Non-invasive prediction of DCE-MRI radiomics model on CCR5 in breast cancer based on a machine learning algorithm.

Abstract

BackgroundNon-invasive methods with universal prognostic guidance for detecting breast cancer (BC) survival biomarkers need to be further explored.ObjectiveThis study aimed to investigate C-C motif chemokine receptor type 5 (CCR5) prognosis value in BC and develop a radiomics model for noninvasive prediction of CCR5 expression in BC.MethodsA total of 840 cases with genomic information were included and divided into CCR5 high- and low-expression groups for clinical characteristic differences exploration. Bioinformatics and survival analysis including Kaplan-Meier (KM) survival analysis, Cox regression, immunoinfiltration analysis, and tumor mutation load (TMB) were performed. For radiomics model development, 98 cases with dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) scans were used. Radiomics features extracted were using Pyradiomics and filtered by maximum-relevance minimum-redundancy (mRMR) and recursive feature elimination (REF) algorithms. Support vector machine (SVM) and logistic regression (LR) models were developed to predict CCR5 expression, with the radiomics score (Rad_score) representing the predicted probability of CCR5 expression. The models' performance was compared using the Delong test, and the model with the superior area under the curve (AUC) values was selected to analyze the correlation between CCR5 expression, Rad_score, and immune genes.ResultsThe CCR5 high-expression group exhibited better overall survival (OS) (p < 0.01). Six radiomics features were selected for model development. The AUCs of the SVM model predicting CCR5 were 0.753 and 0.748 in the training and validation sets, respectively, while the AUCs of the LR model were 0.763 and 0.762. Calibration curves and decision curve analysis (DCA) validated the models' calibration and clinical utility. The SVM_Rad_score showed a strong association with immune-related genes.ConclusionsThe DCE-MRI radiomics model presents a novel, non-invasive tool for predicting CCR5 expression in BC and provides valuable insights to inform clinical decision-making.