Prediction of molecular subtypes of breast cancer using decision tree model based on ultrasonic and clinical features

SHI Wei; XIA Wei; HUANG Min; CHANG Cai; WANG Yu; YOU Xiaohui; GU Huayun; DONG Zhifen; GUO Jianfeng

doi:10.19732/j.cnki.2096-6210.2022.03.006

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Prediction of molecular subtypes of breast cancer using decision tree model based on ultrasonic and clinical features

更新时间：2025-12-15

- Prediction of molecular subtypes of breast cancer using decision tree model based on ultrasonic and clinical features
- Vol. 31, Issue 3, Pages: 249-257(2022)
- 作者机构：
  
  1. 中国科学院苏州生物医学工程技术研究所医学影像技术研究室,江苏,苏州,215163
  2. 南京医科大学附属苏州医院，苏州市立医院超声科,江苏,苏州,215001
  3. 复旦大学附属肿瘤医院超声科,上海,200032
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2022.03.006
  CLC：
- Published Online：28 June 2022，
  
  Published：28 June 2022
- 稿件说明：
移动端阅览
施维,夏威,黄敏,等. 基于超声和临床特征决策树模型预测乳腺癌分子亚型[J]. 肿瘤影像学, 2022, 31(3): 249-257 https://doi.

org/10.19732/j.cnki.2096-6210.2022.03.006
施维,夏威,黄敏,等. 基于超声和临床特征决策树模型预测乳腺癌分子亚型[J]. 肿瘤影像学, 2022, 31(3): 249-257 https://doi. DOI： 10.19732/j.cnki.2096-6210.2022.03.006.

org/10.19732/j.cnki.2096-6210.2022.03.006 DOI：

摘要

目的：

建立不同亚型浸润性乳腺癌的超声决策树预测模型，并分析模型的临床价值。

方法：

回顾并分析420例经病理学检查证实的浸润性乳腺癌患者，其中管腔A（Luminal A，LA）型患者137例、管腔B（Luminal B，LB）型患者157例、人表皮生长因子受体2过表达（human epidermal growth factor receptor 2 over-expression，HER2+）型患者61例和三阴性乳腺癌（triple-negative breast cancer，TNBC）型患者65例。使用方差分析和Fisher精确概率检验统计分析患者的超声特征和临床特征，将差异有统计学意义的特征纳入决策树模型以预测乳腺癌分子亚型。采用受试者工作特征（receiver operating characteristic，ROC）曲线的曲线下面积（area under curve，AUC）评价模型的预测效果。

结果：

对于4种乳腺癌分子亚型，找到7种差异有统计学意义（

＜0.05）的临床和超声特征：临床分期、肿瘤最大径、肿瘤内部回声变化、肿瘤后方回声变化、钙化形态、钙化部位、有无转移性淋巴结。LA、LB、HER2

和TNBC型决策树模型的训练集AUC分别为0.731、0.708、0.722和0.877。此外，与高年资超声科医师相比，决策树模型鉴别TNBC型的灵敏度（81.0%）较高。

结论：

基于超声和临床特征构建的决策树模型可以准确地预测乳腺癌分子亚型。

Abstract

Objective:

To establish the ultrasonic decision tree model for predicting different subtypes of invasive breast cancer

and to analyze the clinical value of the model.

Methods:

A total of 420 invasive breast cancer patients were analyzed retrospectively

which were confirmed by pathology. All patients were divided

into 4 molecular subtypes: Luminal A (LA) type (

=137)

Luminal B (LB) type (

=157)

human epidermal growth factor receptor 2 over-expression (HER2

) type (

=61) and triple-negative breast cancer (TNBC) type (

=65). The ultrasonic and clinical features of patients were evaluated by analysis of variance and Fisher exact probability test

and the statistically significant features were included in the decision tree model for molecular subtype identification of breast cancer. The corresponding area under the receiver operating characteristic (ROC) curves (AUC) were calculated to assess the performance of each decision tree.

Results:

Seven features

including clinical stage

maximum diameter

echo patterns

posterior acoustic features

calcification pattern

the position of calcification

and lymph node metastasis

were statistically significantdifferences (

＜0.05) among four molecular subtypes. The AUC of LA

HER2

and TNBC type decision tree models were 0.721

0.708

0.722

0.877 respectively in training sets. Moreover

the sensitivity of the decision tree model (81.0%) for TNBC type was higher than that of the senior sonographer.

Conclusion:

The decision tree built by ultrasonic and clinical features has favorable value in the prediction of breast cancer molecular subtypes with high diagnostic accuracy.

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