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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 appli...

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Autores principales: Abdoli, Neman, Zhang, Ke, Gilley, Patrik, Chen, Xuxin, Sadri, Youkabed, Thai, Theresa, Dockery, Lauren, Moore, Kathleen, Mannel, Robert, Qiu, Yuchen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669238/
https://www.ncbi.nlm.nih.gov/pubmed/38002458
http://dx.doi.org/10.3390/bioengineering10111334
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author Abdoli, Neman
Zhang, Ke
Gilley, Patrik
Chen, Xuxin
Sadri, Youkabed
Thai, Theresa
Dockery, Lauren
Moore, Kathleen
Mannel, Robert
Qiu, Yuchen
author_facet Abdoli, Neman
Zhang, Ke
Gilley, Patrik
Chen, Xuxin
Sadri, Youkabed
Thai, Theresa
Dockery, Lauren
Moore, Kathleen
Mannel, Robert
Qiu, Yuchen
author_sort Abdoli, Neman
collection PubMed
description 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.
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spelling pubmed-106692382023-11-20 Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy Abdoli, Neman Zhang, Ke Gilley, Patrik Chen, Xuxin Sadri, Youkabed Thai, Theresa Dockery, Lauren Moore, Kathleen Mannel, Robert Qiu, Yuchen Bioengineering (Basel) Article 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. MDPI 2023-11-20 /pmc/articles/PMC10669238/ /pubmed/38002458 http://dx.doi.org/10.3390/bioengineering10111334 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Abdoli, Neman
Zhang, Ke
Gilley, Patrik
Chen, Xuxin
Sadri, Youkabed
Thai, Theresa
Dockery, Lauren
Moore, Kathleen
Mannel, Robert
Qiu, Yuchen
Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy
title Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy
title_full Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy
title_fullStr Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy
title_full_unstemmed Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy
title_short Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy
title_sort evaluating the effectiveness of 2d and 3d ct image features for predicting tumor response to chemotherapy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669238/
https://www.ncbi.nlm.nih.gov/pubmed/38002458
http://dx.doi.org/10.3390/bioengineering10111334
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