Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Probability Maps Derived from Quantitative Ultrasound Parametric Images

Objective:
Neoadjuvant chemotherapy (NAC) is widely used in the treatment of breast cancer. However, to date, there are no fully reliable, non-invasive methods for monitoring NAC. In this article, we propose a new method for classifying NAC-responsive and unresponsive tumors using quantitative ultrasound.
Methods:
The study used ultrasound data collected from breast tumors treated with NAC. The proposed method is based on the hypothesis that areas that characterize the effect of therapy particularly well can be found. For this purpose, parametric images of texture features calculated from tumor images were converted into NAC response probability maps, and areas with a probability above 0.5 were used for classification.
Results:
The results obtained after the third cycle of NAC show that the classification of tumors using the traditional method (area under the ROC curve AUC = 0.81–0.88) can be significantly improved thanks to the proposed new approach (AUC = 0.84–0.94). This improvement is achieved over a wide range of cutoff values (0.2–0.7), and the probability maps obtained from different quantitative parameters correlate well.
Conclusion:
The results suggest that there are tumor areas that are particularly well suited to assessing response to NAC.
Significance:
The proposed approach to monitoring the effects of NAC not only leads to a better classification of responses, but also may contribute to a better understanding of the microstructure of neoplastic tumors observed in an ultrasound examination.