# A Machine Learning and Radiomics Approach in Lung Cancer for Predicting Histological Subtype
## 3 Methods
### Classification
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Name**} & \multicolumn{3}{c}{**Mean**} & \multicolumn{3}{c}{**Std**} & \multicolumn{3}{c}{**Median**} & \multicolumn{3}{c}{**IQR**} & \multicolumn{1}{c}{\(p\)**-Value**} \\ \cline{2-9}  & **LUAD** & **Other** & **LUAD** & **Other** & **LUAD** & **Other** & **LUAD** & **Other** & **BH** \\ \hline O\_glcm\_Autocorrelation & \(-\)0.2449 & 0.3470 & 0.8658 & 1.0672 & \(-\)0.3783 & 0.3439 & 1.2597 & 1.6090 & 0.0011 * \\ \hline O\_glcm\_CP & \(-\)0.1698 & 0.2334 & 0.6434 & 1.3142 & \(-\)0.3974 & \(-\)0.1727 & 0.6376 & 1.0980 & 0.1406 \\ \hline O\_glsm\_SALGLE & 0.1244 & \(-\)0.1560 & 1.0337 & 0.9384 & \(-\)0.0800 & \(-\)0.2190 & 0.9632 & 1.2388 & 0.1993 \\ \hline O\_firstorder\_Mean & \(-\)0.1230 & 0.1965 & 1.1544 & 0.6577 & 0.1154 & 0.2908 & 0.8318 & 0.6643 & 0.0207 * \\ \hline W-HHH\_glcm\_JointEnergy & \(-\)0.0526 & 0.0776 & 1.0925 & 0.8696 & \(-\)0.3825 & \(-\)0.1291 & 0.8819 & 1.0823 & 0.1591 \\ \hline W-HHH\_glcm\_Imc1 & \(-\)0.0565 & 0.0869 & 1.0679 & 0.9050 & 0.1817 & 0.3549 & 0.7541 & 0.6453 & 0.1993 \\ \hline W-HHH\_firstorder\_Energy & \(-\)0.0917 & 0.1253 & 0.1835 & 1.5198 & \(-\)0.1577 & \(-\)0.1340 & 0.0651 & 0.1342 & 0.1591 \\ \hline W-LHH\_firstorder\_Energy & \(-\)0.1404 & 0.1940 & 0.5804 & 1.3641 & \(-\)0.3493 & \(-\)0.2788 & 0.2868 & 0.6737 & 0.1406 \\ \hline W-LHH\_firstorder\_Median & 0.1818 & \(-\)0.2496 & 1.1321 & 0.7358 & \(-\)0.0498 & \(-\)0.1823 & 0.6231 & 0.4321 & 0.0036 * \\ \hline W-LLH\_glcm\_Correlation & \(-\)0.0680 & 0.0908 & 1.0421 & 0.9493 & 0.0318 & 0.1667 & 1.1192 & 0.9215 & 0.1993 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics for features distribution between adenocarcinoma and other cases. Std is the standard deviation, IQR is the interquartile range. Mann-Whitney U tests was used to calculate the \(p\)-values. The multiple comparisons problem was handled with Benjamini-Hochberg (BH) correction. * indicates statistically significant features (\(p\)-value < 0.05). In all the experiments, cross-validation was performed with 10-folds and stratified samples, i.e., the distribution of classes remains the same across folds. Figure 6 shows the workflows implemented for the four classification experiments in terms of input data and output results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyperparameters** & **Value** \\ \hline \multirow{3}{*}{LogReg} & max\_iter & 100 \\ \cline{2-3}  & penalty & 12 \\ \cline{2-3}  & C & 1.0 \\ \hline \multirow{3}{*}{SVM} & kernel & rbf \\ \cline{2-3}  & C & 1.0 \\ \cline{2-3}  & penalty & 12 \\ \hline \multirow{3}{*}{AdaBo} & n\_estimators & 50 \\ \cline{2-3}  & learning\_rate & 1.0 \\ \cline{2-3}  & n\_estimators & 100 \\ \cline{2-3}  & criterion & gini \\ \cline{2-3}  & max\_depth & None \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training hyperparameters. Figure 6: Experimental Setup. (**a**) Workflow for experiment 1. It consists of separate internal cross-validation for each cohort. (**b**) Workflow for experiment 2. It consists of using D1 for training and D2 and D3 as external validation cohorts. (**c**) Workflow for experiment 3. It consists of using D1 and D2 for training and D3 as external validation cohort. (**d**) Workflow for experiment 4. It consists of performing internal cross-validation considering D1, D2, and D3 as a single large cohort.