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
奥利给完成签到,获得积分10
刚刚
明明完成签到 ,获得积分10
1秒前
芹菜自愿内卷完成签到,获得积分10
1秒前
zokor完成签到 ,获得积分0
4秒前
努力退休小博士完成签到 ,获得积分10
5秒前
橙子完成签到,获得积分10
6秒前
陈补天完成签到 ,获得积分10
7秒前
CipherSage应助慧灰huihui采纳,获得10
8秒前
乐观健柏完成签到,获得积分10
9秒前
11秒前
CodeCraft应助大橙子采纳,获得10
11秒前
量子星尘发布了新的文献求助10
12秒前
jeeya完成签到,获得积分10
13秒前
15秒前
科目三应助科研通管家采纳,获得10
15秒前
科目三应助科研通管家采纳,获得10
15秒前
伦语发布了新的文献求助10
15秒前
顾矜应助科研通管家采纳,获得10
15秒前
xuzj应助科研通管家采纳,获得10
15秒前
xuzj应助科研通管家采纳,获得10
15秒前
15秒前
NexusExplorer应助科研通管家采纳,获得10
15秒前
15秒前
15秒前
丘比特应助科研通管家采纳,获得10
16秒前
yull完成签到,获得积分10
16秒前
小巧书雪完成签到,获得积分10
19秒前
大大怪将军完成签到,获得积分10
20秒前
哈哈哈完成签到 ,获得积分0
20秒前
小怪完成签到,获得积分10
21秒前
爱吃泡芙完成签到,获得积分10
22秒前
白桃战士完成签到,获得积分10
23秒前
25秒前
qingchenwuhou完成签到 ,获得积分10
25秒前
XXX完成签到,获得积分10
26秒前
锡嘻完成签到 ,获得积分10
26秒前
27秒前
彗星入梦完成签到 ,获得积分10
27秒前
恋恋青葡萄完成签到,获得积分10
27秒前
隐形万言完成签到,获得积分10
29秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
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 4038201
求助须知:如何正确求助?哪些是违规求助? 3575940
关于积分的说明 11373987
捐赠科研通 3305747
什么是DOI,文献DOI怎么找? 1819274
邀请新用户注册赠送积分活动 892662
科研通“疑难数据库(出版商)”最低求助积分说明 815022