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Radiomics signatures for predicting the Ki-67 level and HER-2 status based on bone metastasis from primary breast cancer

Authors :
Hongxiao Zhang
Shuxian Niu
Huanhuan Chen
Lihua Wang
Xiaoyu Wang
Yujiao Wu
Jiaxin Shi
Zhuoning Li
Yanjun Hu
Zhiguang Yang
Xiran Jiang
Source :
Frontiers in Cell and Developmental Biology, Vol 11 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

This study explores the potential of radiomics to predict the proliferation marker protein Ki-67 levels and human epidermal growth factor receptor 2 (HER-2) status based on MRI images of patients with spinal metastasis from primary breast cancer. A total of 110 patients with pathologically confirmed spinal metastases from primary breast cancer were enrolled between Dec. 2017 and Dec. 2021. All patients underwent T1-weighted contrast-enhanced MRI scans. The PyRadiomics package was used to extract features from the MRI images based on the intraclass correlation coefficient and least absolute shrinkage and selection operator. The most predictive features were used to develop the radiomics signature. The Chi-Square test, Fisher’s exact test, Student’s t-test, and Mann–Whitney U test were used to evaluate the clinical and pathological characteristics between the high- and low-level Ki-67 groups and the HER-2 positive/negative groups. The radiomics models were compared using receiver operating characteristic curve analysis. The area under the receiver operating characteristic curve (AUC), sensitivity (SEN), and specificity (SPE) were generated as comparison metrics. From the spinal MRI scans, five and two features were identified as the most predictive for the Ki-67 level and HER-2 status, respectively. The developed radiomics signatures generated good prediction performance for the Ki-67 level in the training (AUC = 0.812, 95% CI: 0.710–0.914, SEN = 0.667, SPE = 0.846) and validation (AUC = 0.799, 95% CI: 0.652–0.947, SEN = 0.722, SPE = 0.833) cohorts. Good prediction performance for the HER-2 status was also achieved in the training (AUC = 0.796, 95% CI: 0.686–0.906, SEN = 0.720, SPE = 0.776) and validation (AUC = 0.705, 95% CI: 0.506–0.904, SEN = 0.733, SPE = 0.762) cohorts. The results of this study provide a better understanding of the potential clinical implications of spinal MRI-based radiomics on the prediction of Ki-67 levels and HER-2 status in breast cancer.

Details

Language :
English
ISSN :
2296634X
Volume :
11
Database :
Directory of Open Access Journals
Journal :
Frontiers in Cell and Developmental Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.f786c7e6a66b4d8d9e58502061ae7ee9
Document Type :
article
Full Text :
https://doi.org/10.3389/fcell.2023.1220320