基于深度学习分割模型的COVID-19肺部病变在低剂量胸部CT上的价值及对预后的影响

Axel Bartoli MD , Joris Fournel , Arnaud Maurin MD , Baptiste Marchi MD , Paul Habert MD , Maxime Castelli MD , Jean-Yves Gaubert MD , Sebastien Cortaredona MD , Jean-Christophe Lagier MD, PhD , Matthieu Million MD, PhD , Didier Raoult MD, PhD , Badih Ghattas MCU , Alexis Jacquier MD, PhD
{"title":"基于深度学习分割模型的COVID-19肺部病变在低剂量胸部CT上的价值及对预后的影响","authors":"Axel Bartoli MD ,&nbsp;Joris Fournel ,&nbsp;Arnaud Maurin MD ,&nbsp;Baptiste Marchi MD ,&nbsp;Paul Habert MD ,&nbsp;Maxime Castelli MD ,&nbsp;Jean-Yves Gaubert MD ,&nbsp;Sebastien Cortaredona MD ,&nbsp;Jean-Christophe Lagier MD, PhD ,&nbsp;Matthieu Million MD, PhD ,&nbsp;Didier Raoult MD, PhD ,&nbsp;Badih Ghattas MCU ,&nbsp;Alexis Jacquier MD, PhD","doi":"10.1016/j.redii.2022.100003","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><p>1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification.</p></div><div><h3>Methods</h3><p>This monocentric retrospective study included training and test datasets taken from 144 and 30 patients, respectively. The reference was the manual segmentation of 3 labels: normal lung, ground-glass opacity(GGO) and consolidation(Cons). Model performance was evaluated with technical metrics, disease volume and extent. Intra- and interobserver agreement were recorded. The prognostic value of DL-driven disease extent was assessed in 1621 distinct patients using C-statistics. The end point was a combined outcome defined as death, hospitalization&gt;10 days, intensive care unit hospitalization or oxygen therapy.</p></div><div><h3>Results</h3><p>The Dice coefficients for lesion (GGO+Cons) segmentations were 0.75±0.08, exceeding the values for human interobserver (0.70±0.08; 0.70±0.10) and intraobserver measures (0.72±0.09). DL-driven lesion quantification had a stronger correlation with the reference than inter- or intraobserver measures. After stepwise selection and adjustment for clinical characteristics, quantification significantly increased the prognostic accuracy of the model (0.82 vs. 0.90; <em>p</em>&lt;0.0001).</p></div><div><h3>Conclusions</h3><p>A DL-driven model can provide reproducible and accurate segmentation of COVID-19 lesions on LDCT. Automatic lesion quantification has independent prognostic value for the identification of high-risk patients.</p></div>","PeriodicalId":74676,"journal":{"name":"Research in diagnostic and interventional imaging","volume":"1 ","pages":"Article 100003"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939894/pdf/","citationCount":"4","resultStr":"{\"title\":\"Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT\",\"authors\":\"Axel Bartoli MD ,&nbsp;Joris Fournel ,&nbsp;Arnaud Maurin MD ,&nbsp;Baptiste Marchi MD ,&nbsp;Paul Habert MD ,&nbsp;Maxime Castelli MD ,&nbsp;Jean-Yves Gaubert MD ,&nbsp;Sebastien Cortaredona MD ,&nbsp;Jean-Christophe Lagier MD, PhD ,&nbsp;Matthieu Million MD, PhD ,&nbsp;Didier Raoult MD, PhD ,&nbsp;Badih Ghattas MCU ,&nbsp;Alexis Jacquier MD, PhD\",\"doi\":\"10.1016/j.redii.2022.100003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objectives</h3><p>1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification.</p></div><div><h3>Methods</h3><p>This monocentric retrospective study included training and test datasets taken from 144 and 30 patients, respectively. The reference was the manual segmentation of 3 labels: normal lung, ground-glass opacity(GGO) and consolidation(Cons). Model performance was evaluated with technical metrics, disease volume and extent. Intra- and interobserver agreement were recorded. The prognostic value of DL-driven disease extent was assessed in 1621 distinct patients using C-statistics. The end point was a combined outcome defined as death, hospitalization&gt;10 days, intensive care unit hospitalization or oxygen therapy.</p></div><div><h3>Results</h3><p>The Dice coefficients for lesion (GGO+Cons) segmentations were 0.75±0.08, exceeding the values for human interobserver (0.70±0.08; 0.70±0.10) and intraobserver measures (0.72±0.09). DL-driven lesion quantification had a stronger correlation with the reference than inter- or intraobserver measures. After stepwise selection and adjustment for clinical characteristics, quantification significantly increased the prognostic accuracy of the model (0.82 vs. 0.90; <em>p</em>&lt;0.0001).</p></div><div><h3>Conclusions</h3><p>A DL-driven model can provide reproducible and accurate segmentation of COVID-19 lesions on LDCT. Automatic lesion quantification has independent prognostic value for the identification of high-risk patients.</p></div>\",\"PeriodicalId\":74676,\"journal\":{\"name\":\"Research in diagnostic and interventional imaging\",\"volume\":\"1 \",\"pages\":\"Article 100003\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939894/pdf/\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research in diagnostic and interventional imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772652522000035\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in diagnostic and interventional imaging","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772652522000035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

