Predictive value of tumoral and peritumoral radiomic features in neoadjuvant chemotherapy response for breast cancer: a retrospective study.

Abstract

Background: Neoadjuvant chemotherapy (NACT) improves surgical outcomes for breast cancer patients, with pathologic complete response (pCR) correlated with enhanced survival. The role of radiomics, particularly from peritumoral tissue, in predicting pCR remains under investigation.

Methods: This retrospective study analyzed radiomic features from pretreatment dynamic contrast-enhanced breast MRI scans of 150 patients undergoing NACT. A proportional approach was used to define peritumoral zones, assessed both with a 10% and 30% extension, allowing more standardized assessments relative to the tumor size. Radiomic features were evaluated alongside clinical and biological data to predict pCR. The association of clinical/biological and radiomic features with pCR to NACT was evaluated using univariate and multivariate analysis, logistic regression, and a random forest model. A clinical/biological model, a radiomic model, and a combined clinical/biological and 4 radiomic models for predicting the response to NACT were constructed. Area under the curve (AUC) and 95% confidence intervals (CIs) were used to assess the performance of the models.

Results: Ninety-five patients (average age 47 years) were finally included. HER2 + , basal-like molecular subtypes, and a high level of Ki67 (≥ 20%) were associated with a higher likelihood of pCR to NACT. The combined clinical-biological-radiomic model, especially with a 10% peritumoral extension, showed improved predictive accuracy (AUC 0.76, CI 0.65-0.85) compared to models using clinical-biological data alone (AUC 0.73, CI 0.63-0.83).

Conclusions: Integrating peritumoral radiomic features with clinical and biological data enhances the prediction of pCR to NACT, underscoring the potential of a multifaceted approach in treatment personalization.