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BACKGROUND Neoadjuvant chemotherapy reduces tumor size prior to surgery in women with breast cancer. We assessed the ability of mammography and ultrasound to predict residual tumor size following neoadjuvant chemotherapy. METHODS In a retrospective review of consecutive breast cancer patients treated with neoadjuvant chemotherapy, residual tumor size estimated by diagnostic imaging was compared with residual tumor size determined by surgical pathology. RESULTS 192 patients with 196 primary breast cancers were studied. Of 104 tumors evaluated by both imaging modalities, ultrasound was able to size 91.3%, however, mammography was able to size only 51.9% of tumors (chi-square, p<.001). Ultrasound also was more accurate than mammography in estimating residual tumor size (62/104 [59.6%] versus 33/104 [31.7%], p < .001). There was little difference in the ability of mammography and ultrasound to predict pathologic complete response (ROC= 0.741 versus 0.784). CONCLUSIONS Breast ultrasound was more accurate than mammography in predicting residual tumor size following neoadjuvant chemotherapy. Likelihood of a complete pathological response was 80% when both imaging modalities demonstrated no residual disease.

Background Healthcare is expected to increasingly integrate technologies enabled by artificial intelligence (AI) into patient care. Understanding perceptions of these tools is essential to successful development and adoption. This exploratory study gauged participants’ level of openness, concern, and perceived benefit associated with AI-driven healthcare technologies. We also explored socio-demographic, health-related, and psychosocial correlates of these perceptions. Methods We developed a measure depicting six AI-driven technologies that either diagnose, predict, or suggest treatment. We administered the measure via an online survey to adults (N = 936) in the United States using MTurk, a crowdsourcing platform. Participants indicated their level of openness to using the AI technology in the healthcare scenario. Items reflecting potential concerns and benefits associated with each technology accompanied the scenarios. Participants rated the extent that the statements of concerns and benefits influenced their perception of favorability toward the technology. Participants completed measures of socio-demographics, health variables, and psychosocial variables such as trust in the healthcare system and trust in technology. Exploratory and confirmatory factor analyses of the concern and benefit items identified two factors representing overall level of concern and perceived benefit. Descriptive analyses examined levels of openness, concern, and perceived benefit. Correlational analyses explored associations of socio-demographic, health, and psychosocial variables with openness, concern, and benefit scores while multivariable regression models examined these relationships concurrently. Results Participants were moderately open to AI-driven healthcare technologies (M = 3.1/5.0 ± 0.9), but there was variation depending on the type of application, and the statements of concerns and benefits swayed views. Trust in the healthcare system and trust in technology were the strongest, most consistent correlates of openness, concern, and perceived benefit. Most other socio-demographic, health-related, and psychosocial variables were less strongly, or not, associated, but multivariable models indicated some personality characteristics (e.g., conscientiousness and agreeableness) and socio-demographics (e.g., full-time employment, age, sex, and race) were modestly related to perceptions. Conclusions Participants’ openness appears tenuous, suggesting early promotion strategies and experiences with novel AI technologies may strongly influence views, especially if implementation of AI technologies increases or undermines trust. The exploratory nature of these findings warrants additional research.

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