Radiomics-guided generative adversarial network for automatic primary target volume segmentation for nasopharyngeal carcinoma using computed tomography images

doi:10.1002/mp.17493

科研成果详情

题名	Radiomics-guided generative adversarial network for automatic primary target volume segmentation for nasopharyngeal carcinoma using computed tomography images
作者	Juebin Jin1; Jicheng Zhang1; Xianwen Yu1; Ziqing Xiang2; Xuanxuan Zhu1; Mingrou Guo 1; Zeshuo Zhao1; WenLong Li 1; Heng Li1; Jiayi Xu 1; Xiance Jin1,3
发表日期	2024-11-13
发表期刊	Medical physics 影响因子和分区
语种	英语
原始文献类型	Journal Article
关键词	adversarial learning computed tomography (CT) nasopharyngeal carcinoma segmentation radiomics
其他关键词	GROSS TUMOR VOLUME ; RADIOTHERAPY ; DELINEATION ; QUALITY ; SYSTEM
摘要	Automatic primary gross tumor volume (GTVp) segmentation for nasopharyngeal carcinoma (NPC) is a quite challenging task because of the existence of similar visual characteristics between tumors and their surroundings, especially on computed tomography (CT) images with severe low contrast resolution. Therefore, most recently proposed methods based on radiomics or deep learning (DL) is difficult to achieve good results on CT datasets., A peritumoral radiomics-guided generative adversarial network (PRG-GAN) was proposed to address this challenge., A total of 157 NPC patients with CT images was collected and divided into training, validation, and testing cohorts of 108, 9, and 30 patients, respectively. The proposed model was based on a standard GAN consisting of a generator network and a discriminator network. Morphological dilation on the initial segmentation results from GAN was first conducted to delineate annular peritumoral region, in which radiomics features were extracted as priori guide knowledge. Then, radiomics features were fused with semantic features by the discriminator's fully connected layer to achieve the voxel-level classification and segmentation. The dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD) were used to evaluate the segmentation performance using a paired samples t-test with Bonferroni correction and Cohen's d (d) effect sizes. A two-sided p-value of less than 0.05 was considered statistically significant., The model-generated predictions had a high overlap ratio with the ground truth. The average DSC, HD95, and ASSD were significantly improved from 0.80 ± 0.12, 4.65 ± 4.71 mm, and 1.35 ± 1.15 mm of GAN to 0.85 ± 0.18 (p = 0.001, d = 0.71), 4.15 ± 7.56 mm (p = 0.002, d = 0.67), and 1.11 ± 1.65 mm (p < 0.001, d = 0.46) of PRG-GAN, respectively., Integrating radiomics features into GAN is promising to solve unclear border limitations and increase the delineation accuracy of GTVp for patients with NPC.
资助项目	National Natural Science Foundation[12475352];Key project of Zhejiang Natural Science Foundation[Z24A050009];Key project of Zhejiang Provincial Health Science and Technology Program[WKJ-ZJ-2437];Major project of Wenzhou Science and Technology Bureau[ZY2022016];Major project of Wenzhou Science and Technology Bureau[ZY2020011];Wenzhou Science and Technology Bureau[Y2023798]
出版者	WILEY
ISSN	0094-2405
EISSN	2473-4209
DOI	10.1002/mp.17493
页数	14
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
收录类别	PUBMED ; SCIE
URL	查看原文
PubMed ID	39535436
通讯作者地址	[Jin, Xiance]Wenzhou Med Univ, Affiliated Hosp 1, Dept Radiotherapy Ctr, Wenzhou 325000, Peoples R China.
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/221616
专题	附属第一医院温州医科大学基础医学院（机能实验教学中心）第一临床医学院（信息与工程学院）、附属第一医院_硕士
作者单位	1.Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.; 2.Department of Medical Engineering, 2nd Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.; 3.School of Basic Medical Science, Wenzhou Medical University, Wenzhou, China.
第一作者单位	附属第一医院
通讯作者单位	附属第一医院
第一作者的第一单位	附属第一医院
推荐引用方式 GB/T 7714	Juebin Jin,Jicheng Zhang,Xianwen Yu,et al. Radiomics-guided generative adversarial network for automatic primary target volume segmentation for nasopharyngeal carcinoma using computed tomography images[J]. Medical physics,2024.
APA	Juebin Jin., Jicheng Zhang., Xianwen Yu., Ziqing Xiang., Xuanxuan Zhu., ... & Xiance Jin. (2024). Radiomics-guided generative adversarial network for automatic primary target volume segmentation for nasopharyngeal carcinoma using computed tomography images. Medical physics.
MLA	Juebin Jin,et al."Radiomics-guided generative adversarial network for automatic primary target volume segmentation for nasopharyngeal carcinoma using computed tomography images".Medical physics (2024).

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