Magnetic resonance imaging-based radiomics was used to evaluate the level of prognosis-related immune cell infiltration in breast cancer tumor microenvironment

Hua Qian; Xiaojing Ren; Maosheng Xu; Zhen Fang; Ruixin Zhang; Yangyang Bu; Changyu Zhou

doi:10.1186/s12880-024-01212-9

Magnetic resonance imaging-based radiomics was used to evaluate the level of prognosis-related immune cell infiltration in breast cancer tumor microenvironment

BMC Med Imaging. 2024 Feb 2;24(1):31. doi: 10.1186/s12880-024-01212-9.

Authors

Hua Qian^{1

2}, Xiaojing Ren², Maosheng Xu^{1

2}, Zhen Fang^{1

2}, Ruixin Zhang^{1

2}, Yangyang Bu^{1

2}, Changyu Zhou^{3

4}

Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), China , 54 Youdian Road, Hangzhou, 310006, Hangzhou, China.
² School of the First Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
³ Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), China , 54 Youdian Road, Hangzhou, 310006, Hangzhou, China. tophorizon@zcmu.edu.cn.
⁴ School of the First Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China. tophorizon@zcmu.edu.cn.

Abstract

Purpose: The tumor immune microenvironment is a valuable source of information for predicting prognosis in breast cancer (BRCA) patients. To identify immune cells associated with BRCA patient prognosis from the Cancer Genetic Atlas (TCGA), we established an MRI-based radiomics model for evaluating the degree of immune cell infiltration in breast cancer patients.

Methods: CIBERSORT was utilized to evaluate the degree of infiltration of 22 immune cell types in breast cancer patients from the TCGA database, and both univariate and multivariate Cox regressions were employed to determine the prognostic significance of immune cell infiltration levels in BRCA patients. We identified independent prognostic factors for BRCA patients. Additionally, we obtained imaging features from the Cancer Imaging Archive (TCIA) database for 73 patients who underwent preoperative MRI procedures, and used the Least Absolute Shrinkage and Selection Operator (LASSO) to select the best imaging features for constructing an MRI-based radiomics model for evaluating immune cell infiltration levels in breast cancer patients.

Results: According to the results of Cox regression analysis, M2 macrophages were identified as an independent prognostic factor for BRCA patients (HR = 32.288, 95% CI: 3.100-357.478). A total of nine significant features were selected to calculate the radiomics-based score. We established an intratumoral model with AUCs (95% CI) of 0.662 (0.495-0.802) and 0.678 (0.438-0.901) in the training and testing cohorts, respectively. Additionally, a peritumoral model was created with AUCs (95% CI) of 0.826 (0.710-0.924) and 0.752 (0.525-0.957), and a combined model was established with AUCs (95% CI) of 0.843 (0.723-0.938) and 0.744 (0.491-0.965). The peritumoral model demonstrated the highest diagnostic efficacy, with an accuracy, sensitivity, and specificity of 0.773, 0.727, and 0.818, respectively, in its testing cohort.

Conclusion: The MRI-based radiomics model has the potential to evaluate the degree of immune cell infiltration in breast cancer patients, offering a non-invasive imaging biomarker for assessing the tumor microenvironment in this disease.

Keywords: Breast cancer; M2 macrophages; Magnetic resonance imaging; Radiomics; Tumor microenvironment.

MeSH terms

Breast Neoplasms* / diagnostic imaging
Female
Humans
Magnetic Resonance Imaging
Prognosis
Radiomics
Tumor Microenvironment