Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning
Abstract Cross-sectional X-ray imaging has become the standard for staging most solid organ malignancies. However, for some malignancies such as urinary bladder cancer, the ability to accurately assess local extent of the disease and understand response to systemic chemotherapy is limited with curre...
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Nature Portfolio
2017
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oai:doaj.org-article:fa5b71fe40344297b76499a04ea5ed9b2021-12-02T15:05:36ZBladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning10.1038/s41598-017-09315-w2045-2322https://doaj.org/article/fa5b71fe40344297b76499a04ea5ed9b2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-09315-whttps://doaj.org/toc/2045-2322Abstract Cross-sectional X-ray imaging has become the standard for staging most solid organ malignancies. However, for some malignancies such as urinary bladder cancer, the ability to accurately assess local extent of the disease and understand response to systemic chemotherapy is limited with current imaging approaches. In this study, we explored the feasibility that radiomics-based predictive models using pre- and post-treatment computed tomography (CT) images might be able to distinguish between bladder cancers with and without complete chemotherapy responses. We assessed three unique radiomics-based predictive models, each of which employed different fundamental design principles ranging from a pattern recognition method via deep-learning convolution neural network (DL-CNN), to a more deterministic radiomics feature-based approach and then a bridging method between the two, utilizing a system which extracts radiomics features from the image patterns. Our study indicates that the computerized assessment using radiomics information from the pre- and post-treatment CT of bladder cancer patients has the potential to assist in assessment of treatment response.Kenny H. ChaLubomir HadjiiskiHeang-Ping ChanAlon Z. WeizerAjjai AlvaRichard H. CohanElaine M. CaoiliChintana ParamagulRavi K. SamalaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017) |
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Medicine R Science Q Kenny H. Cha Lubomir Hadjiiski Heang-Ping Chan Alon Z. Weizer Ajjai Alva Richard H. Cohan Elaine M. Caoili Chintana Paramagul Ravi K. Samala Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning |
| description |
Abstract Cross-sectional X-ray imaging has become the standard for staging most solid organ malignancies. However, for some malignancies such as urinary bladder cancer, the ability to accurately assess local extent of the disease and understand response to systemic chemotherapy is limited with current imaging approaches. In this study, we explored the feasibility that radiomics-based predictive models using pre- and post-treatment computed tomography (CT) images might be able to distinguish between bladder cancers with and without complete chemotherapy responses. We assessed three unique radiomics-based predictive models, each of which employed different fundamental design principles ranging from a pattern recognition method via deep-learning convolution neural network (DL-CNN), to a more deterministic radiomics feature-based approach and then a bridging method between the two, utilizing a system which extracts radiomics features from the image patterns. Our study indicates that the computerized assessment using radiomics information from the pre- and post-treatment CT of bladder cancer patients has the potential to assist in assessment of treatment response. |
| format |
article |
| author |
Kenny H. Cha Lubomir Hadjiiski Heang-Ping Chan Alon Z. Weizer Ajjai Alva Richard H. Cohan Elaine M. Caoili Chintana Paramagul Ravi K. Samala |
| author_facet |
Kenny H. Cha Lubomir Hadjiiski Heang-Ping Chan Alon Z. Weizer Ajjai Alva Richard H. Cohan Elaine M. Caoili Chintana Paramagul Ravi K. Samala |
| author_sort |
Kenny H. Cha |
| title |
Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning |
| title_short |
Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning |
| title_full |
Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning |
| title_fullStr |
Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning |
| title_full_unstemmed |
Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning |
| title_sort |
bladder cancer treatment response assessment in ct using radiomics with deep-learning |
| publisher |
Nature Portfolio |
| publishDate |
2017 |
| url |
https://doaj.org/article/fa5b71fe40344297b76499a04ea5ed9b |
| work_keys_str_mv |
AT kennyhcha bladdercancertreatmentresponseassessmentinctusingradiomicswithdeeplearning AT lubomirhadjiiski bladdercancertreatmentresponseassessmentinctusingradiomicswithdeeplearning AT heangpingchan bladdercancertreatmentresponseassessmentinctusingradiomicswithdeeplearning AT alonzweizer bladdercancertreatmentresponseassessmentinctusingradiomicswithdeeplearning AT ajjaialva bladdercancertreatmentresponseassessmentinctusingradiomicswithdeeplearning AT richardhcohan bladdercancertreatmentresponseassessmentinctusingradiomicswithdeeplearning AT elainemcaoili bladdercancertreatmentresponseassessmentinctusingradiomicswithdeeplearning AT chintanaparamagul bladdercancertreatmentresponseassessmentinctusingradiomicswithdeeplearning AT raviksamala bladdercancertreatmentresponseassessmentinctusingradiomicswithdeeplearning |
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1718388807212990464 |