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 BV]
卷期号:31 (2): 605-616 被引量:5
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
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
刘鑫完成签到,获得积分10
刚刚
Lenacici发布了新的文献求助10
1秒前
奥特超曼应助从容冰夏采纳,获得10
2秒前
2秒前
HJJHJH发布了新的文献求助30
2秒前
安静燕关注了科研通微信公众号
3秒前
葛博完成签到,获得积分10
4秒前
科研通AI5应助岳凯采纳,获得10
5秒前
ChatGPT发布了新的文献求助10
8秒前
9秒前
10秒前
刻苦绿柳完成签到 ,获得积分10
12秒前
12秒前
CR7完成签到,获得积分0
13秒前
曹松柏发布了新的文献求助10
14秒前
CodeCraft应助健忘的寻菱采纳,获得10
15秒前
小乐完成签到 ,获得积分10
15秒前
16秒前
安静燕发布了新的文献求助10
16秒前
16秒前
李y梅子发布了新的文献求助20
16秒前
17秒前
大模型应助leo瀚采纳,获得10
17秒前
18秒前
杨欢发布了新的文献求助10
19秒前
微风低回发布了新的文献求助10
21秒前
21秒前
23秒前
彩色宛筠完成签到,获得积分10
23秒前
23秒前
28秒前
114514发布了新的文献求助10
29秒前
29秒前
打打应助曹松柏采纳,获得10
31秒前
岳阳张震岳完成签到,获得积分10
32秒前
Medicovv发布了新的文献求助10
32秒前
微风低回完成签到,获得积分10
33秒前
HYT发布了新的文献求助10
35秒前
张雷应助114514采纳,获得20
36秒前
李治海完成签到,获得积分10
39秒前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Social Research Methods (4th Edition) by Maggie Walter (2019) 1030
A new approach to the extrapolation of accelerated life test data 1000
Indomethacinのヒトにおける経皮吸収 400
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 370
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 3993519
求助须知:如何正确求助?哪些是违规求助? 3534225
关于积分的说明 11265055
捐赠科研通 3274061
什么是DOI,文献DOI怎么找? 1806274
邀请新用户注册赠送积分活动 883084
科研通“疑难数据库(出版商)”最低求助积分说明 809710