目的1)建立一种深度学习(DL)管道,用于在低剂量计算机断层扫描(LDCT)上量化COVID-19肺部病变。2)评价dl驱动病变量化的预后价值。方法本单中心回顾性研究包括144例和30例患者的训练和测试数据集。参照手工分割3个标签:正常肺、磨玻璃不透明(GGO)和实变(Cons)。用技术指标、疾病量和程度评价模型的性能。记录了观察员内部和观察员之间的一致意见。采用C-statistics对1621例不同类型患者的dl驱动病变程度进行预后评估。终点是一个综合结果,定义为死亡、住院10天、重症监护病房住院或氧气治疗。结果病变(GGO+ con)分割的Dice系数为0.75±0.08,超过了人类观察者间的数值(0.70±0.08;0.70±0.10)和观察者内测量值(0.72±0.09)。dl驱动的病变量化与参考的相关性比观察者间或观察者内测量的相关性更强。在逐步选择和调整临床特征后,量化显著提高了模型的预后准确性(0.82 vs 0.90;术中,0.0001)。结论dl驱动模型可在LDCT上对COVID-19病变进行可重复、准确的分割。病变自动量化对高危患者的识别具有独立的预后价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT

Objectives

1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification.

Methods

This monocentric retrospective study included training and test datasets taken from 144 and 30 patients, respectively. The reference was the manual segmentation of 3 labels: normal lung, ground-glass opacity(GGO) and consolidation(Cons). Model performance was evaluated with technical metrics, disease volume and extent. Intra- and interobserver agreement were recorded. The prognostic value of DL-driven disease extent was assessed in 1621 distinct patients using C-statistics. The end point was a combined outcome defined as death, hospitalization>10 days, intensive care unit hospitalization or oxygen therapy.

Results

The Dice coefficients for lesion (GGO+Cons) segmentations were 0.75±0.08, exceeding the values for human interobserver (0.70±0.08; 0.70±0.10) and intraobserver measures (0.72±0.09). DL-driven lesion quantification had a stronger correlation with the reference than inter- or intraobserver measures. After stepwise selection and adjustment for clinical characteristics, quantification significantly increased the prognostic accuracy of the model (0.82 vs. 0.90; p<0.0001).

Conclusions

A DL-driven model can provide reproducible and accurate segmentation of COVID-19 lesions on LDCT. Automatic lesion quantification has independent prognostic value for the identification of high-risk patients.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Possible limited justification for systematic head computed tomography scans based solely on antithrombotic therapy in elderly patients (aged 75 or older) with mild traumatic brain injury Dedicated software to harmonize the follow-up of oncological patients Glenoid morphology variation between patients with hypermobile shoulder joints and controls: Identification of hyperlaxity-related morphologic bone changes Efficacity of CT-guided intra-articular cervical facet steroid injection for cervical radiculopathy Evaluation of a new beads reflux control microcatheter in drug-eluting bead transarterial chemoembolization
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1