Exhaled volatile organic compounds identify patients with colorectal cancer

医学 结直肠癌 接收机工作特性 癌症 内科学 气体分析呼吸 生物标志物 线性判别分析 呼吸试验 结肠镜检查 人口 胃肠病学 人工智能 化学 解剖 环境卫生 幽门螺杆菌 生物化学 计算机科学
作者
Donato F. Altomare,M. Di Lena,Francesca Porcelli,L. Trizio,E Travaglio,Maria Tutino,Silvano Dragonieri,V. Memeo,Gianluigi de Gennaro
出处
期刊:British Journal of Surgery [Oxford University Press]
卷期号:100 (1): 144-150 被引量:260
标识
DOI:10.1002/bjs.8942
摘要

Abstract Background An effective screening tool for colorectal cancer is still lacking. Analysis of the volatile organic compounds (VOCs) linked to cancer is a new frontier in cancer screening, as tumour growth involves several metabolic changes leading to the production of specific compounds that can be detected in exhaled breath. This study investigated whether patients with colorectal cancer have a specific VOC pattern compared with the healthy population. Methods Exhaled breath was collected in an inert bag (Tedlar®) from patients with colorectal cancer and healthy controls (negative at colonoscopy), and processed offline by thermal-desorber gas chromatography–mass spectrometry to evaluate the VOC profile. During the trial phase VOCs of interest were identified and selected, and VOC patterns able to discriminate patients from controls were set up; in the validation phase their discriminant performance was tested on blinded samples. A probabilistic neural network (PNN) validated by the leave-one-out method was used to identify the pattern of VOCs that better discriminated between the two groups. Results Some 37 patients and 41 controls were included in the trial phase. Application of a PNN to a pattern of 15 compounds showed a discriminant performance with a sensitivity of 86 per cent, a specificity of 83 per cent and an accuracy of 85 per cent (area under the receiver operating characteristic (ROC) curve 0·852). The accuracy of PNN analysis was confirmed in the validation phase on a further 25 subjects; the model correctly assigned 19 patients, giving an overall accuracy of 76 per cent. Conclusion The pattern of VOCs in patients with colorectal cancer was different from that in healthy controls. The PNN in this study was able to discriminate patients with colorectal cancer with an accuracy of over 75 per cent. Breath VOC analysis appears to have potential clinical application in colorectal cancer screening, although further studies are required to confirm its reliability in heterogeneous clinical settings.

科研通智能强力驱动
Strongly Powered by AbleSci AI
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
刚刚
1秒前
小白系列产品完成签到,获得积分20
2秒前
天天快乐应助duoya采纳,获得10
2秒前
Liuying2809发布了新的文献求助10
2秒前
诚心的鸡翅完成签到,获得积分10
3秒前
FashionBoy应助胡桃采纳,获得10
4秒前
科研小白花完成签到,获得积分10
4秒前
jwx完成签到,获得积分0
5秒前
上官若男应助刘大河采纳,获得10
5秒前
xiaopan发布了新的文献求助30
5秒前
芙蕖如玉发布了新的文献求助10
5秒前
魔幻寄琴发布了新的文献求助10
5秒前
了喔啰完成签到,获得积分10
6秒前
LL发布了新的文献求助10
7秒前
8秒前
yk完成签到,获得积分10
9秒前
9秒前
10秒前
10秒前
华仔应助志灰灰采纳,获得10
10秒前
Xuan发布了新的文献求助10
10秒前
娃娃菜妮完成签到 ,获得积分10
10秒前
11秒前
11秒前
11秒前
11秒前
紫气东来应助hheeb采纳,获得10
12秒前
今后应助一切顺利采纳,获得10
12秒前
热心市民小红花应助violin采纳,获得10
13秒前
13秒前
Hui发布了新的文献求助30
14秒前
赘婿应助优雅的化蛹采纳,获得10
14秒前
126发布了新的文献求助10
14秒前
Ava应助qdd采纳,获得10
14秒前
14秒前
wzwz发布了新的文献求助10
14秒前
Naturebo完成签到,获得积分10
14秒前
深情安青应助诉酒采纳,获得10
15秒前
15秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kinesiophobia : a new view of chronic pain behavior 3000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 1100
Signals, Systems, and Signal Processing 510
Discrete-Time Signals and Systems 510
Proceedings of the Fourth International Congress of Nematology, 8-13 June 2002, Tenerife, Spain 500
Le genre Cuphophyllus (Donk) st. nov 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5938673
求助须知:如何正确求助?哪些是违规求助? 7045119
关于积分的说明 15874948
捐赠科研通 5068623
什么是DOI,文献DOI怎么找? 2726065
邀请新用户注册赠送积分活动 1684637
关于科研通互助平台的介绍 1612462