Prediction of Ki-67 expression status in breast cancer based on ultrasound radiomics combined with clinicopathologic features
10.3760/cma.j.cn131148-20231020-00175
- VernacularTitle:基于超声影像组学联合临床病理学特征预测乳腺癌Ki-67表达状态
- Author:
Heng ZHANG
1
;
Sai ZHANG
;
Tong ZHAO
;
Xiaoqin LI
;
Xiaoli ZHOU
;
Xinye NI
Author Information
1. 南京医科大学附属常州第二人民医院放疗科 江苏省医学物理工程研究中心 南京医科大学医学物理研究中心 江苏省常州市医学物理重点实验室，常州 213003
- Keywords:
Ultrasonography;
Radiomics;
Breast cancer;
Ki-67
- From:
Chinese Journal of Ultrasonography
2024;33(2):165-173
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the prediction of the tumor proliferation antigen(Ki-67) expression status in breast cancer patients based on ultrasound radiomics combined with clinicopathologic features.Methods:Breast cancer patients who underwent 2D ultrasound and Ki-67 examination from January 2018 to February 2022 in Changzhou Second People′s Hospital, Nanjing Medical University were retrospectively analyzed. Among them, 427 patients from Chengzhong campus were randomly divided into training and validation sets in the ratio of 8∶2, and 229 patients from Yanghu campus were used as an independent external test set. Radiomics features were extracted from the region of interest of 2D ultrasound images, and the Mann-Whitney U test, recursive feature elimination, and minimum absolute shrinkage and selection operators were used to perform feature dimensionality reduction and to establish a radiomics score(Rad-score). Subsequently, single/multifactor logistic regression regression analyses were used to construct a joint prediction model based on Rad-score and clinicopathological features. Model performance and utility were assessed using the subject operating characteristic area under the curve (AUC), calibration curve, and decision curve analyses. Results:The AUCs of the joint model for predicting Ki-67 expression status in breast cancer in the training, validation, and test sets were 0.858, 0.797, and 0.802, respectively, which were superior to those of the radiomics (0.772, 0.731, and 0.713) and clinical models (0.738, 0.750, and 0.707). Calibration curve and decision curve analyses indicated that the joint model had good calibration and clinical value.Conclusions:A joint model based on ultrasound radiomics and clinicopathological features can effectively predict the Ki-67 expression status of breast cancer, which is expected to become a non-invasive tool for Ki-67 detection and provide clinicians with an important auxiliary diagnostic and therapeutic decision-making basis.
