Cargando…
Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies
Achieving high feature reproducibility while preserving biological information is one of the main challenges for the generalizability of current radiomics studies. Non-clinical imaging variables, such as reconstruction kernels, have shown to significantly impact radiomics features. In this study, we...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8707549/ https://www.ncbi.nlm.nih.gov/pubmed/34941646 http://dx.doi.org/10.3390/tomography7040074 |
_version_ | 1784622463649316864 |
---|---|
author | Yoon, Jin H. Sun, Shawn H. Xiao, Manjun Yang, Hao Lu, Lin Li, Yajun Schwartz, Lawrence H. Zhao, Binsheng |
author_facet | Yoon, Jin H. Sun, Shawn H. Xiao, Manjun Yang, Hao Lu, Lin Li, Yajun Schwartz, Lawrence H. Zhao, Binsheng |
author_sort | Yoon, Jin H. |
collection | PubMed |
description | Achieving high feature reproducibility while preserving biological information is one of the main challenges for the generalizability of current radiomics studies. Non-clinical imaging variables, such as reconstruction kernels, have shown to significantly impact radiomics features. In this study, we retrain an open-source convolutional neural network (CNN) to harmonize computerized tomography (CT) images with various reconstruction kernels to improve feature reproducibility and radiomic model performance using epidermal growth factor receptor (EGFR) mutation prediction in lung cancer as a paradigm. In the training phase, the CNN was retrained and tested on 32 lung cancer patients’ CT images between two different groups of reconstruction kernels (smooth and sharp). In the validation phase, the retrained CNN was validated on an external cohort of 223 lung cancer patients’ CT images acquired using different CT scanners and kernels. The results showed that the retrained CNN could be successfully applied to external datasets with different CT scanner parameters, and harmonization of reconstruction kernels from sharp to smooth could significantly improve the performance of radiomics model in predicting EGFR mutation status in lung cancer. In conclusion, the CNN based method showed great potential in improving feature reproducibility and generalizability by harmonizing medical images with heterogeneous reconstruction kernels. |
format | Online Article Text |
id | pubmed-8707549 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87075492021-12-25 Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies Yoon, Jin H. Sun, Shawn H. Xiao, Manjun Yang, Hao Lu, Lin Li, Yajun Schwartz, Lawrence H. Zhao, Binsheng Tomography Article Achieving high feature reproducibility while preserving biological information is one of the main challenges for the generalizability of current radiomics studies. Non-clinical imaging variables, such as reconstruction kernels, have shown to significantly impact radiomics features. In this study, we retrain an open-source convolutional neural network (CNN) to harmonize computerized tomography (CT) images with various reconstruction kernels to improve feature reproducibility and radiomic model performance using epidermal growth factor receptor (EGFR) mutation prediction in lung cancer as a paradigm. In the training phase, the CNN was retrained and tested on 32 lung cancer patients’ CT images between two different groups of reconstruction kernels (smooth and sharp). In the validation phase, the retrained CNN was validated on an external cohort of 223 lung cancer patients’ CT images acquired using different CT scanners and kernels. The results showed that the retrained CNN could be successfully applied to external datasets with different CT scanner parameters, and harmonization of reconstruction kernels from sharp to smooth could significantly improve the performance of radiomics model in predicting EGFR mutation status in lung cancer. In conclusion, the CNN based method showed great potential in improving feature reproducibility and generalizability by harmonizing medical images with heterogeneous reconstruction kernels. MDPI 2021-12-03 /pmc/articles/PMC8707549/ /pubmed/34941646 http://dx.doi.org/10.3390/tomography7040074 Text en © 2021 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 Yoon, Jin H. Sun, Shawn H. Xiao, Manjun Yang, Hao Lu, Lin Li, Yajun Schwartz, Lawrence H. Zhao, Binsheng Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies |
title | Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies |
title_full | Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies |
title_fullStr | Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies |
title_full_unstemmed | Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies |
title_short | Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies |
title_sort | convolutional neural network addresses the confounding impact of ct reconstruction kernels on radiomics studies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8707549/ https://www.ncbi.nlm.nih.gov/pubmed/34941646 http://dx.doi.org/10.3390/tomography7040074 |
work_keys_str_mv | AT yoonjinh convolutionalneuralnetworkaddressestheconfoundingimpactofctreconstructionkernelsonradiomicsstudies AT sunshawnh convolutionalneuralnetworkaddressestheconfoundingimpactofctreconstructionkernelsonradiomicsstudies AT xiaomanjun convolutionalneuralnetworkaddressestheconfoundingimpactofctreconstructionkernelsonradiomicsstudies AT yanghao convolutionalneuralnetworkaddressestheconfoundingimpactofctreconstructionkernelsonradiomicsstudies AT lulin convolutionalneuralnetworkaddressestheconfoundingimpactofctreconstructionkernelsonradiomicsstudies AT liyajun convolutionalneuralnetworkaddressestheconfoundingimpactofctreconstructionkernelsonradiomicsstudies AT schwartzlawrenceh convolutionalneuralnetworkaddressestheconfoundingimpactofctreconstructionkernelsonradiomicsstudies AT zhaobinsheng convolutionalneuralnetworkaddressestheconfoundingimpactofctreconstructionkernelsonradiomicsstudies |