Deep learning-based radiomic nomogram to predict risk categorization of thymic epithelial tumors: A multicenter study

列线图 医学 无线电技术 队列 分类 接收机工作特性 深度学习 人工智能 放射科 机器学习 肿瘤科 内科学 计算机科学
作者
Hao Zhou,Harrison X. Bai,Zhicheng Jiao,Biqi Cui,Jing Wu,Haijun Zheng,Huan Yang,Weihua Liao
出处
期刊:European Journal of Radiology [Elsevier BV]
卷期号:168: 111136-111136 被引量:5
标识
DOI:10.1016/j.ejrad.2023.111136
摘要

PurposeThe study was aimed to develop and evaluate a deep learning-based radiomics to predict the histological risk categorization of thymic epithelial tumors (TETs), which can be highly informative for patient treatment planning and prognostic assessment.MethodA total of 681 patients with TETs from three independent hospitals were included and separated into derivation cohort and external test cohort. Handcrafted and deep learning features were extracted from preoperative contrast-enhanced CT images and selected to build three radiomics signatures (radiomics signature [Rad_Sig], deep learning signature [DL_Sig] and deep learning radiomics signature [DLR_Sig]) to predict risk categorization of TETs. A deep learning-based radiomic nomogram (DLRN) was then depicted to visualize the classification evaluation. The performance of predictive models was compared using the receiver operating characteristic and decision curve analysis (DCA).ResultsAmong three radiomics signatures, DLR_Sig demonstrated optimum performance with an AUC of 0.883 for the derivation cohort and 0.749 for the external test cohort. Combining DLR_Sig with age and gender, DLRN was depict and exhibited optimum performance among all radiomics models with an AUC of 0.965, accuracy of 0.911, sensitivity of 0.921 and specificity of 0.902 in the derivation cohort, and an AUC of 0.786, accuracy of 0.774, sensitivity of 0.778 and specificity of 0.771 in the external test cohort. The DCA showed that DLRN had greater clinical benefit than other radiomics signatures.ConclusionsOur study developed and validated a DLRN to accurately predict the risk categorization of TETs, which has potential to facilitate individualized treatment and improve patient prognosis evaluation.
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
所所应助THINKG采纳,获得10
1秒前
vv123456ha发布了新的文献求助10
1秒前
粉蒸肉发布了新的文献求助10
1秒前
爱因斯宣发布了新的文献求助10
2秒前
2秒前
太叔丹翠完成签到 ,获得积分10
2秒前
2秒前
3秒前
3秒前
3秒前
学物理的小汁完成签到,获得积分10
4秒前
共享精神应助程锦采纳,获得10
4秒前
5秒前
6秒前
老朱发布了新的文献求助10
6秒前
zzzz应助wyh采纳,获得10
7秒前
jx314发布了新的文献求助30
7秒前
keke发布了新的文献求助10
7秒前
精英刺客发布了新的文献求助10
8秒前
8秒前
一一发布了新的文献求助10
8秒前
dd发布了新的文献求助10
8秒前
胡思乱想完成签到,获得积分10
9秒前
火乐完成签到,获得积分20
10秒前
xxx发布了新的文献求助10
10秒前
10秒前
10秒前
yy严发布了新的文献求助10
11秒前
11秒前
12秒前
13秒前
13秒前
搞怪的听南完成签到,获得积分10
14秒前
14秒前
16秒前
17秒前
LiuJ应助怪杰采纳,获得10
17秒前
Lyncus发布了新的文献求助50
18秒前
程锦发布了新的文献求助10
18秒前
失眠的珩完成签到,获得积分10
19秒前
高分求助中
【请各位用户详细阅读此贴后再求助】科研通的精品贴汇总(请勿应助) 10000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
International Code of Nomenclature for algae, fungi, and plants (Madrid Code) (Regnum Vegetabile) 500
Maritime Applications of Prolonged Casualty Care: Drowning and Hypothermia on an Amphibious Warship 500
Comparison analysis of Apple face ID in iPad Pro 13” with first use of metasurfaces for diffraction vs. iPhone 16 Pro 500
Towards a $2B optical metasurfaces opportunity by 2029: a cornerstone for augmented reality, an incremental innovation for imaging (YINTR24441) 500
Materials for Green Hydrogen Production 2026-2036: Technologies, Players, Forecasts 500
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 4058604
求助须知:如何正确求助?哪些是违规求助? 3596879
关于积分的说明 11426957
捐赠科研通 3321926
什么是DOI,文献DOI怎么找? 1826640
邀请新用户注册赠送积分活动 897222
科研通“疑难数据库(出版商)”最低求助积分说明 818310