MRI-based radiomics model for predicting the lymph-vascular space invasion status of cervical cancer patients

JIANG Wenyan; CUI Linpeng; JIN Ying; LIU Guangxing; MA Chao; LUO Yahong

doi:10.19732/j.cnki.2096-6210.2021.01.007

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MRI-based radiomics model for predicting the lymph-vascular space invasion status of cervical cancer patients

更新时间：2025-12-15

- MRI-based radiomics model for predicting the lymph-vascular space invasion status of cervical cancer patients
- Vol. 30, Issue 1, Pages: 43-49(2021)
- 作者机构：
  
  1. 辽宁省肿瘤医院（中国医科大学肿瘤医院）肿瘤防治办公室,辽宁,沈阳,110042
  2. 中国医科大学生物医学工程系,辽宁,沈阳,110122
  3. 辽宁省计划生育科学研究院,辽宁,沈阳,110031
  4. 辽宁省肿瘤医院（中国医科大学肿瘤医院）医学影像科,辽宁,沈阳,110042
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2021.01.007
  CLC：
- Published Online：28 February 2021，
  
  Published：28 February 2021
- 稿件说明：
移动端阅览
姜文研,崔林鹏,金瑛,等. 基于MRI影像组学模型预测宫颈淋巴血管间隙浸润的研究[J]. 肿瘤影像学, 2021, 30(1): 43-49 https://doi.

org/10.19732/j.cnki.2096-6210.2021.01.007
姜文研,崔林鹏,金瑛,等. 基于MRI影像组学模型预测宫颈淋巴血管间隙浸润的研究[J]. 肿瘤影像学, 2021, 30(1): 43-49 https://doi. DOI： 10.19732/j.cnki.2096-6210.2021.01.007.

org/10.19732/j.cnki.2096-6210.2021.01.007 DOI：

摘要

目的：

使用多模态磁共振成像（magnetic resonance imaging，MRI）影像组学方法对早期宫颈癌患者淋巴血管间隙浸润（lymph-vascular space invasion，LVSI）进行辅助预测。

方法：

收集2015年11月2018年8月在辽宁省肿瘤医院就诊的151例早期宫颈癌患者的双序列MRI影像，通过提取和筛选影像组学特征，构建诺模图预测模型，并绘制受试者工作特征（receiver operating characteristic，ROC）曲线，通过曲线下面积（area under curve，AUC）、特异度和灵敏度评价模型的预测性能，绘制校正曲线展示模型的预测性能，绘制决策曲线分析模型的临床价值。

结果：

所建立的诺模图预测模型在训练集和测试集上的AUC值分别为0.812（特异度为0.705，灵敏度为0.818）和0.775（特异度为0.759，灵敏度为0.765），校正曲线表明诺模图有良好的预测能力，决策曲线分析表明所建立的诺模图有着良好的临床应用价值。

结论：

使用双序列MRI构建影像组学模型是作为早期宫颈癌LVSI预测的有效方法。

Abstract

Objective:

To establish a radiomics model for predicting the lymph-vascular space invasion (LVSI) status of cervical cancer patients using multi-sequence magnetic resonance imaging (MRI).

Methods:

Double sequence MRI data of 151 patients obtained from Liaoning Provincial Cancer Hospital from November 2015 to August 2018 were selected. Features from the two MRI sequences were extracted and selected. A nomogram model for predicting the LVSI status were constructed and evaluated using receiver

operating characteristic (ROC) curve

area under curve (AUC)

sensitivity

specificity and calibration curve. The clinical value of nomogram model was evaluated by a decision curve analysis model.

Results:

The AUC values of established nomogram model were 0.812 (specificity=0.705

sensitivity=0.818) and 0.775 (specificity=0.759

sensitivity=0.765) in the train and test cohorts

respectively. The calibration curve shows that the nomogram has a good prediction ability. The decision curve analysis suggested that model has good clinical value.

Conclusion:

Radiomics model based on double sequence MRI features has good potential inprediction of LVSI in cervical patients.

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