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Can a proposed double branch multimodality-contribution-aware TripNet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples?

OBJECTIVES: To evaluate the potential improvement of prediction performance of a proposed double branch multimodality-contribution-aware TripNet (MCAT) in microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on a small sample. METHODS: In this retrospective study, 121 HCCs from 103 c...

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Autores principales: Deng, Yuhui, Jia, Xibin, Yu, Gaoyuan, Hou, Jian, Xu, Hui, Ren, Ahong, Wang, Zhenchang, Yang, Dawei, Yang, Zhenghan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640917/
https://www.ncbi.nlm.nih.gov/pubmed/36387069
http://dx.doi.org/10.3389/fonc.2022.1035775
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author Deng, Yuhui
Jia, Xibin
Yu, Gaoyuan
Hou, Jian
Xu, Hui
Ren, Ahong
Wang, Zhenchang
Yang, Dawei
Yang, Zhenghan
author_facet Deng, Yuhui
Jia, Xibin
Yu, Gaoyuan
Hou, Jian
Xu, Hui
Ren, Ahong
Wang, Zhenchang
Yang, Dawei
Yang, Zhenghan
author_sort Deng, Yuhui
collection PubMed
description OBJECTIVES: To evaluate the potential improvement of prediction performance of a proposed double branch multimodality-contribution-aware TripNet (MCAT) in microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on a small sample. METHODS: In this retrospective study, 121 HCCs from 103 consecutive patients were included, with 44 MVI positive and 77 MVI negative, respectively. A MCAT model aiming to improve the accuracy of deep neural network and alleviate the negative effect of small sample size was proposed and the improvement of MCAT model was verified among comparisons between MCAT and other used deep neural networks including 2DCNN (two-dimentional convolutional neural network), ResNet (residual neural network) and SENet (squeeze-and-excitation network), respectively. RESULTS: Through validation, the AUC value of MCAT is significantly higher than 2DCNN based on CT, MRI, and both imaging (P < 0.001 for all). The AUC value of model with single branch pretraining based on small samples is significantly higher than model with end-to-end training in CT branch and double branch (0.62 vs 0.69, p=0.016, 0.65 vs 0.83, p=0.010, respectively). The AUC value of the double branch MCAT based on both CT and MRI imaging (0.83) was significantly higher than that of the CT branch MCAT (0.69) and MRI branch MCAT (0.73) (P < 0.001, P = 0.03, respectively), which was also significantly higher than common-used ReNet (0.67) and SENet (0.70) model (P < 0.001, P = 0.005, respectively). CONCLUSION: A proposed Double branch MCAT model based on a small sample can improve the effectiveness in comparison to other deep neural networks or single branch MCAT model, providing a potential solution for scenarios such as small-sample deep learning and fusion of multiple imaging modalities.
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spelling pubmed-96409172022-11-15 Can a proposed double branch multimodality-contribution-aware TripNet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples? Deng, Yuhui Jia, Xibin Yu, Gaoyuan Hou, Jian Xu, Hui Ren, Ahong Wang, Zhenchang Yang, Dawei Yang, Zhenghan Front Oncol Oncology OBJECTIVES: To evaluate the potential improvement of prediction performance of a proposed double branch multimodality-contribution-aware TripNet (MCAT) in microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on a small sample. METHODS: In this retrospective study, 121 HCCs from 103 consecutive patients were included, with 44 MVI positive and 77 MVI negative, respectively. A MCAT model aiming to improve the accuracy of deep neural network and alleviate the negative effect of small sample size was proposed and the improvement of MCAT model was verified among comparisons between MCAT and other used deep neural networks including 2DCNN (two-dimentional convolutional neural network), ResNet (residual neural network) and SENet (squeeze-and-excitation network), respectively. RESULTS: Through validation, the AUC value of MCAT is significantly higher than 2DCNN based on CT, MRI, and both imaging (P < 0.001 for all). The AUC value of model with single branch pretraining based on small samples is significantly higher than model with end-to-end training in CT branch and double branch (0.62 vs 0.69, p=0.016, 0.65 vs 0.83, p=0.010, respectively). The AUC value of the double branch MCAT based on both CT and MRI imaging (0.83) was significantly higher than that of the CT branch MCAT (0.69) and MRI branch MCAT (0.73) (P < 0.001, P = 0.03, respectively), which was also significantly higher than common-used ReNet (0.67) and SENet (0.70) model (P < 0.001, P = 0.005, respectively). CONCLUSION: A proposed Double branch MCAT model based on a small sample can improve the effectiveness in comparison to other deep neural networks or single branch MCAT model, providing a potential solution for scenarios such as small-sample deep learning and fusion of multiple imaging modalities. Frontiers Media S.A. 2022-10-24 /pmc/articles/PMC9640917/ /pubmed/36387069 http://dx.doi.org/10.3389/fonc.2022.1035775 Text en Copyright © 2022 Deng, Jia, Yu, Hou, Xu, Ren, Wang, Yang and Yang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Deng, Yuhui
Jia, Xibin
Yu, Gaoyuan
Hou, Jian
Xu, Hui
Ren, Ahong
Wang, Zhenchang
Yang, Dawei
Yang, Zhenghan
Can a proposed double branch multimodality-contribution-aware TripNet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples?
title Can a proposed double branch multimodality-contribution-aware TripNet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples?
title_full Can a proposed double branch multimodality-contribution-aware TripNet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples?
title_fullStr Can a proposed double branch multimodality-contribution-aware TripNet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples?
title_full_unstemmed Can a proposed double branch multimodality-contribution-aware TripNet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples?
title_short Can a proposed double branch multimodality-contribution-aware TripNet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples?
title_sort can a proposed double branch multimodality-contribution-aware tripnet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples?
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640917/
https://www.ncbi.nlm.nih.gov/pubmed/36387069
http://dx.doi.org/10.3389/fonc.2022.1035775
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