Preoperative Assessment for High‐Risk Endometrial Cancer by Developing an MRI‐ and Clinical‐Based Radiomics Nomogram: A Multicenter Study

列线图 无线电技术 医学 子宫内膜癌 置信区间 逻辑回归 放射科 接收机工作特性 Lasso(编程语言) 淋巴结切除术 核医学 内科学 癌症 万维网 计算机科学
作者
Bi Cong Yan,Ying Li,Hua Feng,Feng Feng,Ming Sun,Guangwu Lin,Guofu Zhang,Jin Wei Qiang
出处
期刊:Journal of Magnetic Resonance Imaging [Wiley]
卷期号:52 (6): 1872-1882 被引量:59
标识
DOI:10.1002/jmri.27289
摘要

Background High‐ and low‐risk endometrial cancer (EC) differ in whether lymphadenectomy is performed. Assessment of high‐risk EC is essential for planning surgery appropriately. Purpose To develop a radiomics nomogram for high‐risk EC prediction preoperatively. Study Type Retrospective. Population In all, 717 histopathologically confirmed EC patients (mean age, 56 years ± 9) divided into a primary group (394 patients from Center A), validation groups 1 and 2 (146 patients from Center B and 177 patients from Centers C–E). Field Strength/Sequence 1.5/ 3T scanners; T 2 ‐weighted imaging, diffusion‐weighted imaging, apparent diffusion coefficient, and contrast enhancement sequences. Assessment A radiomics nomogram was generated by combining the selected radiomics features and clinical parameters (metabolic syndrome, cancer antigen 125, age, tumor grade following curettage, and tumor size). The area under the curve (AUC) of the receiver operator characteristic was used to evaluate the predictive performance of the radiomics nomogram for high‐risk EC. The surgical procedure suggested by the nomogram was compared with the actual procedure performed for the patients. Net benefit of the radiomics nomogram was evaluated by a clinical decision curve (CDC), net reclassification index (NRI), and integrated discrimination improvement (IDI). Statistical Tests Binary least absolute shrinkage and selection operator (LASSO) logistic regression, linear regression, and multivariate binary logistic regression were used to select radiomics features and clinical parameters. Results The AUC for prediction of high‐risk EC for the radiomics nomogram in the primary group, validation groups 1 and 2 were 0.896 (95% confidence interval [CI]: 0.866–0.926), 0.877 (95% CI: 0.825–0.930), and 0.919 (95% CI: 0.879–0.960), respectively. The nomogram achieved good net benefit by CDC analysis for high‐risk EC. NRIs were 1.17, 1.28, and 1.51, and IDIs were 0.41, 0.60, and 0.61 in the primary group, validation groups 1 and 2, respectively. Data Conclusion The radiomics nomogram exhibited good performance in the individual prediction of high‐risk EC, and might be used for surgical management of EC. Level of Evidence 4 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2020;52:1872–1882.

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
Preseverance完成签到,获得积分10
刚刚
无妨完成签到,获得积分10
1秒前
浮梦发布了新的文献求助10
1秒前
1秒前
systemthinker发布了新的文献求助10
2秒前
whjbb完成签到,获得积分10
2秒前
qing发布了新的文献求助30
3秒前
落霞发布了新的文献求助10
4秒前
4秒前
乐乐应助mia采纳,获得10
4秒前
cdercder应助Rached采纳,获得10
4秒前
吃点红糖馒头完成签到,获得积分10
5秒前
wsazah完成签到,获得积分10
7秒前
含蓄虔纹完成签到,获得积分10
8秒前
8秒前
袁瑞发布了新的文献求助10
8秒前
8秒前
NiL发布了新的文献求助10
9秒前
聪明海豚完成签到,获得积分20
10秒前
10秒前
搜集达人应助浩哥要strong采纳,获得10
10秒前
仲颖完成签到,获得积分10
11秒前
Lucas应助专注的草丛采纳,获得10
11秒前
今后应助Ame采纳,获得10
13秒前
hrpppp发布了新的文献求助10
13秒前
byr完成签到,获得积分10
13秒前
15秒前
lixm发布了新的文献求助10
15秒前
浩哥要strong完成签到,获得积分10
15秒前
16秒前
18秒前
欣灵应助仲颖采纳,获得10
18秒前
顾矜应助袁瑞采纳,获得10
19秒前
Leon完成签到,获得积分10
19秒前
19秒前
20秒前
吸墨完成签到,获得积分10
20秒前
落寞凌波发布了新的文献求助10
20秒前
酷波er应助lixm采纳,获得10
21秒前
寒酥完成签到,获得积分10
21秒前
高分求助中
Cronologia da história de Macau 5000
Matrix Methods in Data Mining and Pattern Recognition 510
Interactions of Vowel Quality and Prosody in East Slavic 500
Vander's Renal Physiology第10版 500
Forensic Science An Introduction to Scientific and Investigative Techniques 6th Edition 400
Virus-like particles empower RNAi for effective control of a Coleopteran pest 400
Materials Informatics Molecules, Crystals and Beyond A volume in Acta Materialia Book Series 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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7097757
求助须知:如何正确求助?哪些是违规求助? 8754006
关于积分的说明 18514969
捐赠科研通 6653432
什么是DOI,文献DOI怎么找? 3138596
关于科研通互助平台的介绍 2247783
邀请新用户注册赠送积分活动 2113533