoic acid & 0003840 & H3O3 PSe & 0.1329 & 0.682 \\ Pos & 225.914 & 1.51 & - & - & - & 0.1154 & 0.634 \\ Neg & 108.901 & 1.65 & - & - & - & 0.0247 & 1.731 \\ Pos & 250.939 & 1.41 & - & - & - & \(-\)0.0289 & 5.156 \\ \hline \hline \end{tabular} Abbreviations: B/M, Benign over malignant ratio; HMDB, Human Metabolome Database; Pos, positive mode; Neg, negative mode; RT, Retention time; SAE, Supervised autoencoder.  
\end{table} 
Table 4: Importance and identity of the 15 top metabolites. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{Methods Performance}} & \multirow{2}{*}{Fold 1} & \multirow{2}{*}{Fold 2} & \multirow{2}{*}{Fold 3} & \multirow{2}{*}{Fold 4} & \multirow{2}{*}{Mean} & \multirow{2}{*}{SD} \\ \hline \multirow{4}{*}{Support Vector Machines} & AUC & 0.833 & 0.771 & 0.938 & 0.958 & 0.875 & 0.088 \\  & Accuracy & 0.895 & 0.842 & 0.947 & 0.947 & 0.908 & 0.050 \\  & Precision & 0.833 & 0.771 & 0.938 & 0.958 & 0.875 & 0.088 \\  & Recall & 0.933 & 0.717 & 0.958 & 0.938 & 0.886 & 0.114 \\  & F1 score & 0.864 & 0.737 & 0.945 & 0.945 & 0.873 & 0.098 \\  & Time elapsed & 0.025 & 0.029 & 0.028 & 0.029 & 0.028 & - \\ \hline \multirow{4}{*}{Partial Least Squares- Discriminant Analysis} & AUC & 0.833 & 0.941 & 1 & 0.944 & 0.930 & 0.070 \\  & Accuracy & 0.895 & 0.895 & 1 & 0.947 & 0.934 & 0.050 \\  & Precision & 0.833 & 0.941 & 1 & 0.944 & 0.930 & 0.070 \\  & Recall & 0.933 & 0.750 & 1 & 0.955 & 0.909 & 0.110 \\  & F1 score & 0.864 & 0.802 & 1 & 0.947 & 0.903 & 0.088 \\  & Time elapsed & 0.004 & 0.001 & 0.001 & 0.001 & 0.002 & - \\  & sil\_plsda & 0.396 & 0.435 & 0.407 & 0.381 & 0.405 & 0.023 \\ \hline \multirow{4}{*}{Random Forest} & AUC & 1 & 0.969 & 0.887 & 0.958 & 0.953 & 0.048 \\  & Accuracy & 1 & 0.947 & 0.895 & 0.947 & 0.947 & 0.043 \\  & Precision & 1 & 0.969 & 0.887 & 0.958 & 0.953 & 0.048 \\  & Recall & 1 & 0.875 & 0.887 & 0.938 & 0.925 & 0.057 \\  & F1 score & 1 & 0.912 & 0.887 & 0.945 & 0.936 & 0.049 \\  & TimeElapsed & 0.362 & 0.343 & 0.344 & 0.342 & 0.348 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of the machine learning methods. Thyroidectomy is the main component in the management of thyroid cancer.