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基于 MRI 的宫颈癌放射组学列线图术前预测淋巴结脉管间隙侵犯

MR-Based Radiomics Nomogram of Cervical Cancer in Prediction of the Lymph-Vascular Space Invasion preoperatively.

机构信息

Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China.

CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China.

出版信息

J Magn Reson Imaging. 2019 May;49(5):1420-1426. doi: 10.1002/jmri.26531. Epub 2018 Oct 26.


DOI:10.1002/jmri.26531
PMID:30362652
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6587470/
Abstract

BACKGROUND: Lymph-vascular space invasion (LVSI) is an unfavorable prognostic factor in cervical cancer. Unfortunately, there are no current clinical tools for the preoperative prediction of LVSI. PURPOSE: To develop and validate an axial T contrast-enhanced (CE) MR-based radiomics nomogram that incorporated a radiomics signature and some clinical parameters for predicting LVSI of cervical cancer preoperatively. STUDY TYPE: Retrospective. POPULATION: In all, 105 patients were randomly divided into two cohorts at a 2:1 ratio. FIELD STRENGTH/SEQUENCE: T CE MRI sequences at 1.5T. ASSESSMENT: Univariate analysis was performed on the radiomics features and clinical parameters. Multivariate analysis was performed to determine the optimal feature subset. The receiver operating characteristic (ROC) analysis was performed to evaluate the performance of prediction model and radiomics nomogram. STATISTICAL TESTS: The Mann-Whitney U-test and the chi-square test were used to evaluate the performance of clinical characteristics and LVSI status by pathology. The minimum-redundancy/maximum-relevance and recursive feature elimination methods were applied to select the features. The radiomics model was constructed using logistic regression. RESULTS: Three radiomics features and one clinical characteristic were selected. The radiomics nomogram showed favorable discrimination between LVSI and non-LVSI groups. The AUC was 0.754 (95% confidence interval [CI], 0.6326-0.8745) in the training cohort and 0.727 (95% CI, 0.5449-0.9097) in the validation cohort. The specificity and sensitivity were 0.756 and 0.828 in the training cohort and 0.773 and 0.692 in the validation cohort. DATA CONCLUSION: T CE MR-based radiomics nomogram serves as a noninvasive biomarker in the prediction of LVSI in patients with cervical cancer preoperatively. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1420-1426.

摘要

背景:淋巴血管空间侵犯(LVSI)是宫颈癌的一个不利预后因素。不幸的是,目前还没有用于术前预测 LVSI 的临床工具。

目的:开发和验证一种基于轴位 T 对比增强(CE)MR 的放射组学列线图,该列线图结合了放射组学特征和一些临床参数,用于术前预测宫颈癌的 LVSI。

研究类型:回顾性。

人群:总共,105 名患者以 2:1 的比例随机分为两组。

磁场强度/序列:1.5T 的 T CE MRI 序列。

评估:对放射组学特征和临床参数进行单变量分析。进行多变量分析以确定最佳特征子集。通过接收者操作特征(ROC)分析评估预测模型和放射组学列线图的性能。

统计学检验:Mann-Whitney U 检验和卡方检验用于评估临床特征和病理 LVSI 状态的性能。应用最小冗余/最大相关性和递归特征消除方法来选择特征。使用逻辑回归构建放射组学模型。

结果:选择了三个放射组学特征和一个临床特征。放射组学列线图显示出区分 LVSI 和非 LVSI 组的良好能力。在训练队列中的 AUC 为 0.754(95%置信区间[CI],0.6326-0.8745),在验证队列中的 AUC 为 0.727(95%CI,0.5449-0.9097)。在训练队列中的特异性和敏感性分别为 0.756 和 0.828,在验证队列中的特异性和敏感性分别为 0.773 和 0.692。

数据结论:基于 T CE MR 的放射组学列线图可作为预测宫颈癌患者术前 LVSI 的非侵入性生物标志物。

证据水平:4 技术功效:第 2 阶段 J. Magn. Reson. Imaging 2019;49:1420-1426。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1402/6587470/c33be4ce6ffe/JMRI-49-1420-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1402/6587470/ae38b2596f67/JMRI-49-1420-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1402/6587470/eafa4fad49bb/JMRI-49-1420-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1402/6587470/d943933ecb11/JMRI-49-1420-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1402/6587470/f4aff6ee2f30/JMRI-49-1420-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1402/6587470/c33be4ce6ffe/JMRI-49-1420-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1402/6587470/ae38b2596f67/JMRI-49-1420-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1402/6587470/eafa4fad49bb/JMRI-49-1420-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1402/6587470/d943933ecb11/JMRI-49-1420-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1402/6587470/f4aff6ee2f30/JMRI-49-1420-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1402/6587470/c33be4ce6ffe/JMRI-49-1420-g005.jpg

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引用本文的文献

[1]
Multi-parametric MRI-based radiomics nomogram for predicting lymphovascular space invasion in early-stage cervical adenocarcinoma.

Front Oncol. 2025-8-21

[2]
Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study.

Sci Rep. 2025-8-10

[3]
Predicting lymphovascular space invasion in early-stage cervical squamous cell carcinoma using heart rate variability.

Front Oncol. 2025-7-21

[4]
Imaging-Based AI for Predicting Lymphovascular Space Invasion in Cervical Cancer: Systematic Review and Meta-Analysis.

J Med Internet Res. 2025-6-16

[5]
Clinical-radiomics nomogram construction from magnetic resonance imaging to diagnose osteoporosis: a preliminary study.

Eur Spine J. 2025-5-29

[6]
Nomogram prediction of the lymph-vascular space invasion in cervical cancer: comparison of 2009 and 2018 staging systems.

Front Oncol. 2025-3-6

[7]
Deep transfer learning radiomics for distinguishing sinonasal malignancies: a preliminary MRI study.

Future Oncol. 2025-4

[8]
Radiomics based on MRI in predicting lymphovascular space invasion of cervical cancer: a meta-analysis.

Front Oncol. 2024-10-17

[9]
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J Appl Clin Med Phys. 2024-12

[10]
The role of radiomics for predicting of lymph-vascular space invasion in cervical cancer patients based on artificial intelligence: a systematic review and meta-analysis.

J Gynecol Oncol. 2025-3

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