Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization

Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work we trained on a public cohort with 201 patients and t...

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Autores principales: Zong, Weiwei, Carver, Eric, Zhu, Simeng, Schaff, Eric, Chapman, Daniel, Lee, Joon, Bagher-Ebadian, Hassan, Movsas, Benjamin, Wen, Winston, Alafif, Tarik, Zong, Xiangyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794806/
https://www.ncbi.nlm.nih.gov/pubmed/36575209
http://dx.doi.org/10.1038/s41598-022-27007-y
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author Zong, Weiwei
Carver, Eric
Zhu, Simeng
Schaff, Eric
Chapman, Daniel
Lee, Joon
Bagher-Ebadian, Hassan
Movsas, Benjamin
Wen, Winston
Alafif, Tarik
Zong, Xiangyun
author_facet Zong, Weiwei
Carver, Eric
Zhu, Simeng
Schaff, Eric
Chapman, Daniel
Lee, Joon
Bagher-Ebadian, Hassan
Movsas, Benjamin
Wen, Winston
Alafif, Tarik
Zong, Xiangyun
author_sort Zong, Weiwei
collection PubMed
description Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work we trained on a public cohort with 201 patients and the cropped 2.5D slices of the prostate glands were used as the input, and the optimal model were searched in the model space using autoKeras. As an innovative move, peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effective in highlighting the most suspicious slices out of a sequence, hopefully to greatly ease the workload for the physicians.
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spelling pubmed-97948062022-12-29 Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization Zong, Weiwei Carver, Eric Zhu, Simeng Schaff, Eric Chapman, Daniel Lee, Joon Bagher-Ebadian, Hassan Movsas, Benjamin Wen, Winston Alafif, Tarik Zong, Xiangyun Sci Rep Article Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work we trained on a public cohort with 201 patients and the cropped 2.5D slices of the prostate glands were used as the input, and the optimal model were searched in the model space using autoKeras. As an innovative move, peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effective in highlighting the most suspicious slices out of a sequence, hopefully to greatly ease the workload for the physicians. Nature Publishing Group UK 2022-12-27 /pmc/articles/PMC9794806/ /pubmed/36575209 http://dx.doi.org/10.1038/s41598-022-27007-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zong, Weiwei
Carver, Eric
Zhu, Simeng
Schaff, Eric
Chapman, Daniel
Lee, Joon
Bagher-Ebadian, Hassan
Movsas, Benjamin
Wen, Winston
Alafif, Tarik
Zong, Xiangyun
Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization
title Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization
title_full Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization
title_fullStr Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization
title_full_unstemmed Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization
title_short Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization
title_sort prostate cancer malignancy detection and localization from mpmri using auto-deep learning as one step closer to clinical utilization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794806/
https://www.ncbi.nlm.nih.gov/pubmed/36575209
http://dx.doi.org/10.1038/s41598-022-27007-y
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