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
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
ISSN0094-2405
EISSN2473-4209
卷号52期号:2页码:1119-1132
DOI10.1002/mp.17493
页数14
WOS类目Radiology, Nuclear Medicine & Medical Imaging
WOS记录号WOS:001358131400001
收录类别PUBMED ; SCIE ; SCOPUS ; EI
EI入藏号20244617373025
EI主题词Computerized tomography
EI分类号1101.2 ; 1101.2.1 ; 1106.8 ; 713.3 Modulators, Demodulators, Limiters, Discriminators, Mixers ; 746 Imaging Techniques
URL查看原文
PubMed ID39535436
SCOPUSEID2-s2.0-85208948312
通讯作者地址[Jin, Xiance]Wenzhou Med Univ, Affiliated Hosp 1, Dept Radiotherapy Ctr, Wenzhou 325000, Peoples R China.
Scopus学科分类Biophysics;Radiology, Nuclear Medicine and Imaging
SCOPUS_IDSCOPUS_ID:85208948312
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)
附属第二医院_呼吸内科
第一临床医学院(信息与工程学院)、附属第一医院_硕士
通讯作者Jin, Xiance
作者单位
1.Wenzhou Med Univ, Affiliated Hosp 1, Dept Radiotherapy Ctr, Wenzhou, Peoples R China;
2.Wenzhou Med Univ, Affiliated Hosp 2, Dept Resp Med, Wenzhou, Peoples R China;
3.Wenzhou Med Univ, Sch Basic Med Sci, Wenzhou, Peoples R 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
Jin, Juebin,Zhang, Jicheng,Yu, Xianwen,et al. Radiomics-guided generative adversarial network for automatic primary target volume segmentation for nasopharyngeal carcinoma using computed tomography images[J]. Medical physics,2024,52(2):1119-1132.
APA Jin, Juebin., Zhang, Jicheng., Yu, Xianwen., Xiang, Ziqing., Zhu, Xuanxuan., ... & Jin, Xiance. (2024). Radiomics-guided generative adversarial network for automatic primary target volume segmentation for nasopharyngeal carcinoma using computed tomography images. Medical physics, 52(2), 1119-1132.
MLA Jin, Juebin,et al."Radiomics-guided generative adversarial network for automatic primary target volume segmentation for nasopharyngeal carcinoma using computed tomography images".Medical physics 52.2(2024):1119-1132.

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