## 2 Materials and Methods
### Develop Machine Learning-Based Models to Predict Tumor Response to Chemotherapy
Figure 3: One example of the tumor segmentation process, in which the central slice is outlined with red color. (**a**) Identified tumor on the CT slices. (**b**) Tumors outlined using our developed CAD software. (**c**) Segmented tumor masks. Figure 2: Sample tumor images from (**a**) responder case to treatment. (**b**) non-responder case. After applying the PCC and LASSO feature selection methods, we identified 115 two-dimensional features as the most effective feature cluster for further analysis. Similarly, 56-126 features were identified for different incomplete 3D tumor features pools, and 73 features were identified from the full 3D feature pool. As illustrated in Table 3, a minimum of 78% of the selected features can be categorized into texture-based features, while the remainder fall into the categories of shape or density features. When dividing the features based on the filters, we observe that the largest group of features is extracted from the wavelet transform-based filtered images, with the features calculated from LBP-filtered images forming the second most prominent group. Our models used these features as inputs to generate the prediction score. Figure 5 illustrates the ROC curve achieved by the models trained with one to nine number of slices of tumor mask and whole slices, while Table 4 summarizes the corresponding values of AUC and overall accuracy values. The results demonstrate that adding more slices to the central slice can increase the prediction performance, but only for a certain range of additional slices. The highest performance was achieved when the model was constructed with one central slice and two adjacent slices, with the model yielding an AUC of 0.91 \(\pm\) 0.01 (95% confidence interval [0.88, 0.94]) and an overall predicting accuracy of 0.84 \(\pm\) 0.03 (95% confidence interval [0.78, 0.88]). The 3D model with three and 5 adjacent slices were the second and third best performing models, which achieved an AUC values of 0.89 \(\pm\) 0.01 and 0.86 \(\pm\) 0.02, respectively. Model_3D, with an AUC of 0.86 \(\pm\) 0.02 (95% confidence interval [0.82, 0.89]), performed better than Model_2D, with AUC of 0.84 \(\pm\) 0.02 (95% confidence interval [0.78, 0.88]). All these trends are also illustrated in Figure 5b. Figure 4: Feature heatmap of (**a**) 2D tumors, (**b**) incomplete 3D tumors, and (**c**) 3D tumor. The corresponding histograms were illustrated in (**d–f**) for 2D, incomplete 3D, and 3D features, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & **AUC \(\pm\) STD** & **ACC \(\pm\) STD** \\  & **95\% CI** & **95\% CI** \\ \hline \multirow{2}{*}{2D} & 0.84 \(\pm\) 0.02 & 0.75 \(\pm\) 0.03 \\  & [0.78, 0.88] & [0.69, 0.80] \\ \hline \multirow{2}{*}{3D\({}_{\text{2 Slices}}\)} & 0.89 \(\pm\) 0.01 & 0.83 \(\pm\) 0.02 \\  & [0.85, 0.92] & [0.77, 0.87] \\ \hline \multirow{2}{*}{3D\({}_{\text{3 Slices}}\)} & 0.91 \(\pm\) 0.01 & 0.84 \(\pm\) 0.02 \\  & [0.88, 0.94] & [0.78, 0.88] \\ \hline \multirow{2}{*}{3D\({}_{\text{4 Slices}}\)} & 0.85 \(\pm\) 0.02 & 0.76 \(\pm\) 0.02 \\  & [0.80, 0.89] & [0.71, 0.82] \\ \hline \multirow{2}{*}{3D\({}_{\text{5 Slices}}\)} & 0.86 \(\pm\) 0.02 & 0.78 \(\pm\) 0.02 \\  & [0.82, 0.90] & [0.72, 0.83] \\ \hline \multirow{2}{*}{3D\({}_{\text{6 Slices}}\)} & 0.84 \(\pm\) 0.02 & 0.74 \(\pm\) 0.03 \\  & [0.79, 0.88] & [0.69, 0.79] \\ \hline \multirow{2}{*}{3D\({}_{\text{7 Slices}}\)} & 0.86 \(\pm\) 0.02 & 0.76 \(\pm\) 0.03 \\  & [0.80, 0.88] & [0.70, 0.80] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of the prediction models. Figure 5: Performance comparison of the models for six-month PFS prediction (**a**) ROC curves of the prediction models. (**b**) The corresponding AUC values of ten prediction models (with 95% CI).