CT imaging-based radiomics signatures improve prognosis prediction in postoperative colorectal cancer

列线图 医学 无线电技术 结直肠癌 比例危险模型 Lasso(编程语言) 放射科 肿瘤科 内科学 癌症 计算机科学 万维网
作者
Yan Kong,Muchen Xu,Xianding Wei,Danqi Qian,Yuan Yin,Zhaohui Huang,Wenchao Gu,Leyuan Zhou
出处
期刊:Journal of X-ray Science and Technology [IOS Press]
卷期号:31 (6): 1281-1294 被引量:2
标识
DOI:10.3233/xst-230090
摘要

To investigate the use of non-contrast-enhanced (NCE) and contrast-enhanced (CE) CT radiomics signatures (Rad-scores) as prognostic factors to help improve the prediction of the overall survival (OS) of postoperative colorectal cancer (CRC) patients.A retrospective analysis was performed on 65 CRC patients who underwent surgical resection in our hospital as the training set, and 19 patient images retrieved from The Cancer Imaging Archive (TCIA) as the external validation set. In training, radiomics features were extracted from the preoperative NCE/CE-CT, then selected through 5-fold cross validation LASSO Cox method and used to construct Rad-scores. Models derived from Rad-scores and clinical factors were constructed and compared. Kaplan-Meier analyses were also used to compare the survival probability between the high- and low-risk Rad-score groups. Finally, a nomogram was developed to predict the OS.In training, a clinical model achieved a C-index of 0.796 (95% CI: 0.722-0.870), while clinical and two Rad-scores combined model performed the best, achieving a C-index of 0.821 (95% CI: 0.743-0.899). Furthermore, the models with the CE-CT Rad-score yielded slightly better performance than that of NCE-CT in training. For the combined model with CE-CT Rad-scores, a C-index of 0.818 (95% CI: 0.742-0.894) and 0.774 (95% CI: 0.556-0.992) were achieved in both the training and validation sets. Kaplan-Meier analysis demonstrated a significant difference in survival probability between the high- and low-risk groups. Finally, the areas under the receiver operating characteristics (ROC) curves for the model were 0.904, 0.777, and 0.843 for 1, 3, and 5-year survival, respectively.NCE-CT or CE-CT radiomics and clinical combined models can predict the OS for CRC patients, and both Rad-scores are recommended to be included when available.

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
斯文败类应助科研苦行僧采纳,获得10
刚刚
刚刚
刚刚
自由的盼柳完成签到 ,获得积分10
刚刚
123study0发布了新的文献求助10
刚刚
科研通AI6.1应助cc采纳,获得10
1秒前
务实珊发布了新的文献求助20
1秒前
1秒前
忧郁难胜完成签到,获得积分10
1秒前
1秒前
Yuki完成签到 ,获得积分10
1秒前
lizil发布了新的文献求助10
2秒前
CJW发布了新的文献求助10
2秒前
3秒前
分隔符发布了新的文献求助10
3秒前
SYY发布了新的文献求助10
4秒前
4秒前
Sprout发布了新的文献求助30
4秒前
4秒前
cindy完成签到 ,获得积分10
5秒前
8564523完成签到,获得积分10
5秒前
尊敬依珊完成签到 ,获得积分10
5秒前
积极雪糕发布了新的文献求助10
5秒前
5秒前
6秒前
WATQ完成签到,获得积分10
6秒前
lizil完成签到,获得积分20
6秒前
6秒前
斯文败类应助专注的问寒采纳,获得10
7秒前
7秒前
干净柏柳完成签到 ,获得积分10
7秒前
大模型应助胡沐恬采纳,获得10
7秒前
7秒前
7秒前
欣慰的怜容完成签到 ,获得积分10
8秒前
英姑应助宁不惜采纳,获得10
8秒前
VE完成签到,获得积分10
8秒前
千跃举报Laign求助涉嫌违规
8秒前
三石发布了新的文献求助10
8秒前
丘比特应助烂漫成仁采纳,获得20
9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
Burger's Medicinal Chemistry, Drug Discovery and Development, Volumes 1 - 8, 8 Volume Set, 8th Edition 1800
Cronologia da história de Macau 1600
Contemporary Debates in Epistemology (3rd Edition) 1000
International Arbitration Law and Practice 1000
文献PREDICTION EQUATIONS FOR SHIPS' TURNING CIRCLES或期刊Transactions of the North East Coast Institution of Engineers and Shipbuilders第95卷 1000
BRITTLE FRACTURE IN WELDED SHIPS 1000
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 纳米技术 计算机科学 化学工程 生物化学 物理 复合材料 内科学 催化作用 物理化学 光电子学 细胞生物学 基因 电极 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 6159901
求助须知:如何正确求助?哪些是违规求助? 7988060
关于积分的说明 16603138
捐赠科研通 5268283
什么是DOI,文献DOI怎么找? 2810896
邀请新用户注册赠送积分活动 1791166
关于科研通互助平台的介绍 1658105