已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Machine learning for lung CT texture analysis: Improvement of inter-observer agreement for radiological finding classification in patients with pulmonary diseases

医学 麦克内马尔试验 放射科 放射性武器 核医学 软件 统计 计算机科学 数学 程序设计语言
作者
Yoshiharu Ohno,Kota Aoyagi,Daisuke Takenaka,Takeshi Yoshikawa,Aina Ikezaki,Yasuko Fujisawa,Kazuhiro Murayama,Hidekazu Hattori,Hiroshi Toyama
出处
期刊:European Journal of Radiology [Elsevier BV]
卷期号:134: 109410-109410 被引量:22
标识
DOI:10.1016/j.ejrad.2020.109410
摘要

Abstract

Purpose

To evaluate the capability ML-based CT texture analysis for improving interobserver agreement and accuracy of radiological finding assessment in patients with COPD, interstitial lung diseases or infectious diseases.

Materials and methods

Training cases (n = 28), validation cases (n = 17) and test cases (n = 89) who underwent thin-section CT at a 320-detector row CT with wide volume scan and two 64-detector row CTs with helical scan were enrolled in this study. From 89 CT data, a total of 350 computationally selected ROI including normal lung, emphysema, nodular lesion, ground-glass opacity, reticulation and honeycomb were evaluated by three radiologists as well as by the software. Inter-observer agreements between consensus reading with and without using the software or software alone and standard references determined by consensus of pulmonologists and chest radiologists were determined using κ statistics. Overall distinguishing accuracies were compared among all methods by McNemar's test.

Results

Agreements for consensus readings obtained with and without the software or the software alone with standard references were determined as significant and substantial or excellent (with the software: κ = 0.91, p < 0.0001; without the software: κ = 0.81, p < 0.0001; the software alone: κ = 0.79, p < 0.0001). Overall differentiation accuracy of consensus reading using the software (94.9 [332/350] %) was significantly higher than that of consensus reading without using the software (84.3 [295/350] %, p < 0.0001) and the software alone (82.3 [288/350] %, p < 0.0001).

Conclusion

ML-based CT texture analysis software has potential for improving interobserver agreement and accuracy for radiological finding assessments in patients with COPD, interstitial lung diseases or infectious diseases.
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
乌苏完成签到 ,获得积分10
1秒前
1234发布了新的文献求助10
1秒前
Orange应助斜玉采纳,获得30
1秒前
3秒前
4秒前
xiaomu发布了新的文献求助30
9秒前
上官若男应助yuan采纳,获得10
9秒前
搜集达人应助含糊的文涛采纳,获得10
10秒前
orixero应助胡萝卜采纳,获得10
12秒前
15秒前
李健应助SUE采纳,获得10
15秒前
奋斗金连完成签到,获得积分10
17秒前
18秒前
pluvia完成签到,获得积分10
18秒前
漂亮白枫发布了新的文献求助10
19秒前
19秒前
香蕉觅云应助土豆采纳,获得10
20秒前
wkjfh应助碧蓝的宝马采纳,获得10
20秒前
胡萝卜完成签到,获得积分10
21秒前
22秒前
我是老大应助水之冬采纳,获得10
24秒前
fang完成签到 ,获得积分10
24秒前
24秒前
24秒前
南风知我意完成签到 ,获得积分10
25秒前
26秒前
27秒前
今后应助冷傲的水儿采纳,获得10
28秒前
homie发布了新的文献求助10
29秒前
29秒前
双椒兔丁完成签到,获得积分10
29秒前
NexusExplorer应助Ramer556采纳,获得10
30秒前
猪蹄发布了新的文献求助10
31秒前
直觉发布了新的文献求助10
32秒前
土豆发布了新的文献求助10
33秒前
一直向前发布了新的文献求助10
37秒前
37秒前
丘比特应助十四采纳,获得10
39秒前
39秒前
霜序完成签到,获得积分10
40秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
Aktuelle Entwicklungen in der linguistischen Forschung 300
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 3989868
求助须知:如何正确求助?哪些是违规求助? 3531994
关于积分的说明 11255752
捐赠科研通 3270793
什么是DOI,文献DOI怎么找? 1805053
邀请新用户注册赠送积分活动 882215
科研通“疑难数据库(出版商)”最低求助积分说明 809208