## 4 Discussion  
We have previously classified renal tumors using a CNN model and data set framework [8]. Compared to the results of prior works, the results obtained in this study are significant. In the previous study, the results obtained by six radiologists and AI were compared, but the predictive rate of fat-poor tumors was only about 50%. It can be said that it is very encouraging that the prediction rate has gradually increased with the development of  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**AU-ROC**} & \multicolumn{4}{c}{**AU-PRC**} \\ \hline
**CT Phase** & **Arterial** & **Delayed** & \begin{tabular}{c} **Non-** \\ **Contrast** \\ \end{tabular}  & **Portal** & **Arterial** & **Delayed** & \ \begin{tabular}{c} **Non-** \\ **Contrast** \\ \end{tabular}  & **Portal** \\ \hline \begin{tabular}{l} **Sample Size** \\ **(Train/Test)** \\ \end{tabular}  & **236/82** & **252/88** & **318/101** & **360/81** & **236/82** & **252/88** & **318/101** & **360/81** \\ \hline Linear SVM & 0.395 \(\pm\) 0.068 & 0.443 \(\pm\) 0.054 & 0.419 \(\pm\) 0.061 & 0.412 \(\pm\) 0.08 & 0.447 \(\pm\) 0.038 & 0.472 \(\pm\) 0.032 & 0.467 \(\pm\) 0.029 & 0.466 \(\pm\) 0.043 \\ Rbf SVM & 0.530 \(\pm\) 0.048 & 0.514 \(\pm\) 0.056 & 0.515 \(\pm\) 0.079 & 0.548 \(\pm\) 0.042 & 0.5 ± 0.0 & 0.514 \(\pm\) 0.034 & 0.525 \(\pm\) 0.05 \\ XGBoost & 0.415 \(\pm\) 0.038 & 0.351 \(\pm\) 0.037 & 0.583 \(\pm\) 0.026 & 0.471 \(\pm\) 0.034 & 0.466 \(\pm\) 0.023 & 0.434 \(\pm\) 0.021 & 0.594 \(\pm\) 0.016 & 0.492 \(\pm\) 0.022 \\ Random & 0.431 \(\pm\) 0.063 & 0.527 \(\pm\) 0.045 & 0.603 \(\pm\) 0.061 & 0.473 \(\pm\) 0.043 & 0.487 \(\pm\) 0.035 & 0.553 \(\pm\) 0.027 & 0.592 \(\pm\) 0.043 & 0.511 \(\pm\) 0.027 \\ \hline \multicolumn{8}{c}{**ACC**} & \multicolumn{4}{c}{**F1 Score**} \\ \hline
**CT Phase** & **Arterial** & **Delayed** & \begin{tabular}{c} **Non-** \\ **Contrast** \\ \end{tabular}  & **Portal** & **Arterial** & **Delayed** & \ \begin{tabular}{c} **Non-** \\ **Contrast** \\ \end{tabular}  & **Portal** \\ \hline \begin{tabular}{l} **Sample Size** \\ **(Train/Test)** \\ \end{tabular}  & **236/82** & **252/88** & **318/101** & **360/81** & **236/82** & **252/88** & **318/101** & **360/81** \\ \hline Linear SVM & 0.659 \(\pm\) 0.003 & 0.681 \(\pm\) 0.019 & 0.695 \(\pm\) 0.022 & 0.702 \(\pm\) 0.023 & 0.792 \(\pm\) 0.023 & 0.810 \(\pm\) 0.014 & 0.820 \(\pm\) 0.015 & 0.824 \(\pm\) 0.015 \\ Rbf SVM & 0.706 \(\pm\) 0.013 & 0.693 \(\pm\) 0.0 & 0.721 \(\pm\) 0.012 & 0.728 \(\pm\) 0.0 & 0.825 \(\pm\) 0.09 & 0.818 \(\pm\) 0.0 & 0.837 \(\pm\) 0.08 & 0.843 \(\pm\) 0.0 \\ XGBoost & 0.668 \(\pm\) 0.012 & 0.633 \(\pm\) 0.013 & 0.728 \(\pm\) 0.016 & 0.725 \(\pm\) 0.008 & 0.798 \(\pm\) 0.008 & 0.773 \(\pm\) 0.01 & 0.840 \(\pm\) 0.01 & 0.840 \(\pm\) 0.005 \\ Random & 0.696 \(\pm\) 0.07 & 0.693 \(\pm\) 0.0 & 0. AI technology. Thus, the ultimate goal of this study was to determine the need for surgery through a single CT scan. This study showed a robust ML pipeline intended to enhance the effective learning of diverse radiomic features (i.e., a wide column of data) and the generalizability of prediction. This approach included an oversampling technique (i.e., SMOTE) to overcome the imbalance between cases and controls, a combination of various feature selection methods (i.e., the mass univariate statistical test, the Principal Component Analysis, and automated Sequential Feature Selection) to select radiomic features rigorously, and an automated hyperparameter tuning tool (i.e., optuna) to identify the optimal ML models. Through the combinatorial use of various techniques, we developed robust and accurate radiomics-based ML pipelines. The optimized pipeline used in this study is expected to aid in designing future radiomics-based ML pipelines. However, in the end, feature-based ML will eventually be replaced by end-to-end deep learning models for improved accuracy, interpretation, and generalizability.