Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI

无线电技术 鉴定(生物学) 人工智能 互联网 高分辨率 计算机科学 分辨率(逻辑) 机器学习 心理学 万维网 生物 地理 遥感 植物
作者
Li Wang,Li Zhou,Shengdan Liu,Yurong Zheng,Qianhan Liu,Minglin Yu,Xiaofei Lu,Wei Lei,Guangxiang Chen
出处
期刊:Progress in Neuro-psychopharmacology & Biological Psychiatry [Elsevier BV]
卷期号:133: 111026-111026 被引量:1
标识
DOI:10.1016/j.pnpbp.2024.111026
摘要

It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted radiomic features of subcortical structures in high-resolution T1-weighted MRI. Different combinations of four feature selection methods (analysis of variance, Kruskal–Wallis, recursive feature elimination and relief) and ten classification algorithms were used to identify the most robust combined models for distinguishing IGD patients from HCs. Furthermore, a nomogram incorporating radiomic signatures and independent clinical factors was developed. Calibration curve and decision curve analyses were used to evaluate the nomogram. The combination of analysis of variance selector and logistic regression classifier identified that the radiomic model constructed with 20 features from the right caudate nucleus and amygdala showed better IGD screening performance. The radiomic model produced good areas under the curves (AUCs) in the training, validation and test cohorts (AUCs of 0.961, 0.903 and 0.895, respectively). In addition, sex, internet addiction test scores and radiomic scores were included in the nomogram as independent risk factors for IGD. Analysis of the correction curve and decision curve showed that the clinical-radiomic model has good reliability (C-index: 0.987). The nomogram incorporating radiomic features of subcortical structures and clinical characteristics achieved satisfactory classification performance and could serve as an effective tool for distinguishing IGD patients from HCs.
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
典雅碧空发布了新的文献求助10
刚刚
ZZ发布了新的文献求助10
1秒前
火柴发布了新的文献求助10
1秒前
2秒前
BY0131完成签到,获得积分20
2秒前
hhj完成签到,获得积分10
2秒前
允柠关注了科研通微信公众号
2秒前
PengHu完成签到,获得积分10
3秒前
千万雷同发布了新的文献求助10
3秒前
留白发布了新的文献求助10
4秒前
隐形曼青应助1256采纳,获得10
4秒前
zzjjzzjj完成签到,获得积分10
4秒前
幸运草完成签到,获得积分10
4秒前
思源应助wangdafa采纳,获得10
5秒前
5秒前
shuiyu完成签到,获得积分10
6秒前
7秒前
7秒前
李爱国应助科研通管家采纳,获得10
7秒前
大个应助科研通管家采纳,获得10
7秒前
领导范儿应助科研通管家采纳,获得10
7秒前
星辰大海应助科研通管家采纳,获得50
7秒前
Jasper应助科研通管家采纳,获得10
7秒前
柏林寒冬应助科研通管家采纳,获得10
7秒前
yar应助科研通管家采纳,获得10
8秒前
鱼鱼色完成签到,获得积分10
8秒前
赘婿应助科研通管家采纳,获得10
8秒前
Jasper应助科研通管家采纳,获得10
8秒前
科研通AI5应助科研通管家采纳,获得10
8秒前
8秒前
8秒前
NexusExplorer应助科研通管家采纳,获得10
8秒前
魁梧的凌瑶完成签到,获得积分10
8秒前
8秒前
CodeCraft应助徐琪采纳,获得10
10秒前
10秒前
Liulin1完成签到,获得积分20
10秒前
叮叮完成签到,获得积分10
10秒前
wanci应助Rabbit采纳,获得10
11秒前
12秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
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 3974882
求助须知:如何正确求助?哪些是违规求助? 3519431
关于积分的说明 11198315
捐赠科研通 3255698
什么是DOI,文献DOI怎么找? 1797904
邀请新用户注册赠送积分活动 877237
科研通“疑难数据库(出版商)”最低求助积分说明 806219