Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy.
Bioengineering (Basel)
; 10(11)2023 Nov 20.
Article
in En
| MEDLINE
| ID: mdl-38002458
ABSTRACT
Background and Objective:
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.Methods:
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 1403 and 1595 features for the 2D and 3D 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. Five-fold cross-validation was used to assess the performance of each individual model.Results:
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.Conclusions:
This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Bioengineering (Basel)
Year:
2023
Document type:
Article
Affiliation country:
Estados Unidos