Related Experiment Video
Updated: Feb 14, 2026
Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
Published on: May 18, 2020
An MRI-based Radiomics Classifier for Preoperative Prediction of Ki-67 Status in Breast Cancer
Cuishan Liang1, Zixuan Cheng2, Yanqi Huang3
1Graduate College, Southern Medical University, 1023 Shatai Nan Road, Baiyun District, Guangzhou 510515, Guangdong, China; Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China; The Second Clinical Medical School, Southern Medical University, 1023 Shatai Nan Road, Baiyun District, Guangzhou 510515, Guangdong, China.
Rationale And Objectives:
This study aims to investigate the value of a magnetic resonance imaging-based radiomics classifier for preoperatively predicting the Ki-67 status in patients with breast cancer.
Materials And Methods:
We chronologically divided 318 patients with clinicopathologically confirmed breast cancer into a training dataset (n = 200) and a validation dataset (n = 118). Radiomics features were extracted from T2-weighted (T2W) and contrast-enhanced T1-weighted (T1+C) images of breast cancer. Radiomics feature selection and radiomics classifiers were generated using the least absolute shrinkage and selection operator regression analysis method. The correlation between the radiomics classifiers and the Ki-67 status in patients with breast cancer was explored. The predictive performances of the radiomics classifiers for the Ki-67 status were evaluated with receiver operating characteristic curves in the training dataset and validated in the validation dataset.
Results:
Through the radiomics feature selection, 16 and 14 features based on T2W and T1+C images, respectively, were selected to constitute the radiomics classifiers. The radiomics classifier based on T2W images was significantly correlated with the Ki-67 status in both the training and the validation datasets (both P < .0001). The radiomics classifier based on T1+C images was significantly correlated with the Ki-67 status in the training dataset (P < .0001) but not in the validation dataset (P = .083). The T2W image-based radiomics classifier exhibited good discrimination for Ki-67 status, with areas under the receiver operating characteristic curves of 0.762 (95% confidence interval: 0.685, 0.838) and 0.740 (95% confidence interval: 0.645, 0.836) in the training and validation datasets, respectively.
Conclusions:
The T2W image-based radiomics classifier was a significant predictor of Ki-67 status in patients with breast cancer. Thus, it may serve as a noninvasive approach to facilitate the preoperative prediction of Ki-67 status in clinical practice.
More Related Videos
Related Concept Videos
03:35Classifying Matter by Composition
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures.
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated.
A mixture is composed of two or...
02:49Classifying Matter by State
02:27Predicting Molecular Geometry
00:59How Data are Classified: Numerical Data
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
01:11How Data are Classified: Categorical Data
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
01:26Marcia's Theory of Identity Status