已入深夜,您辛苦了!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,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

An MRI-Based Radiomics Nomogram for Differentiation of Benign and Malignant Vertebral Compression Fracture

列线图 无线电技术 医学 接收机工作特性 逻辑回归 磁共振成像 放射科 肿瘤科 内科学
作者
Qianqian Feng,Shan Xu,Xiaoli Gong,Teng Wang,Xiaopeng He,Da-wei Liao,Fugang Han
出处
期刊:Academic Radiology [Elsevier]
卷期号:31 (2): 605-616 被引量:7
标识
DOI:10.1016/j.acra.2023.07.011
摘要

Rationale and Objectives

This study aimed to develop and validate a magnetic resonance imaging (MRI)-based radiomics nomogram combining radiomics signatures and clinical factors to differentiate between benign and malignant vertebral compression fractures (VCFs).

Materials and Methods

A total of 189 patients with benign VCFs (n = 112) or malignant VCFs (n = 77) were divided into training (n = 133) and validation (n = 56) cohorts. Radiomics features were extracted from MRI T1-weighted images and short-TI inversion recovery images to develop the radiomics signature, and the Rad score was constructed using least absolute shrinkage and selection operator regression. Demographic and MRI morphological characteristics were assessed to build a clinical factor model using multivariate logistic regression analysis. A radiomics nomogram was constructed based on the Rad score and independent clinical factors. Finally, the diagnostic performance of the radiomics nomogram, clinical model, and radiomics signature was validated using receiver operating characteristic and decision curve analysis (DCA).

Results

Six features were used to build a combined radiomics model (combined-RS). Pedicle or posterior element involvement, paraspinal mass, and fluid sign were identified as the most important morphological factors for building the clinical factor model. The radiomics signature was superior to the clinical model in terms of the area under the curve (AUC), accuracy, and specificity. The radiomics nomogram integrating the combined-RS, pedicle or posterior element involvement, paraspinal mass, and fluid sign achieved favorable predictive efficacy, generating AUCs of 0.92 and 0.90 in the training and validation cohorts, respectively. The DCA indicated good clinical usefulness of the radiomics nomogram.

Conclusion

The MRI-based radiomics nomogram, combining the radiomics signature and clinical factors, showed favorable predictive efficacy for differentiating benign from malignant VCFs.
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
李爱国应助莱斯够瓦瑞丝采纳,获得10
2秒前
2秒前
2秒前
yuyu发布了新的文献求助10
2秒前
彭于晏应助11231采纳,获得10
3秒前
执着的以筠完成签到 ,获得积分10
4秒前
粽子发布了新的文献求助10
6秒前
加强派克发布了新的文献求助10
7秒前
安详的夜春完成签到 ,获得积分10
8秒前
8秒前
9秒前
科研通AI2S应助yao采纳,获得10
9秒前
yuanyuan发布了新的文献求助30
9秒前
10秒前
Owen应助咚咚采纳,获得10
12秒前
13秒前
Crystal发布了新的文献求助10
14秒前
15秒前
Lucas应助读书的时候采纳,获得10
15秒前
所所应助简单寻冬采纳,获得10
15秒前
Ava应助accept小猫采纳,获得10
16秒前
LILI完成签到 ,获得积分10
17秒前
17秒前
19秒前
20秒前
21秒前
sys549发布了新的文献求助10
23秒前
lzy发布了新的文献求助10
24秒前
上官若男应助BAOBAO采纳,获得10
24秒前
24秒前
cyy发布了新的文献求助10
24秒前
阳光男孩完成签到 ,获得积分10
25秒前
cgr完成签到,获得积分10
25秒前
FG发布了新的文献求助10
26秒前
cgr发布了新的文献求助10
27秒前
顾矜应助初遇之时最暖采纳,获得10
28秒前
乐乐应助Wendy采纳,获得10
30秒前
白猫完成签到 ,获得积分10
30秒前
Ava应助动听葵阴采纳,获得10
31秒前
小二郎应助汤圆和lucky采纳,获得10
32秒前
高分求助中
2025-2031全球及中国金刚石触媒粉行业研究及十五五规划分析报告 40000
(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
Introduction to strong mixing conditions volume 1-3 5000
Agyptische Geschichte der 21.30. Dynastie 3000
Les Mantodea de guyane 2000
Clinical Microbiology Procedures Handbook, Multi-Volume, 5th Edition 2000
„Semitische Wissenschaften“? 1510
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5746095
求助须知:如何正确求助?哪些是违规求助? 5430774
关于积分的说明 15354692
捐赠科研通 4885972
什么是DOI,文献DOI怎么找? 2626998
邀请新用户注册赠送积分活动 1575502
关于科研通互助平台的介绍 1532213