Ozcan, B. Bersu

Elevated intratumoral interstitial fluid pressure (IFP) can negatively impact the efficacy of neoadjuvant chemotherapy (NAC) of breast cancer. This study aims to predict breast cancer pathological complete response (pCR) to NAC based on IFP measures by 3-D subharmonic-aided pressure estimation (SHAPE). SHAPE uses the inverse linear relationship between hydrostatic pressure and subharmonic signal magnitude from intravascular ultrasound contrast agents. Thirty-eight breast cancer patients undergoing NAC from three clinical sites were enrolled for this study. SHAPE data were collected for each subject at 0%, 10%, and 30% of NAC completion using a Logiq E9 system (GE HeathCare) equipped with an RSP6-16D probe. The contrast agent used was Definity (Lantheus Medical Imaging). The subharmonic signal gradients between tumor and adjacent normal tissue were calculated and compared with patient pathological response outcome using t-test and receiver operating characteristic (ROC) curve analysis. Pressure gradients between pCR (15 patients) and non-pCR (23 patients) groups were significantly different at 0 (p = 0.02), 10 (p = 0.03), and 30% (p = 0.0003) completion of NAC. Area under the curve values of 0.69, 0.77, and 0.86 were achieved at 0%, 10%, and 30% of NAC completion, respectively. Overall, SHAPE pressure gradients may aid prediction of NAC pathological response in breast cancer patients.

Abstract Artificial intelligence (AI) in breast imaging is a rapidly developing field with promising results. Despite the large number of recent publications in this field, unanswered questions have led to limited implementation of AI into daily clinical practice for breast radiologists. This paper provides an overview of the key limitations of AI in breast imaging including, but not limited to, limited numbers of FDA-approved algorithms and annotated data sets with histologic ground truth; concerns surrounding data privacy, security, algorithm transparency, and bias; and ethical issues. Ultimately, the successful implementation of AI into clinical care will require thoughtful action to address these challenges, transparency, and sharing of AI implementation workflows, limitations, and performance metrics within the breast imaging community and other end-users.