Deep Learning–based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses

医学 人工智能 再现性 无线电技术 放射科 核医学 计算机科学 数学 统计
作者
Jooae Choe,Sang Min Lee,Kyung‐Hyun Do,Gaeun Lee,June‐Goo Lee,Sang Min Lee,Joon Beom Seo
出处
期刊:Radiology [Radiological Society of North America]
卷期号:292 (2): 365-373 被引量:253
标识
DOI:10.1148/radiol.2019181960
摘要

Background Intratumor heterogeneity in lung cancer may influence outcomes. CT radiomics seeks to assess tumor features to provide detailed imaging features. However, CT radiomic features vary according to the reconstruction kernel used for image generation. Purpose To investigate the effect of different reconstruction kernels on radiomic features and assess whether image conversion using a convolutional neural network (CNN) could improve reproducibility of radiomic features between different kernels. Materials and Methods In this retrospective analysis, patients underwent non–contrast material–enhanced and contrast material–enhanced axial chest CT with soft kernel (B30f) and sharp kernel (B50f) reconstruction using a single CT scanner from April to June 2017. To convert different kernels without sinogram, the CNN model was developed using residual learning and an end-to-end way. Kernel-converted images were generated, from B30f to B50f and from B50f to B30f. Pulmonary nodules or masses were semiautomatically segmented and 702 radiomic features (tumor intensity, texture, and wavelet features) were extracted. Measurement variability in radiomic features was evaluated using the concordance correlation coefficient (CCC). Results A total of 104 patients were studied, including 54 women and 50 men, with pulmonary nodules or masses (mean age, 63.2 years ± 10.5). The CCC between two readers using the same kernel was 0.92, and 592 of 702 (84.3%) of the radiomic features were reproducible (CCC ≥ 0.85); using different kernels, the CCC was 0.38 and only 107 of 702 (15.2%) of the radiomic features were reliable. Texture features and wavelet features were predominantly affected by reconstruction kernel (CCC, from 0.88 to 0.61 for texture features and from 0.92 to 0.35 for wavelet features). After applying image conversion, CCC improved to 0.84 and 403 of 702 (57.4%) radiomic features were reproducible (CCC, 0.85 for texture features and 0.84 for wavelet features). Conclusion Chest CT image conversion using a convolutional neural network effectively reduced the effect of two different reconstruction kernels and may improve the reproducibility of radiomic features in pulmonary nodules or masses. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Park in this issue.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
lsy完成签到,获得积分10
1秒前
Sensons完成签到,获得积分10
1秒前
嘟嘟发布了新的文献求助10
2秒前
3秒前
金旭完成签到,获得积分10
4秒前
RG完成签到,获得积分10
4秒前
4秒前
5秒前
FLZLC发布了新的文献求助10
6秒前
长白完成签到,获得积分10
6秒前
6秒前
6秒前
诸天蓉发布了新的文献求助10
6秒前
敏哇哇哇发布了新的文献求助10
7秒前
8秒前
许雨青发布了新的文献求助30
8秒前
09chenyun完成签到,获得积分10
9秒前
暖暖完成签到,获得积分10
10秒前
刘尹发布了新的文献求助30
10秒前
10秒前
tuanheqi应助松鼠15111采纳,获得100
11秒前
pl脆脆发布了新的文献求助10
11秒前
12秒前
汤圆完成签到,获得积分10
14秒前
14秒前
领导范儿应助明亮依琴采纳,获得10
15秒前
16秒前
16秒前
胡浩完成签到,获得积分10
16秒前
敏哇哇哇完成签到,获得积分10
16秒前
雪兔妹妹完成签到 ,获得积分10
16秒前
17秒前
17秒前
17秒前
英俊的铭应助汤圆采纳,获得10
18秒前
18秒前
Owen应助松鼠15111采纳,获得10
19秒前
814791097完成签到,获得积分10
19秒前
Jasper应助lpj采纳,获得10
19秒前
123321发布了新的文献求助10
19秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Toward a Combinatorial Approach for the Prediction of IgG Half-Life and Clearance 500
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3970008
求助须知:如何正确求助?哪些是违规求助? 3514711
关于积分的说明 11175563
捐赠科研通 3250077
什么是DOI,文献DOI怎么找? 1795198
邀请新用户注册赠送积分活动 875630
科研通“疑难数据库(出版商)”最低求助积分说明 804931