基于多参数磁共振成像的放射组学列线图预测子宫内膜癌淋巴管间隙浸润
Multiparametric MRI-Based Radiomics Nomogram for Predicting Lymphovascular Space Invasion in Endometrial Carcinoma.
作者信息
Luo Yan, Mei Dongdong, Gong Jingshan, Zuo Min, Guo Xiaojing
机构信息
Department of Radiology, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, Shenzhen, China.
Department of Radiology, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China.
出版信息
J Magn Reson Imaging. 2020 Oct;52(4):1257-1262. doi: 10.1002/jmri.27142. Epub 2020 Apr 21.
BACKGROUND
Lymphovascular space invasion (LVSI) of endometrial carcinoma (EMC) is one of the important prognostic factors, which is not usually visible subjectively. Therefore, clinicians lack imaging-based evidence about LVSI for preoperative treatment decision-making.
PURPOSE
To develop a multiparametric MRI (mpMRI)-based radiomics nomogram for predicting LVSI in EMC and provide decision-making support to clinicians.
STUDY TYPE
Retrospective.
POPULATION
In all, 144 patients with histologically confirmed EMC, 101 patients in a training cohort, and 43 patients in a test cohort.
FIELD STRENGTH/SEQUENCE: T WI, contrast enhanced-T WI, and diffusion-weighted imaging (DWI) at 3.0T MRI.
ASSESSMENT
Tumors were independently segmented images by two radiologists. Two pathologists reviewed the tissue specimens of the tumors to identify the existence of LVSI in consensus.
STATISTICAL TESTS
The intraclass correlation coefficient was used to test the reliability and least absolute shrinkage and selection operator (LASSO) regression for features selection and then developed a radiomics signature named Rad-score. A nomogram was developed in the training cohort. The diagnostic performance of the nomogram model was assessed by area under the curve (AUC) of the receiver operator characteristic (ROC) in the training and test cohort, respectively.
RESULTS
LVSI was identified in 32 patients (22.2%). Older age and high grade were correlated with LVSI at univariate analysis. There were five radiomics features that were identified as independent risk factors for LVSI by LASSO regression. Based on age, grade, and Rad-score, the AUC values of the nomogram model to predict LVSI in the training and test cohort were 0.820 (95% confidence interval [CI]: 0.725, 0.916; sensitivity: 82.6%, specificity: 72.9%), 0.807 (95% CI: 0.673, 0.941; sensitivity: 77.8%, specificity: 78.6%), respectively.
DATA CONCLUSION
The radiomic-based machine-learning model using a nomogram algorithm achieved high diagnostic performance for predicting LVSI of EMC preoperatively, which could enhance risk stratification and provide support for therapeutic decision-making.
LEVEL OF EVIDENCE
TECHNICAL EFFICACY STAGE
- J. Magn. Reson. Imaging 2020;52:1257-1262.
背景
子宫内膜癌（EMC）的淋巴管间隙浸润（LVSI）是重要的预后因素之一，通常无法主观观察到。因此，临床医生在术前治疗决策时缺乏基于影像学的LVSI证据。
目的
开发一种基于多参数MRI（mpMRI）的影像组学列线图，用于预测EMC中的LVSI，并为临床医生提供决策支持。
研究类型
回顾性研究。
研究对象
共144例经组织学确诊的EMC患者，其中101例纳入训练队列，43例纳入测试队列。
场强/序列：3.0T MRI的TWI、对比增强TWI和扩散加权成像（DWI）。
评估
两名放射科医生独立对肿瘤进行图像分割。两名病理科医生共同审阅肿瘤组织标本以确定LVSI的存在。
统计分析
采用组内相关系数检验可靠性，并使用最小绝对收缩和选择算子（LASSO）回归进行特征选择，进而生成名为Rad-score的影像组学特征。在训练队列中构建列线图。分别通过训练队列和测试队列中受试者工作特征（ROC）曲线下面积（AUC）评估列线图模型的诊断性能。
结果
32例患者（22.2%）被诊断为LVSI。单因素分析显示，年龄较大和高级别与LVSI相关。LASSO回归确定了5个影像组学特征为LVSI的独立危险因素。基于年龄、分级和Rad-score，列线图模型在训练队列和测试队列中预测LVSI的AUC值分别为0.820（95%置信区间[CI]：0.725, 0.916；灵敏度：82.6%，特异度：72.9%）、0.807（95%CI：0.673, 0.941；灵敏度：77.8%，特异度：78.6%）。
数据结论
基于影像组学的机器学习模型使用列线图算法在术前预测EMC的LVSI方面具有较高的诊断性能，可加强风险分层并为治疗决策提供支持。
证据水平
2级。
技术效能阶段
3级。《磁共振成像杂志》2020年；52:1257 - 1262。
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