Preoperative staging CT-based radiomics signature in predicting Ki-67 status in breast cancer

LI Jiao; WU Lei; LIU Zaiyi; LIU Chunling; YANG Xiaojun; LIU Weixiao; YE Weitao; LIANG Changhong

doi:10.19732/j.cnki.2096-6210.2020.02.005

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Preoperative staging CT-based radiomics signature in predicting Ki-67 status in breast cancer

更新时间：2025-12-15

- Preoperative staging CT-based radiomics signature in predicting Ki-67 status in breast cancer
- Vol. 29, Issue 2, Pages: 90-97(2020)
- 作者机构：
  
  1. 南方医科大学第二临床医学院,广东,广州,510515
  2. 广东省人民医院（广东省医学科学院）放射科,广东,广州,510080
  3. 华南理工大学医学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2020.02.005
  CLC：
- Published Online：28 April 2020，
  
  Published：28 April 2020
- 稿件说明：
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黎娇,吴磊,刘再毅,等. 基于术前分期CT的影像组学标签预测乳腺癌Ki-67增殖指数[J]. 肿瘤影像学, 2020, 29(2): 90-97 https://doi.

org/10.19732/j.cnki.2096-6210.2020.02.005
黎娇,吴磊,刘再毅,等. 基于术前分期CT的影像组学标签预测乳腺癌Ki-67增殖指数[J]. 肿瘤影像学, 2020, 29(2): 90-97 https://doi. DOI： 10.19732/j.cnki.2096-6210.2020.02.005.

org/10.19732/j.cnki.2096-6210.2020.02.005 DOI：

摘要

目的：

探讨基于术前分期CT的影像组学标签对乳腺癌Ki-67增殖指数无创预测的附加价值。

方法：

收集2016年5月2017年12月经手术后病理学检查证实为浸润性乳腺癌的患者245例，且均为临床术前评估分期需行常规胸部CT增强扫描，按时间顺序分为训练组（145例）和验证组（100例）。手动勾画病灶感兴趣区（region of interest，ROI），基于病灶三维图像提取影像组学特征，通过mRMR算法及Boruta算法筛选组学特征并利用logistic回归构建影像组学标签。采用受试者工作特征（receiver operating characteristic，ROC）曲线评价训练组中影像组学标签预测Ki-67增殖指数的效能，并以获得的预测阈值在验证组中进行验证。

结果：

最终获得由8个组学特征构成的影像组学标签，其对于乳腺癌术前Ki-67增殖指数具有较好的预测效能，在训练组和验证组中的ROC曲线的曲线下面积（area under curve，AUC）分别为0.782（95% CI：0.691~0.874）和0.781（95% CI：0.686~0.876）。

结论：

基于术前分期CT的影像组学标签在预测Ki-67增殖指数方面具有一定价值，也是术前常规胸部CT增强扫描在辅助临床分期之外的附加价值，有潜力成为一种无创方法来实现术前对Ki-67增殖指数的预测。

Abstract

Objective:

To explore the additional value of preoperative CT-based radiomics signature for non-invasive prediction of Ki-67 proliferation index in breast cancer.

Methods:

We retrospectively collected the data from 245 patients who were pathologically diagnosed with invasive breast cancer from May. 2016 to Dec. 2017. All patients were performed routine staging enhanced chest CT before surgery. The pat

ients were chronologically divided into training group (145 patients) and validation group (100 patients). Regions of interest (ROI) were manually delineated around the tumor profile. Using the interclass correlation coefficients

mRMR algorithm and Boruta algorithm

we performed feature selection and construction of the radiomics signature. The predictive performances of the radiomics signatures for Ki-67 proliferation index were evaluated with receiver operating characteristic (ROC) curve in training group

then validated in validation group with the obtained predictive threshold.

Results:

The radiomics signatures were constituted by eight selective features

showed good discrimination for Ki-67 proliferation index

with area under curve (AUC) of 0.782 (95% CI: 0.691-0.874) in training group and 0.781 (95% CI: 0.686-0.876) in validation group.

Conclusion:

The radiomics signatures based on preoperative staging CT have certain value for preoperative prediction of Ki-67 proliferation index in breast cancer

which can provide additional value of preoperative staging enhanced chest CT for clinical treatment decisions

and it may serve as a noninvasive approach to facilitate the preoperative prediction of Ki-67 proliferation index in clinical practice.

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