Dataset Information

Radiomics Assessment of the Tumor Immune Microenvironment to Predict Outcomes in Breast Cancer.

ABSTRACT:

Background

The immune microenvironment of tumors provides information on prognosis and prediction. A prior validation of the immunoscore for breast cancer (IS_BC) was made on the basis of a systematic assessment of immune landscapes extrapolated from a large number of neoplastic transcripts. Our goal was to develop a non-invasive radiomics-based IS_BC predictive factor.

Methods

Immunocell fractions of 22 different categories were evaluated using CIBERSORT on the basis of a large, open breast cancer cohort derived from comprehensive information on gene expression. The IS_BC was constructed using the LASSO Cox regression model derived from the Immunocell type scores, with 479 quantified features in the intratumoral and peritumoral regions as observed from DCE-MRI. A radiomics signature [radiomics ImmunoScore (RIS)] was developed for the prediction of IS_BC using a random forest machine-learning algorithm, and we further evaluated its relationship with prognosis.

Results

An IS_BC consisting of seven different immune cells was established through the use of a LASSO model. Multivariate analyses showed that the IS_BC was an independent risk factor in prognosis (HR=2.42, with a 95% CI of 1.49-3.93; P<0.01). A radiomic signature of 21 features of the IS_BC was then exploited and validated (the areas under the curve [AUC] were 0.899 and 0.815). We uncovered statistical associations between the RIS signature with recurrence-free and overall survival rates (both P<0.05).

Conclusions

The RIS is a valuable instrument with which to assess the immunoscore, and offers important implications for the prognosis of breast cancer.

SUBMITTER: Han X

PROVIDER: S-EPMC8761791 | biostudies-literature |

REPOSITORIES: biostudies-literature

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