Ultrasound-based radiomics nomogram for prediction of lymphovascular invasion in invasive breast cancer

ZHA Hailing; PAN Jiazhen; LIU Wei; LIU Xinpei; WANG Hui; LI Cuiying

doi:10.19732/j.cnki.2096-6210.2021.01.002

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Ultrasound-based radiomics nomogram for prediction of lymphovascular invasion in invasive breast cancer

更新时间：2025-12-15

- Ultrasound-based radiomics nomogram for prediction of lymphovascular invasion in invasive breast cancer
- Vol. 30, Issue 1, Pages: 6-15(2021)
- 作者机构：
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- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2021.01.002
  CLC：
- Published Online：28 February 2021，
  
  Published：28 February 2021
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查海玲,潘加珍,刘薇,等. 基于超声影像组学模型预测浸润性乳腺癌淋巴管血管侵犯状态[J]. 肿瘤影像学, 2021, 30(1): 6-15 https://doi.

org/10.19732/j.cnki.2096-6210.2021.01.002
查海玲,潘加珍,刘薇,等. 基于超声影像组学模型预测浸润性乳腺癌淋巴管血管侵犯状态[J]. 肿瘤影像学, 2021, 30(1): 6-15 https://doi. DOI： 10.19732/j.cnki.2096-6210.2021.01.002.

org/10.19732/j.cnki.2096-6210.2021.01.002 DOI：

摘要

目的：

建立并验证浸润性乳腺癌（invasive breast cancer，IBC）患者术前预测淋巴管血管侵犯（lymphovascular invasion，LVI）的影像组学模型。

方法：

纳入258例经病理组织学检查证实的IBC患者，包括LVI阴性和阳性，按7∶3随机分配为训练集和验证集。运用最大相关性最小冗余度（the maximum relevance minimum redundancy， mRMR）和最小绝对收缩和选择算子（ the least absolute shrinkage and selection operator，LASSO）算法筛选自患者二维超声图像中获取的影像组学特征，建立影像组学评分，同时联合临床特征建立术前预测IBC患者LVI状态的模型，并评估模型的效能。

结果：

由最终筛选得到的10个影像组学特征建立的影像组学评分，在训练集及验证集中均表现良好，训练组及验证组曲线下面积（area under curve，AUC）分别为0.801和0.786。在临床模型中，腋窝淋巴结（axillary lymph node，ALN）状态和超声乳腺影像报告和数据系统（Breast Imaging Reporting and Data System，BI-RADS）分类对IBC患者的LVI状态预测是有意义的。最终，整合了超声影像组学评分和临床模型有效临床特征的影像组学模型在训练集和验证集中预测能力均表现最佳（训练组AUC：0.865；验证组AUC：0.857）。决策曲线分析（decision curve analysis，DCA）表明，影像组学模型具有临床应用价值且优于其他两种单纯模型。

结论：

上述影像组学模型可用于术前预测IBC患者的LVI状态，并可作为指导后续个体化治疗的有效临床工具。

Abstract

Objective:

To develop and validate a radiomics nomogram for preoperative prediction of lymphovascular invasion (LVI) in patients with invasive breast cancer (IBC).

Methods:

In this study

258 patients with histological

ly confirmed IBC with or without LVI

were randomly assigned to training and validation dataset at 7∶3. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithms were used to screen the radiomics features obtained from two-dimensional ultrasound images

and the ultrasound-based radiomics score was established by these features. A final nomogram

combining the radiomics features and clinical features

for predicting LVI of IBC was established. Nomogram performance was assessed via both calibration and discrimination statistics.

Results:

Obtained from 10 features

the radiomics score indicated a favorable discriminatory capability in the training set with an area under curve (AUC) of 0.801

whereas a value of 0.786 was observed in the validation set. In the clinical model

ultrasound-reported axillary lymph node (ALN)

ultrasound Breast Imaging Reporting and Data System (BI-RADS) classification were effective for LVI prediction in IBC. The final nomogram integrated the ultrosound radiomics score and two clinical features. Good calibration was achieved for the nomogram in both the training and validated datasets (AUC of training set: 0.865; AUC of validation set: 0.857). DCA demonstrated that the combined model was superior to the others in terms of clinical practicability.

Conclusion:

The above described radiomics nomogram can preoperativelypredict LVI in patients with IBC and may constitute a usefully clinical tool to guide subsequent personalized treatment.

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