## 4 Discussion  
In this study, we conducted a comprehensive evaluation of various radiomic feature extraction methods for predicting early response of ovarian cancer to chemotherapy. Our investigation focused on 2D, incomplete 3D, and 3D radiomic features and their ability to represent and discriminate the underlying tumor characteristics. Our findings demonstrate that the incomplete 3D features extracted from the central tumor slice and its two adjacent slices yielded better prediction accuracy and might be recommended as the preferred approach. As compared to the previous studies, the most unique characteristic of our investigation is that we explored the performance of the features computed from incomplete 3D tumors, which contain a portion of the tumor slices. The comparison between the 2D or 3D features has been investigated by many research groups. In general, the 2D features are calculated from a single slice containing the maximal projection within the entire tumor. Accordingly, tumor segmentation and annotation are relatively easier, but some useful information is lost. For instance, it is difficult to accurately extract tumor volume, surface area, spherical disproportion, and other features that reflect geometric irregularities from a 2D tumor. On the other end, despite the comprehensive information, the 3D feature suffers from more information uncertainties. Given that it is very difficult to clearly define the boundary between the tumor region and its surround healthy tissues, there is no segmentation algorithm which is able to perfectly extract the tumor contour [44]. Moreover, the tumor heterogeneity may only be inside several central slices. The marginal slices may only contain suspicious surrounding tissue, and they may not have any information associated with the needed treatment responses. As a result, more uncertainties will be added on the segmented 3D tumors, which may adversely affect the performance of prediction models. Therefore, there is a trade-off between adding more useful information and uncertainties when sequentially increasing the adjacent slices. In this study, we first demonstrated that using the central and its two neighboring slices may achieve better performance than other situations. To the best of our knowledge, no similar studies have been performed on this topic before. Moreover, our study highlights the importance of texture-based features and wavelet filtering for predicting treatment response. We observe that texture-based features were more informative, especially when they are computed from the wavelet transform-processed images. This could be attributed by the fact that texture-based features are able to capture the heterogeneity and complexity of the tumor microenvironment, which can provide insights into tumor biology and response to treatment. Wavelet transform is able to initially concentrate the tumor edge and texture information, while the other irrelevant information is discarded. Meanwhile, our study suggests that the number of slices used for CT image analysis can have an impact on the correlation structure of extracted radiomics features. The high correlation only occurs on the complete 3D tumor features from the tumor images processed by certain kinds of filters (e.g., logarithm, exponential, and square), which may be explained by the fact that such filters can enhance certain 3D structures, and these structures could lead to consistent value changes on some categories of the features. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **AUC \(\pm\) STD** & **ACC \(\pm\) STD** \\  & **95\% CI** & **95\% CI** \\ \hline \multirow{2}{*}{3D\({}_{8\text{ Slices}}\)} & 0.84 \(\pm\) 0.02 & 0.75 \(\pm\) 0.03 \\  & [0.78, 0.86] & [0.70, 0.80] \\ \hline \multirow{2}{*}{3D\({}_{9\text{ Slices}}\)} & 0.83 \(\pm\) 0.01 & 0.73 \(\pm\) 0.02 \\  & [0.78, 0.87] & [0.69, 0.78] \\ \hline \multirow{2}{*}{3D} & 0.86 \(\pm\) 0.02 & 0.77 \(\pm\) 0.02 \\  & [0.82, 0.89] & [0.72, 0.83] \\ \hline \hline \end{tabular}
\end{table}