Prediction of pathological complete response with neoadjuvant chemotherapy by using radiomic features in breast ultrasound image

The effectiveness of pharmacotherapy has been improved through the development of drugs that incorporate the knowledge of the molecular biology of breast cancer. Therefore, neoadjuvant chemotherapy (NAC) is actively administered to patients who wish to undergo breast-conserving surgery. During NAC, some patients have a pathological complete response (pCR). This study aims to develop a method for predicting patients with pCR during NAC. This creates new value for preoperative imaging. Breast ultrasound images were collected from 43 patients with breast cancer who received NAC at the Kumamoto University Hospital. The tumor area on the breast ultrasound image was manually marked. From the marked tumor regions, 379 radiomics features related to size, shape, density, and texture were measured. We employed the least absolute shrinkage and selection operator to select the useful radiomic features. Linear discriminant analysis (LDA) with eight selected radiomic features was used to distinguish between pCR and non-pCR. Leave-one-out was used for training and testing LDA. The sensitivity, specificity, and AUC were 89.5 % (17/19), 83.3% (19/24), and 0.920, respectively. Because the LDA is the simplest classifier, the phenotype of the lesion in the breast ultrasound image may contain information that predicts the therapeutic effect. Our proposed method could provide a new value for preoperative imaging.