基于多模态磁共振影像组学的早期宫颈癌淋巴管间隙侵犯术前评估的多中心研究
A Multicenter Study on Preoperative Assessment of Lymphovascular Space Invasion in Early-Stage Cervical Cancer Based on Multimodal MR Radiomics.
作者信息
Wu Yu, Wang Shuxing, Chen Yiqing, Liao Yuting, Yin Xuntao, Li Ting, Wang Rui, Luo Xiaomei, Xu Wenchan, Zhou Jing, Wang Simin, Bu Jun, Zhang Xiaochun
机构信息
Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou, China.
Department of Radiology, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, China.
出版信息
J Magn Reson Imaging. 2023 Nov;58(5):1638-1648. doi: 10.1002/jmri.28676. Epub 2023 Mar 16.
BACKGROUND
As lymphovascular space invasion (LVSI) was closely related to lymph node metastasis and prognosis, the preoperative assessment of LVSI in early-stage cervical cancer is crucial for patients.
PURPOSE
To develop and validate nomogram based on multimodal MR radiomics to assess LVSI status in cervical cancer patients.
STUDY TYPE
Retrospective.
POPULATION
The study included 168 cervical cancer patients, of whom 129 cases (age 51.36 ± 9.99 years) from institution 1 were included as the training cohort and 39 cases (age 52.59 ± 10.23 years) from institution 2 were included as the external test cohort.
FIELD STRENGTH/SEQUENCE: There were 1.5 T and 3.0 T MRI scans (T1-weighted imaging [T1WI], fat-saturated T2-weighted imaging [FS-T2WI], and contrast-enhanced [CE]).
ASSESSMENT
Six machine learning models were built and selected to construct the radiomics signature. The nomogram model was constructed by combining the radiomics signature with the clinical signature, which was then validated for discrimination, calibration, and clinical usefulness.
STATISTICAL TESTS
The clinical characteristics were compared using t-tests, Mann-Whitney U tests, or chi-square tests. The Spearman and LASSO methods were used to select radiomics features. The receiver operating characteristic (ROC) analysis was performed, and the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated.
RESULTS
The logistic regression (LR) model performed best in each sequence. The AUC of CE-T1-T2WI-combined was the highest in the LR model, with an AUC of 0.775 (95% CI: 0.570-0.979) in external test cohort. The nomogram showed high predictive performance in the training (AUC: 0.883 [95% CI: 0.823-0.943]) and test cohort (AUC: 0.830 [95% CI: 0.657-1.000]) for predicting LVSI. Decision curve analysis demonstrated that the nomogram was clinically useful.
DATA CONCLUSION
Our findings suggest that the proposed nomogram model based on multimodal MRI of CE T1WI-T2WI-combined could be used to assess LVSI status in early cervical cancer.
EVIDENCE LEVEL
TECHNICAL EFFICACY
Stage 2.
背景
由于淋巴管间隙浸润（LVSI）与淋巴结转移及预后密切相关，早期宫颈癌患者术前对LVSI进行评估至关重要。
目的
开发并验证基于多模态MR影像组学的列线图，以评估宫颈癌患者的LVSI状态。
研究类型
回顾性研究。
研究对象
该研究纳入了168例宫颈癌患者，其中来自机构1的129例患者（年龄51.36±9.99岁）作为训练队列，来自机构2的39例患者（年龄52.59±10.23岁）作为外部测试队列。
场强/序列：进行了1.5T和3.0T MRI扫描（T1加权成像[T1WI]、脂肪饱和T2加权成像[FS-T2WI]以及对比增强[CE]）。
评估
构建并选择了6种机器学习模型以构建影像组学特征。通过将影像组学特征与临床特征相结合构建列线图模型，随后对其区分能力、校准度及临床实用性进行验证。
统计学检验
采用t检验、曼-惠特尼U检验或卡方检验比较临床特征。使用斯皮尔曼和套索方法选择影像组学特征。进行受试者操作特征（ROC）分析，并计算曲线下面积（AUC）、准确性、敏感性和特异性。
结果
逻辑回归（LR）模型在各序列中表现最佳。在LR模型中，CE-T1-T2WI联合序列的AUC最高，在外部测试队列中的AUC为0.775（95%CI：0.570-0.979）。列线图在训练队列（AUC：0.883[95%CI：0.823-0.943]）和测试队列（AUC：0.830[95%CI：0.657-1.000]）中对LVSI的预测表现出较高性能。决策曲线分析表明该列线图具有临床实用性。
数据结论
我们的研究结果表明，所提出的基于CE T1WI-T2WI联合多模态MRI的列线图模型可用于评估早期宫颈癌的LVSI状态。
证据水平
4级。
技术效能
2级。
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