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Predicting the Efficacy of Neoadjuvant Chemotherapy for Pancreatic Cancer Using Deep Learning of Contrast-Enhanced Ultrasound Videos

Contrast-enhanced ultrasound (CEUS) is a promising imaging modality in predicting the efficacy of neoadjuvant chemotherapy for pancreatic cancer, a tumor with high mortality. In this study, we proposed a deep-learning-based strategy for analyzing CEUS videos to predict the prognosis of pancreatic ca...

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Autores principales: Shao, Yuming, Dang, Yingnan, Cheng, Yuejuan, Gui, Yang, Chen, Xueqi, Chen, Tianjiao, Zeng, Yan, Tan, Li, Zhang, Jing, Xiao, Mengsu, Yan, Xiaoyi, Lv, Ke, Zhou, Zhuhuang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10341263/
https://www.ncbi.nlm.nih.gov/pubmed/37443577
http://dx.doi.org/10.3390/diagnostics13132183
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author Shao, Yuming
Dang, Yingnan
Cheng, Yuejuan
Gui, Yang
Chen, Xueqi
Chen, Tianjiao
Zeng, Yan
Tan, Li
Zhang, Jing
Xiao, Mengsu
Yan, Xiaoyi
Lv, Ke
Zhou, Zhuhuang
author_facet Shao, Yuming
Dang, Yingnan
Cheng, Yuejuan
Gui, Yang
Chen, Xueqi
Chen, Tianjiao
Zeng, Yan
Tan, Li
Zhang, Jing
Xiao, Mengsu
Yan, Xiaoyi
Lv, Ke
Zhou, Zhuhuang
author_sort Shao, Yuming
collection PubMed
description Contrast-enhanced ultrasound (CEUS) is a promising imaging modality in predicting the efficacy of neoadjuvant chemotherapy for pancreatic cancer, a tumor with high mortality. In this study, we proposed a deep-learning-based strategy for analyzing CEUS videos to predict the prognosis of pancreatic cancer neoadjuvant chemotherapy. Pre-trained convolutional neural network (CNN) models were used for binary classification of the chemotherapy as effective or ineffective, with CEUS videos collected before chemotherapy as the model input, and with the efficacy after chemotherapy as the reference standard. We proposed two deep learning models. The first CNN model used videos of ultrasound (US) and CEUS (US+CEUS), while the second CNN model only used videos of selected regions of interest (ROIs) within CEUS (CEUS-ROI). A total of 38 patients with strict restriction of clinical factors were enrolled, with 76 original CEUS videos collected. After data augmentation, 760 and 720 videos were included for the two CNN models, respectively. Seventy-six-fold and 72-fold cross-validations were performed to validate the classification performance of the two CNN models. The areas under the curve were 0.892 and 0.908 for the two models. The accuracy, recall, precision and F1 score were 0.829, 0.759, 0.786, and 0.772 for the first model. Those were 0.864, 0.930, 0.866, and 0.897 for the second model. A total of 38.2% and 40.3% of the original videos could be clearly distinguished by the deep learning models when the naked eye made an inaccurate classification. This study is the first to demonstrate the feasibility and potential of deep learning models based on pre-chemotherapy CEUS videos in predicting the efficacy of neoadjuvant chemotherapy for pancreas cancer.
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spelling pubmed-103412632023-07-14 Predicting the Efficacy of Neoadjuvant Chemotherapy for Pancreatic Cancer Using Deep Learning of Contrast-Enhanced Ultrasound Videos Shao, Yuming Dang, Yingnan Cheng, Yuejuan Gui, Yang Chen, Xueqi Chen, Tianjiao Zeng, Yan Tan, Li Zhang, Jing Xiao, Mengsu Yan, Xiaoyi Lv, Ke Zhou, Zhuhuang Diagnostics (Basel) Article Contrast-enhanced ultrasound (CEUS) is a promising imaging modality in predicting the efficacy of neoadjuvant chemotherapy for pancreatic cancer, a tumor with high mortality. In this study, we proposed a deep-learning-based strategy for analyzing CEUS videos to predict the prognosis of pancreatic cancer neoadjuvant chemotherapy. Pre-trained convolutional neural network (CNN) models were used for binary classification of the chemotherapy as effective or ineffective, with CEUS videos collected before chemotherapy as the model input, and with the efficacy after chemotherapy as the reference standard. We proposed two deep learning models. The first CNN model used videos of ultrasound (US) and CEUS (US+CEUS), while the second CNN model only used videos of selected regions of interest (ROIs) within CEUS (CEUS-ROI). A total of 38 patients with strict restriction of clinical factors were enrolled, with 76 original CEUS videos collected. After data augmentation, 760 and 720 videos were included for the two CNN models, respectively. Seventy-six-fold and 72-fold cross-validations were performed to validate the classification performance of the two CNN models. The areas under the curve were 0.892 and 0.908 for the two models. The accuracy, recall, precision and F1 score were 0.829, 0.759, 0.786, and 0.772 for the first model. Those were 0.864, 0.930, 0.866, and 0.897 for the second model. A total of 38.2% and 40.3% of the original videos could be clearly distinguished by the deep learning models when the naked eye made an inaccurate classification. This study is the first to demonstrate the feasibility and potential of deep learning models based on pre-chemotherapy CEUS videos in predicting the efficacy of neoadjuvant chemotherapy for pancreas cancer. MDPI 2023-06-27 /pmc/articles/PMC10341263/ /pubmed/37443577 http://dx.doi.org/10.3390/diagnostics13132183 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
Shao, Yuming
Dang, Yingnan
Cheng, Yuejuan
Gui, Yang
Chen, Xueqi
Chen, Tianjiao
Zeng, Yan
Tan, Li
Zhang, Jing
Xiao, Mengsu
Yan, Xiaoyi
Lv, Ke
Zhou, Zhuhuang
Predicting the Efficacy of Neoadjuvant Chemotherapy for Pancreatic Cancer Using Deep Learning of Contrast-Enhanced Ultrasound Videos
title Predicting the Efficacy of Neoadjuvant Chemotherapy for Pancreatic Cancer Using Deep Learning of Contrast-Enhanced Ultrasound Videos
title_full Predicting the Efficacy of Neoadjuvant Chemotherapy for Pancreatic Cancer Using Deep Learning of Contrast-Enhanced Ultrasound Videos
title_fullStr Predicting the Efficacy of Neoadjuvant Chemotherapy for Pancreatic Cancer Using Deep Learning of Contrast-Enhanced Ultrasound Videos
title_full_unstemmed Predicting the Efficacy of Neoadjuvant Chemotherapy for Pancreatic Cancer Using Deep Learning of Contrast-Enhanced Ultrasound Videos
title_short Predicting the Efficacy of Neoadjuvant Chemotherapy for Pancreatic Cancer Using Deep Learning of Contrast-Enhanced Ultrasound Videos
title_sort predicting the efficacy of neoadjuvant chemotherapy for pancreatic cancer using deep learning of contrast-enhanced ultrasound videos
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10341263/
https://www.ncbi.nlm.nih.gov/pubmed/37443577
http://dx.doi.org/10.3390/diagnostics13132183
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