Radiomics in colorectal cancer patients

医学 无线电技术 结直肠癌 磁共振成像 放射科 放射基因组学 医学影像学 辅助治疗 转移 化疗 肿瘤科 癌症 内科学
作者
Riccardo Inchingolo,Cesare Maino,Roberto Cannella,Federica Vernuccio,Francesco Cortese,Michele Dezio,Antonio Rosario Pisani,Teresa Giandola,Marco Gatti,Valentina Giannini,Davide Ippolito,Riccardo Faletti
出处
期刊:World Journal of Gastroenterology [Baishideng Publishing Group]
卷期号:29 (19): 2888-2904 被引量:19
标识
DOI:10.3748/wjg.v29.i19.2888
摘要

The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.

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
华仔的应助被激动的萧采纳,获得10
刚刚
积极鱼完成签到 ,获得积分10
刚刚
2秒前
3秒前
fy发布了新的文献求助10
4秒前
CipherSage的应助被杨文成采纳,获得10
7秒前
William发布了新的文献求助10
7秒前
aikeyan完成签到,获得积分10
11秒前
21222324完成签到 ,获得积分10
12秒前
神探狄仁杰完成签到 ,获得积分10
13秒前
ding的应助被sdfg采纳,获得10
13秒前
科研通AI6.4的应助被kdttt采纳,获得30
14秒前
科研通AI6.4的应助被金枪鱼子采纳,获得30
16秒前
18秒前
19秒前
19秒前
桃酥完成签到 ,获得积分10
20秒前
幽默的季节完成签到 ,获得积分10
20秒前
共产主义战士的应助被future采纳,获得10
21秒前
曾曾完成签到,获得积分10
22秒前
研友_LX66qZ完成签到,获得积分10
22秒前
22秒前
柴鱼0625完成签到 ,获得积分10
23秒前
24秒前
Joyce完成签到,获得积分10
24秒前
bb发布了新的文献求助10
24秒前
阿峰完成签到,获得积分10
26秒前
无语的汉堡完成签到 ,获得积分10
26秒前
27秒前
Yann的应助被fy采纳,获得10
27秒前
sdfg发布了新的文献求助10
29秒前
30秒前
herococa的应助被11111112222采纳,获得10
31秒前
SASI完成签到 ,获得积分10
32秒前
杨文成发布了新的文献求助10
33秒前
SciGPT的应助被Yeee1226采纳,获得10
33秒前
34秒前
ncxxxxxx发布了新的文献求助10
34秒前
34秒前
追寻清完成签到,获得积分0
35秒前
高分求助中
(应助此贴封号)通过应助OA文献获取积分 10000
Rosenblum, Global Change Biology 800
Computational Chemical Reaction Engineering: Modeling, Simulation, and Design with MATLAB 600
Organizational Behavior 510
Management and the Arts 510
Production Logging: Theoretical and Interpretive Elements 400
CLSI C56QG Examples of Hemolyzed, Icteric, and Lipemic/Turbid Samples Quick Guide 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 计算机科学 工程类 纳米技术 内科学 物理 有机化学 化学工程 生物化学 复合材料 光电子学 细胞生物学 心理学 量子力学 催化作用 物理化学 电极
热门帖子
关注 科研通微信公众号,转发送积分 7817798
求助须知:如何正确求助?哪些是违规求助? 9346252
关于积分的说明 20534768
捐赠科研通 7410296
什么是DOI,文献DOI怎么找? 3331819
关于科研通互助平台的介绍 2478130
邀请新用户注册赠送积分活动 2351558