Machine Learning‐Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma

医学 磁共振成像 颅咽管瘤 放射科 病态的 核磁共振 病理 计算机科学 物理
作者
Zhou‐San Huang,Xiang Xiao,Xiaodan Li,Hai‐Zhu Mo,Wenle He,Yao‐Hong Deng,Li‐Jun Lu,Yuan‐Kui Wu,Hao Liu
出处
期刊:Journal of Magnetic Resonance Imaging [Wiley]
卷期号:54 (5): 1541-1550 被引量:27
标识
DOI:10.1002/jmri.27761
摘要

BACKGROUND: Preoperative, noninvasive discrimination of the craniopharyngioma subtypes is important because it influences the treatment strategy. PURPOSE: To develop a radiomic model based on multiparametric magnetic resonance imaging for noninvasive discrimination of pathological subtypes of craniopharyngioma. STUDY TYPE: Retrospective. POPULATION: A total of 164 patients from two medical centers were enrolled in this study. Patients from the first medical center were divided into a training cohort (N = 99) and an internal validation cohort (N = 33). Patients from the second medical center were used as the external independent validation cohort (N = 32). FIELD STRENGTH/SEQUENCE: -w) on 3.0 T or 1.5 T magnetic resonance scanners. ASSESSMENT: Pathological subtypes (squamous papillary craniopharyngioma and adamantinomatous craniopharyngioma) were confirmed by surgery and hematoxylin and eosin staining. Optimal radiomic feature selection was performed by SelectKBest, the least absolute shrinkage and selection operator algorithm, and support vector machine (SVM) with a recursive feature elimination algorithm. Models based on each sequence or combinations of sequences were built using a SVM classifier and used to differentiate pathological subtypes of craniopharyngioma in the training cohort, internal validation, and external validation cohorts. STATISTICAL TESTS: The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of the radiomic models. RESULTS: -w, were selected and used to construct the radiomic model. The AUC values of the radiomic model were 0.899, 0.810, and 0.920 in the training cohort, internal and external validation cohorts, respectively. The AUC values of the clinicoradiological model were 0.677, 0.655, and 0.671 in the training cohort, internal and external validation cohorts, respectively. DATA CONCLUSION: -w has a high discriminatory ability for pathological subtypes of craniopharyngioma. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: 2.
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
1秒前
动听千风完成签到 ,获得积分10
2秒前
hs完成签到,获得积分0
2秒前
wuy发布了新的文献求助10
2秒前
3秒前
Medici完成签到,获得积分10
4秒前
汉堡包应助ZYC007采纳,获得20
5秒前
tytyty完成签到,获得积分10
5秒前
6秒前
zz1完成签到,获得积分10
6秒前
ypp完成签到,获得积分10
7秒前
科研通AI6.4应助1111采纳,获得10
7秒前
小何发布了新的文献求助10
8秒前
自然毛衣发布了新的文献求助10
8秒前
彭于晏应助wuy采纳,获得10
9秒前
tly发布了新的文献求助30
10秒前
乐乐应助免我蹉跎苦采纳,获得10
10秒前
11秒前
安逸1发布了新的文献求助10
11秒前
11秒前
13秒前
123完成签到,获得积分10
13秒前
14秒前
Star-XYX完成签到,获得积分10
14秒前
淡定的心情完成签到,获得积分10
14秒前
JamesPei应助迷路的怡采纳,获得10
15秒前
kk发布了新的文献求助10
16秒前
l1zz应助seven采纳,获得10
17秒前
忧郁老头发布了新的文献求助10
17秒前
大模型应助浮光采纳,获得10
18秒前
栾松壕发布了新的文献求助10
19秒前
冬日空虚完成签到,获得积分10
19秒前
小杨完成签到,获得积分10
20秒前
高兴的悟空完成签到,获得积分10
20秒前
一园一木完成签到,获得积分10
22秒前
背后芷雪发布了新的文献求助10
23秒前
24秒前
25秒前
Ava应助tyk采纳,获得10
26秒前
26秒前
高分求助中
Psychopathic Traits and Quality of Prison Life 1000
Malcolm Fraser : a biography 680
Signals, Systems, and Signal Processing 610
天津市智库成果选编 600
Forced degradation and stability indicating LC method for Letrozole: A stress testing guide 500
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
A Foreign Missionary on the Long March: The Unpublished Memoirs of Arnolis Hayman of the China Inland Mission 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6452555
求助须知:如何正确求助?哪些是违规求助? 8264295
关于积分的说明 17610980
捐赠科研通 5517783
什么是DOI,文献DOI怎么找? 2904129
邀请新用户注册赠送积分活动 1880979
关于科研通互助平台的介绍 1723132