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
cdercder应助方方不是很方采纳,获得10
刚刚
hhhhh发布了新的文献求助10
刚刚
研友_85Y5z8完成签到,获得积分20
1秒前
111发布了新的文献求助10
1秒前
2秒前
科研通AI6.2应助17876581310采纳,获得10
2秒前
快乐谷云完成签到,获得积分10
3秒前
3秒前
搜集达人应助MM采纳,获得10
4秒前
酷波er应助行7采纳,获得10
4秒前
yousheng完成签到,获得积分10
6秒前
6秒前
细心涵阳发布了新的文献求助10
6秒前
科研通AI6.3应助科研狗采纳,获得30
6秒前
6秒前
科研通AI6.4应助科研狗采纳,获得10
6秒前
科研通AI6.2应助科研狗采纳,获得10
7秒前
lili完成签到,获得积分10
7秒前
科研通AI6.2应助科研狗采纳,获得10
7秒前
7秒前
Warren发布了新的文献求助10
7秒前
科研通AI6.4应助科研狗采纳,获得10
7秒前
鸭不抗揍发布了新的文献求助10
8秒前
8秒前
71发布了新的文献求助10
8秒前
8秒前
9秒前
眉洛发布了新的文献求助10
9秒前
9秒前
桐桐应助狂野抽屉采纳,获得10
10秒前
10秒前
CipherSage应助Jadon采纳,获得10
10秒前
11秒前
乐乐应助寻悦采纳,获得10
11秒前
hhhhh完成签到,获得积分10
11秒前
11秒前
Yoci发布了新的文献求助10
12秒前
英姑应助wunai012321采纳,获得10
12秒前
毛毛虫应助无私枫叶采纳,获得10
13秒前
番茄发布了新的文献求助10
13秒前
高分求助中
(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
2026年中国辛酸癸酸聚乙二醇甘油酯行业市场规模及竞争格局分析报告 1000
48V Low-voltage Power Distribution Network (PDN) Architecture Industry Report, 2024 800
Fundamentals of Pharmaceutical and Biologics Regulations: A Global Perspective, Second Edition 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
适配Micro-LED色转换的高兼容性量子点负性光刻胶制备与工艺研究 500
Vander's Renal Physiology第10版 500
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 7314987
求助须知:如何正确求助?哪些是违规求助? 8931207
关于积分的说明 18930819
捐赠科研通 6975173
什么是DOI,文献DOI怎么找? 3213771
关于科研通互助平台的介绍 2381799
邀请新用户注册赠送积分活动 2192189