Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

Abstract Purpose This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs). Methods We ret...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xiaonan Shao, Rong Niu, Xiaoliang Shao, Jianxiong Gao, Yunmei Shi, Zhenxing Jiang, Yuetao Wang
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Lung adenocarcinoma Differential diagnosis Deep learning Fluorodeoxyglucose F18 Positron emission tomography-computed tomography Medical physics. Medical radiology. Nuclear medicine R895-920
Acceso en línea:	https://doaj.org/article/9441fcd5608447b39f27ca47d353e6d6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9441fcd5608447b39f27ca47d353e6d6
record_format	dspace
spelling	oai:doaj.org-article:9441fcd5608447b39f27ca47d353e6d62021-11-08T10:57:40ZApplication of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules10.1186/s40658-021-00423-12197-7364https://doaj.org/article/9441fcd5608447b39f27ca47d353e6d62021-11-01T00:00:00Zhttps://doi.org/10.1186/s40658-021-00423-1https://doaj.org/toc/2197-7364Abstract Purpose This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs). Methods We retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks). Results A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372–1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73]. Conclusion The 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.Xiaonan ShaoRong NiuXiaoliang ShaoJianxiong GaoYunmei ShiZhenxing JiangYuetao WangSpringerOpenarticleLung adenocarcinomaDifferential diagnosisDeep learningFluorodeoxyglucose F18Positron emission tomography-computed tomographyMedical physics. Medical radiology. Nuclear medicineR895-920ENEJNMMI Physics, Vol 8, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Lung adenocarcinoma Differential diagnosis Deep learning Fluorodeoxyglucose F18 Positron emission tomography-computed tomography Medical physics. Medical radiology. Nuclear medicine R895-920
spellingShingle	Lung adenocarcinoma Differential diagnosis Deep learning Fluorodeoxyglucose F18 Positron emission tomography-computed tomography Medical physics. Medical radiology. Nuclear medicine R895-920 Xiaonan Shao Rong Niu Xiaoliang Shao Jianxiong Gao Yunmei Shi Zhenxing Jiang Yuetao Wang Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules
description	Abstract Purpose This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs). Methods We retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks). Results A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372–1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73]. Conclusion The 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.
format	article
author	Xiaonan Shao Rong Niu Xiaoliang Shao Jianxiong Gao Yunmei Shi Zhenxing Jiang Yuetao Wang
author_facet	Xiaonan Shao Rong Niu Xiaoliang Shao Jianxiong Gao Yunmei Shi Zhenxing Jiang Yuetao Wang
author_sort	Xiaonan Shao
title	Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules
title_short	Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules
title_full	Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules
title_fullStr	Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules
title_full_unstemmed	Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules
title_sort	application of dual-stream 3d convolutional neural network based on 18f-fdg pet/ct in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/9441fcd5608447b39f27ca47d353e6d6
work_keys_str_mv	AT xiaonanshao applicationofdualstream3dconvolutionalneuralnetworkbasedon18ffdgpetctindistinguishingbenignandinvasiveadenocarcinomaingroundglasslungnodules AT rongniu applicationofdualstream3dconvolutionalneuralnetworkbasedon18ffdgpetctindistinguishingbenignandinvasiveadenocarcinomaingroundglasslungnodules AT xiaoliangshao applicationofdualstream3dconvolutionalneuralnetworkbasedon18ffdgpetctindistinguishingbenignandinvasiveadenocarcinomaingroundglasslungnodules AT jianxionggao applicationofdualstream3dconvolutionalneuralnetworkbasedon18ffdgpetctindistinguishingbenignandinvasiveadenocarcinomaingroundglasslungnodules AT yunmeishi applicationofdualstream3dconvolutionalneuralnetworkbasedon18ffdgpetctindistinguishingbenignandinvasiveadenocarcinomaingroundglasslungnodules AT zhenxingjiang applicationofdualstream3dconvolutionalneuralnetworkbasedon18ffdgpetctindistinguishingbenignandinvasiveadenocarcinomaingroundglasslungnodules AT yuetaowang applicationofdualstream3dconvolutionalneuralnetworkbasedon18ffdgpetctindistinguishingbenignandinvasiveadenocarcinomaingroundglasslungnodules
_version_	1718442448357687296

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

Ejemplares similares