Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Bioengineering (Basel, Switzerland) - 10(2023), 11 vom: 20. Nov. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Abdoli, Neman [VerfasserIn] |
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| Links: |
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| Themen: |
2D and 3D features |
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| Anmerkungen: |
Date Revised 10.02.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/bioengineering10111334 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM364936762 |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a 2D and 3D features | |
| 650 | 4 | |a chemotherapy response prediction | |
| 650 | 4 | |a incomplete 3D features | |
| 650 | 4 | |a ovarian cancer | |
| 650 | 4 | |a precision medicine | |
| 650 | 4 | |a radiomics | |
| 700 | 1 | |a Zhang, Ke |e verfasserin |4 aut | |
| 700 | 1 | |a Gilley, Patrik |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Xuxin |e verfasserin |4 aut | |
| 700 | 1 | |a Sadri, Youkabed |e verfasserin |4 aut | |
| 700 | 1 | |a Thai, Theresa |e verfasserin |4 aut | |
| 700 | 1 | |a Dockery, Lauren |e verfasserin |4 aut | |
| 700 | 1 | |a Moore, Kathleen |e verfasserin |4 aut | |
| 700 | 1 | |a Mannel, Robert |e verfasserin |4 aut | |
| 700 | 1 | |a Qiu, Yuchen |e verfasserin |4 aut | |
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