  2D and 3D tumor features are widely used in a variety of medical image
analysis tasks. However, for chemotherapy response prediction, the
effectiveness between different kinds of 2D and 3D features are not
comprehensively assessed, especially in ovarian cancer-related applications.
This investigation aims to accomplish such a comprehensive evaluation. For this
purpose, CT images were collected retrospectively from 188 advanced-stage
ovarian cancer patients. All the metastatic tumors that occurred in each
patient were segmented and then processed by a set of six filters. Next, three
categories of features, namely geometric, density, and texture features, were
calculated from both the filtered results and the original segmented tumors,
generating a total of 1595 and 1403 features for the 3D and 2D tumors,
respectively. In addition to the conventional single-slice 2D and full-volume
3D tumor features, we also computed the incomplete-3D tumor features, which
were achieved by sequentially adding one individual CT slice and calculating
the corresponding features. Support vector machine (SVM) based prediction
models were developed and optimized for each feature set. 5-fold
cross-validation was used to assess the performance of each individual model.
The results show that the 2D feature-based model achieved an AUC (area under
the ROC curve [receiver operating characteristic]) of 0.84+-0.02. When adding
more slices, the AUC first increased to reach the maximum and then gradually
decreased to 0.86+-0.02. The maximum AUC was yielded when adding two adjacent
slices, with a value of 0.91+-0.01. This initial result provides meaningful
information for optimizing machine learning-based decision-making support tools
in the future.
