Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction

Abstract

Rationale and Objectives: Breast cancer progression and treatment response are significantly influenced by the tumor microenvironment (TME). Traditional methods for assessing TME are invasive, posing a challenge for patient care. This study introduces a non-invasive approach to TME classification by integrating radiomics and machine learning, aiming to predict the TME status using imaging data, thereby aiding in prognostic outcome prediction. Materials and MethodsUtilizing multi-omics data from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), this study employed CIBERSORT and MCP-counter algorithms analyze immune infiltration in breast cancer. A radiomics classifier was developed using a random forest algorithm, leveraging quantitative features extracted from intratumoral and peritumoral regions of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) scans. The classifer’s ability to predict diverse TME states were and their prognostic implications were evaluated using Kaplan-Meier survival curves. ResultsThree distinct TME states were identified using RNA-Seq data, each displaying unique prognostic and biological characteristics. Notably, patients with increased immune cell infiltration showed significantly improved prognoses (P < 0.05). The classifier, comprising 24 radiomic features, demonstrated high predictive accuracy (AUC of training set = 0.960, 95 % CI: 0.922, 0.997; AUC of testing set = 0.853, 95 % CI: 0.687, 1.000) in differentiating these TME states. Predictions from the classifier also correlated significantly with overall patient survival (P < 0.05). ConclusionThis study offers a detailed analysis of the complex TME states in breast cancer and presents a reliable, noninvasive radiomics classifier for TME assessment. The classifer’s accurate prediction of TME status and its correlation with prognosis highlight its potential as a tool in personalized breast cancer treatment, paving the way for more individualized and less invasive therapeutic strategies.