A Radiogenomic Model using MRI and Gene Signature to Predict Complete Response in Breast Cancer

Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is strongly associated with improved survival. Radiogenomic approaches integrating MRI. radiomics with gene signatures may enhance pretreatment prediction of therapeutic.

This study aimed to develop a radiogenomic model for predicting pCR in breast cancer using MRI radiomics combined with an immune-related 23-gene signature (IRSN-23).

We retrospectively analyzed 112 women with histologically confirmed breast cancer (pCR, n = 21; non-pCR, n = 91) who underwent pre-NAC MRI and had IRSN-23 scores derived from tumor tissue. Patients were assigned to training (n = 100) and validation (n = 12) cohorts. From pre-NAC MRI, 100 radiomic features were extracted. A support vector classifier was trained to predict pCR using three models: radiomics alone, IRSN-23 alone, and the combined radiogenomic model. Receiver operating characteristic (ROC) analysis and areas under the curve (AUC) were calculated. Kaplan-Meier survival analyses assessed relapse-free survival (RFS) and overall survival (OS) based on model-predicted pCR.

In the training cohort, AUCs (95 % confidence interval [CI]) were 0.83 (0.73-0.92) for IRSN-23, 0.76 (0.63-0.89) for MRI radiomics, and 0.93 (0.86-1.00) for the combined model. In the validation cohort, corresponding AUCs were 0.78 (0.49-1.00), 0.59 (0.20-0.99), and 0.89 (0.69-1.00). The combined model significantly outperformed MRI (p = 0.007) and IRSN-23 (p = 0.031) in the training cohort. Differences in RFS and OS according to model-predicted pCR did not reach statistical significance, although patients predicted to achieve pCR showed a trend toward longer survival.

Integrating MRI radiomics with IRSN-23 improved pCR prediction compared with either modality alone and may support treatment planning, while the observed associations with survival outcomes should be interpreted as exploratory.