乳腺癌放射组学：基于 MRI 放射组学模型预测 Ki-67 表达。

Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models.

机构信息

Istanbul Universitesi- Cerrahpasa Medical Faculty, Department of Radiology, Kocamustafapasa, Istanbul, Turkey.

Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, Turkey.

出版信息

Acad Radiol. 2022 Jan;29 Suppl 1:S116-S125. doi: 10.1016/j.acra.2021.02.001. Epub 2021 Mar 18.

DOI:10.1016/j.acra.2021.02.001

PMID:33744071

Abstract

RATIONALE AND OBJECTIVES

We aimed to investigate the value of magnetic resonance image (MRI)-based radiomics in predicting Ki-67 expression of breast cancer.

METHODS

In this retrospective study, 159 lesions from 154 patients were included. Radiomic features were extracted from contrast-enhanced T1-weighted MRI (C+MRI) and apparent diffusion coefficient (ADC) maps, with open-source software. Dimension reduction was done with reliability analysis, collinearity analysis, and feature selection. Two different Ki-67 expression cut-off values (14% vs 20%) were studied as reference standard for the classifications. Input for the models were radiomic features from individual MRI sequences or their combination. Classifications were performed using a generalized linear model.

RESULTS

Considering Ki-67 cut-off value of 14%, training and testing AUC values were 0.785 (standard deviation [SD], 0.193) and 0.849 for ADC; 0.696 (SD, 0.150) and 0.695 for C+MRI; 0.755 (SD, 0.171) and 0.635 for the combination of both sequences, respectively. Regarding Ki-67 cut-off value of 20%, training and testing AUC values were 0.744 (SD, 0.197) and 0.617 for ADC; 0.629 (SD, 0.251) and 0.741 for C+MRI; 0.761 (SD, 0.207) and 0.618 for the combination of both sequences, respectively.

CONCLUSION

ADC map-based selected radiomic features coupled with generalized linear modeling might be a promising non-invasive method to determine the Ki-67 expression level of breast cancer.

摘要

背景与目的

本研究旨在探讨磁共振成像（MRI）放射组学在预测乳腺癌 Ki-67 表达中的价值。

方法

本回顾性研究纳入了 154 例患者的 159 个病灶。使用开源软件从对比增强 T1 加权 MRI（C+MRI）和表观扩散系数（ADC）图中提取放射组学特征。采用可靠性分析、共线性分析和特征选择进行降维处理。以 Ki-67 表达的两个不同截断值（14%比 20%）作为分类的参考标准。模型的输入是来自单个 MRI 序列或其组合的放射组学特征。使用广义线性模型进行分类。

结果

以 Ki-67 截断值为 14%时，ADC 的训练和测试 AUC 值分别为 0.785（标准差 [SD]，0.193）和 0.849；C+MRI 的训练和测试 AUC 值分别为 0.696（SD，0.150）和 0.695；两种序列组合的训练和测试 AUC 值分别为 0.755（SD，0.171）和 0.635。以 Ki-67 截断值为 20%时，ADC 的训练和测试 AUC 值分别为 0.744（SD，0.197）和 0.617；C+MRI 的训练和测试 AUC 值分别为 0.629（SD，0.251）和 0.741；两种序列组合的训练和测试 AUC 值分别为 0.761（SD，0.207）和 0.618。