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CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma
Preoperatively accurate evaluation of risk for early postoperative recurrence contributes to maximizing the therapeutic success for intrahepatic cholangiocarcinoma (iCCA) patients. This study aimed to investigate the potential of deep learning (DL) algorithms for predicting postoperative early recur...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9120508/ https://www.ncbi.nlm.nih.gov/pubmed/35590089 http://dx.doi.org/10.1038/s41598-022-12604-8 |
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author | Wakiya, Taiichi Ishido, Keinosuke Kimura, Norihisa Nagase, Hayato Kanda, Taishu Ichiyama, Sotaro Soma, Kenji Matsuzaka, Masashi Sasaki, Yoshihiro Kubota, Shunsuke Fujita, Hiroaki Sawano, Takeyuki Umehara, Yutaka Wakasa, Yusuke Toyoki, Yoshikazu Hakamada, Kenichi |
author_facet | Wakiya, Taiichi Ishido, Keinosuke Kimura, Norihisa Nagase, Hayato Kanda, Taishu Ichiyama, Sotaro Soma, Kenji Matsuzaka, Masashi Sasaki, Yoshihiro Kubota, Shunsuke Fujita, Hiroaki Sawano, Takeyuki Umehara, Yutaka Wakasa, Yusuke Toyoki, Yoshikazu Hakamada, Kenichi |
author_sort | Wakiya, Taiichi |
collection | PubMed |
description | Preoperatively accurate evaluation of risk for early postoperative recurrence contributes to maximizing the therapeutic success for intrahepatic cholangiocarcinoma (iCCA) patients. This study aimed to investigate the potential of deep learning (DL) algorithms for predicting postoperative early recurrence through the use of preoperative images. We collected the dataset, including preoperative plain computed tomography (CT) images, from 41 patients undergoing curative surgery for iCCA at multiple institutions. We built a CT patch-based predictive model using a residual convolutional neural network and used fivefold cross-validation. The prediction accuracy of the model was analyzed. We defined early recurrence as recurrence within a year after surgical resection. Of the 41 patients, early recurrence was observed in 20 (48.8%). A total of 71,081 patches were extracted from the entire segmented tumor area of each patient. The average accuracy of the ResNet model for predicting early recurrence was 98.2% for the training dataset. In the validation dataset, the average sensitivity, specificity, and accuracy were 97.8%, 94.0%, and 96.5%, respectively. Furthermore, the area under the receiver operating characteristic curve was 0.994. Our CT-based DL model exhibited high predictive performance in projecting postoperative early recurrence, proposing a novel insight into iCCA management. |
format | Online Article Text |
id | pubmed-9120508 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-91205082022-05-21 CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma Wakiya, Taiichi Ishido, Keinosuke Kimura, Norihisa Nagase, Hayato Kanda, Taishu Ichiyama, Sotaro Soma, Kenji Matsuzaka, Masashi Sasaki, Yoshihiro Kubota, Shunsuke Fujita, Hiroaki Sawano, Takeyuki Umehara, Yutaka Wakasa, Yusuke Toyoki, Yoshikazu Hakamada, Kenichi Sci Rep Article Preoperatively accurate evaluation of risk for early postoperative recurrence contributes to maximizing the therapeutic success for intrahepatic cholangiocarcinoma (iCCA) patients. This study aimed to investigate the potential of deep learning (DL) algorithms for predicting postoperative early recurrence through the use of preoperative images. We collected the dataset, including preoperative plain computed tomography (CT) images, from 41 patients undergoing curative surgery for iCCA at multiple institutions. We built a CT patch-based predictive model using a residual convolutional neural network and used fivefold cross-validation. The prediction accuracy of the model was analyzed. We defined early recurrence as recurrence within a year after surgical resection. Of the 41 patients, early recurrence was observed in 20 (48.8%). A total of 71,081 patches were extracted from the entire segmented tumor area of each patient. The average accuracy of the ResNet model for predicting early recurrence was 98.2% for the training dataset. In the validation dataset, the average sensitivity, specificity, and accuracy were 97.8%, 94.0%, and 96.5%, respectively. Furthermore, the area under the receiver operating characteristic curve was 0.994. Our CT-based DL model exhibited high predictive performance in projecting postoperative early recurrence, proposing a novel insight into iCCA management. Nature Publishing Group UK 2022-05-19 /pmc/articles/PMC9120508/ /pubmed/35590089 http://dx.doi.org/10.1038/s41598-022-12604-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Wakiya, Taiichi Ishido, Keinosuke Kimura, Norihisa Nagase, Hayato Kanda, Taishu Ichiyama, Sotaro Soma, Kenji Matsuzaka, Masashi Sasaki, Yoshihiro Kubota, Shunsuke Fujita, Hiroaki Sawano, Takeyuki Umehara, Yutaka Wakasa, Yusuke Toyoki, Yoshikazu Hakamada, Kenichi CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma |
title | CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma |
title_full | CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma |
title_fullStr | CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma |
title_full_unstemmed | CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma |
title_short | CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma |
title_sort | ct-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9120508/ https://www.ncbi.nlm.nih.gov/pubmed/35590089 http://dx.doi.org/10.1038/s41598-022-12604-8 |
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