Predicting Cell Line Invasion in Breast Tumor Microenvironment from Radiological Imaging Phenotypes

Abstract Background: The abundance of immune and stromal cells in the tumor microenvironment (TME) is informative of levels of inflammation, angiogenesis, and desmoplasia. Radiomics, an approach of extracting quantitative features from radiological imaging to characterize diseases, have been shown to predict molecular classification, cancer recurrence risk, and many other disease outcomes. However, the ability of radiomics methods to predict aspects related to tumor TME remains unclear. In this study, we employed a radio-genomics approach and machine learning models to predict the infiltration of 10 cell lines in breast cancer lesions utilizing radiomic features extracted from breast Dynamic Contrast Enhanced Magnetic Resonance Imaging. Methods: We performed a retrospective study utilizing 73 patients from two independent institutions with imaging and gene expression data provided by The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA), respectively. A set of 199 radiomic features including shape-based, morphological, texture, and kinetic characteristics were extracted from the lesion volumes. To capture one-to-one relationships between radiomic features and cell line abundance, we performed linear regression on each radiomic feature/cell line abundance combination. Each regression model was tested for statistical significance. In addition, multivariate models were built for the cell line infiltration status (i.e. “high” vs “low”) prediction. A feature selection process via Recursive Feature Elimination was applied to the radiomic features on the training set. The classification models took the form of a binary logistic extreme gradient boosting framework. Two evaluation methods including leave-one-out cross validation and external independent test, were used for radiomic model learning and testing. The models’ performance was measured via area under the receiver operating characteristic curve (AUC). Results: Univariate relationships were identified between a set of radiomic features and the abundance of fibroblasts. Multivariate models yielded leave-one-out cross validation AUCs ranging from 0.5 to 0.83, and independent test AUCs ranging from 0.5 to 0.68 for the multiple cell line invasion predictions. Conclusions: Our study suggests a relationship between breast tumor’s microenvironment in terms of few cell lines and the breast MRI-derived radiological imaging phenotypes. Further evaluation with larger cohorts is needed.