Radiomic Nomogram for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer: Predictive Value of Staging Contrast-enhanced CT.

INTRODUCTION

The purpose of this study was to predict pathologic complete response (pCR) to neoadjuvant therapy in breast cancer using radiomics based on pretreatment staging contrast-enhanced computed tomography (CECT).

PATIENTS AND METHODS

A total of 215 patients were retrospectively analyzed. Based on the intratumoral and peritumoral regions of CECT images, radiomic features were extracted and selected, respectively, to develop an intratumoral signature and a peritumoral signature with logistic regression in a training dataset (138 patients from November 2015 to October 2017). We also developed a clinical model with the molecular characterization of the tumor. A radiomic nomogram was further constructed by incorporating the intratumoral and peritumoral signatures with molecular characterization. The performance of the nomogram was validated in terms of discrimination, calibration, and clinical utility in an independent validation dataset (77 patients from November 2017 to December 2018). Stratified analysis was performed to develop a subtype-specific radiomic signature for each subgroup.

RESULTS

Compared with the clinical model (area under the curve [AUC], 0.756), the radiomic nomogram (AUC, 0.818) achieved better performance for pCR prediction in the validation dataset with continuous net reclassification improvement of 0.787 and good calibration. Decision curve analysis suggested the nomogram was clinically useful. Subtype-specific radiomic signatures showed improved AUCs (luminal subgroup, 0.936; human epidermal growth factor receptor 2-positive subgroup, 0.825; and triple negative subgroup, 0.858) for pCR prediction.

CONCLUSION

This study has revealed a predictive value of pretreatment staging-CECT and successfully developed and validated a radiomic nomogram for individualized prediction of pCR to neoadjuvant therapy in breast cancer, which could assist clinical decision-making and improve patient outcome.