A CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy: a multicenter study

列线图 无线电技术 医学 食管鳞状细胞癌 食管癌 比例危险模型 放化疗 放射科 肿瘤科 癌症 放射治疗 内科学
作者
Jie Gong,Jianchao Lu,Wencheng Zhang,Wei Huang,Jing Wang,Jing Wang,Meng Fan,Hongfei Sun,Lina Zhao
出处
期刊:Journal of Translational Medicine [BioMed Central]
卷期号:22 (1)
标识
DOI:10.1186/s12967-024-05897-y
摘要

To develop and validate an online individualized model for predicting local recurrence-free survival (LRFS) in esophageal squamous cell carcinoma (ESCC) treated by definitive chemoradiotherapy (dCRT). ESCC patients from three hospitals were randomly stratified into the training set (715) and the internal testing set (179), and patients from the other hospital as the external testing set (120). The important radiomic features extracted from contrast-enhanced computed tomography (CECT)-based subregions clustered from the whole volume of tumor and peritumor were selected and used to construct the subregion-based radiomic signature by using COX proportional hazards model, which was compared with the tumor-based radiomic signature. The clinical model and the radiomics model combing the clinical factors and the radiomic signature were further constructed and compared, which were validated in two testing sets. The subresion-based radiomic signature showed better prognostic performance than the tumor-based radiomic signature (training: 0.642 vs. 0.621, internal testing: 0.657 vs. 0.638, external testing: 0.636 vs. 0.612). Although the tumor-based radiomic signature, the subregion-based radiomic signature, the tumor-based radiomics model, and the subregion-based radiomics model had better performance compared to the clinical model, only the subregion-based radiomics model showed a significant advantage (p < 0.05; training: 0.666 vs. 0.616, internal testing: 0.689 vs. 0.649, external testing: 0.642 vs. 0.604). The clinical model and the subregion-based radiomics model were visualized as the nomograms, which are available online and could interactively calculate LRFS probability. We established and validated a CECT-based online radiomics nomogram for predicting LRFS in ESCC received dCRT, which outperformed the clinical model and might serve as a powerful tool to facilitate individualized treatment. This retrospective study was approved by the ethics committee (KY20222145-C-1).
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
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
科研通AI5应助Luna采纳,获得10
刚刚
司徒诗蕾发布了新的文献求助10
1秒前
852应助诸葛朝雪采纳,获得10
2秒前
dushicheng发布了新的文献求助10
2秒前
2秒前
3秒前
老驴拉磨完成签到,获得积分10
5秒前
6秒前
Azhou应助kkscanl采纳,获得150
7秒前
HEIKU应助tutu采纳,获得10
7秒前
科研完成签到,获得积分10
8秒前
鲤鱼完成签到,获得积分10
9秒前
Mr.SG完成签到,获得积分10
9秒前
10秒前
找文献呢完成签到,获得积分10
10秒前
小谷完成签到 ,获得积分10
11秒前
我是老大应助wengjiaqi采纳,获得10
12秒前
zho发布了新的文献求助10
14秒前
小蝌完成签到,获得积分10
15秒前
华仔应助旅程采纳,获得10
15秒前
19秒前
善学以致用应助Pooh采纳,获得10
20秒前
YOOK发布了新的文献求助10
22秒前
23秒前
23秒前
23秒前
小黑发布了新的文献求助10
23秒前
su完成签到,获得积分10
23秒前
24秒前
24秒前
星辰大海应助科研通管家采纳,获得10
26秒前
JamesPei应助科研通管家采纳,获得10
26秒前
jiesenya完成签到,获得积分10
26秒前
yizhouchang应助科研通管家采纳,获得10
26秒前
深情安青应助科研通管家采纳,获得10
26秒前
田様应助科研通管家采纳,获得10
26秒前
CipherSage应助科研通管家采纳,获得10
26秒前
星辰大海应助科研通管家采纳,获得10
26秒前
顾矜应助科研通管家采纳,获得10
26秒前
yizhouchang应助科研通管家采纳,获得200
26秒前
高分求助中
Introduction to Strong Mixing Conditions Volumes 1-3 500
Tip60 complex regulates eggshell formation and oviposition in the white-backed planthopper, providing effective targets for pest control 400
Optical and electric properties of monocrystalline synthetic diamond irradiated by neutrons 320
共融服務學習指南 300
Essentials of Pharmacoeconomics: Health Economics and Outcomes Research 3rd Edition. by Karen Rascati 300
Peking Blues // Liao San 300
E-commerce live streaming impact analysis based on stimulus-organism response theory 260
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 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3801383
求助须知:如何正确求助?哪些是违规求助? 3347052
关于积分的说明 10331704
捐赠科研通 3063333
什么是DOI,文献DOI怎么找? 1681602
邀请新用户注册赠送积分活动 807616
科研通“疑难数据库(出版商)”最低求助积分说明 763818