清晨好,您是今天最早来到科研通的研友!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,伴您科研之路漫漫前行!

An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker

拉曼光谱 癌症 胃癌 胃切除术 内科学 医学 胃肠病学 病理 物理 光学
作者
Zozan Güleken,Paweł Jakubczyk,Wiesław Paja,Krzysztof Pancerz,Agnieszka Wosiak,İlhan Yaylım,Güldal İnal Gültekin,Nevzat Tarhan,Mehmet Tolgahan Hakan,Dilara Sönmez,Devrim Sarıbal,Soykan Arîkan,Joanna Depciuch
出处
期刊:Computer Methods and Programs in Biomedicine [Elsevier BV]
卷期号:234: 107523-107523 被引量:34
标识
DOI:10.1016/j.cmpb.2023.107523
摘要

Globally, gastric carcinoma (Gca) ranks fifth in terms of incidence and third in terms of mortality. Higher serum tumor markers (TMs) than those from healthy individuals, led to TMs clinical application as diagnostic biomarkers for Gca. Actually, there is no accurate blood test to diagnose Gca.Raman spectroscopy is applied as an efficient, credible, minimally invasive technique to evaluate the serum TMs levels in blood samples. After curative gastrectomy, serum TMs levels are important in predicting the recurrence of gastric cancer, which must be detected early. The experimentally assesed TMs levels using Raman measurements and ELİSA test were used to develop a prediction model based on machine learning techniques. A total of 70 participants diagnosed with gastric cancer after surgery (n = 26) and healthy (n = 44) were comrpised in this study.In the Raman spectra of gastric cancer patients, an additional peak at 1182 cm-1 was observed and, the Raman intensity of amide III, II, I, and CH2 proteins as well as lipids functional group was higher. Furthermore, Principal Component Analysis (PCA) showed, that it is possible to distinguish between the control and Gca groups using the Raman range between 800 and 1800 cm-1, as well as between 2700 and 3000 cm-1. The analysis of Raman spectra dynamics in gastric cancer and healthy patients showed, that the vibrations at 1302 and 1306 cm-1 were characteristic for cancer patients. In addition, the selected machine learning methods showed classification accuracy of more than 95%, while obtaining an AUROC of 0.98. Such results were obtained using Deep Neural Networks and the XGBoost algorithm.The obtained results suggest, that Raman shifts at 1302 and 1306 cm-1 could be spectroscopic markers of gastric cancer.
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
隐形曼青应助科研通管家采纳,获得10
3秒前
ceeray23发布了新的文献求助20
13秒前
orixero应助ceeray23采纳,获得20
48秒前
1233发布了新的文献求助10
51秒前
ChatGPT发布了新的文献求助10
52秒前
JG完成签到 ,获得积分10
53秒前
jkaaa完成签到,获得积分10
53秒前
1233完成签到,获得积分10
1分钟前
太清完成签到 ,获得积分10
1分钟前
1分钟前
ceeray23发布了新的文献求助20
1分钟前
cadcae完成签到,获得积分10
1分钟前
Eric800824完成签到 ,获得积分10
1分钟前
庚朝年完成签到 ,获得积分10
1分钟前
violetlishu完成签到 ,获得积分10
2分钟前
ceeray23发布了新的文献求助20
2分钟前
yingzaifeixiang完成签到 ,获得积分10
2分钟前
wodetaiyangLLL完成签到 ,获得积分10
2分钟前
称心如意完成签到 ,获得积分10
2分钟前
h41692011完成签到 ,获得积分10
3分钟前
dingding发布了新的文献求助10
3分钟前
今后应助dingding采纳,获得10
4分钟前
Jj7完成签到,获得积分10
4分钟前
4分钟前
4分钟前
4分钟前
4分钟前
彦嘉发布了新的文献求助10
4分钟前
斯文败类应助ceeray23采纳,获得20
5分钟前
打打应助Dave采纳,获得10
5分钟前
5分钟前
5分钟前
ceeray23发布了新的文献求助20
5分钟前
sh1ro发布了新的文献求助10
5分钟前
nojego完成签到,获得积分10
5分钟前
脑洞疼应助科研通管家采纳,获得10
6分钟前
顾矜应助科研通管家采纳,获得10
6分钟前
wjx完成签到 ,获得积分10
6分钟前
John完成签到,获得积分10
6分钟前
顾矜应助ceeray23采纳,获得20
6分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
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
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 3990550
求助须知:如何正确求助?哪些是违规求助? 3532220
关于积分的说明 11256532
捐赠科研通 3271057
什么是DOI,文献DOI怎么找? 1805207
邀请新用户注册赠送积分活动 882302
科研通“疑难数据库(出版商)”最低求助积分说明 809234