Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.
Breast MRI is not a cost-effective modality for screening women at intermediate risk, including those with dense breast tissue as the only risk. Abbreviated breast MRI protocols have been proposed as a way of achieving efficiency and rapid throughput. Use of these abbreviated protocols may increase availability and provide women with greater access to breast MRI.
Studies have aimed to assess prognostic factors to characterize its risk of invasive potential; however, there still remains a lack of uniformity in workup and treatment. We summarize current knowledge of DCIS and the ongoing controversies.
Transgender is the umbrella term for individuals whose gender identity and/or gender expression differs from their assigned sex at birth. With the rise in patients undergoing gender-affirming hormone therapy and gender-affirming surgery, it is increasingly important for radiologists to be aware of breast imaging considerations for this population. While diagnostic imaging protocols for transgender individuals are generally similar to those for cisgender women, screening guidelines are more variable. Currently, several professional and institutional guidelines have been created to address breast cancer screening in the transgender population, specifically screening mammography in transfeminine individuals who undergo hormone therapy. This article defines appropriate terminology with respect to the transgender population, reviews evidence for breast cancer risk and screening in transgender individuals, considers diagnostic breast imaging approaches, and discusses special considerations and challenges with regard to health care access and public education for these individuals.
Ultrasound is an important imaging modality for the detection and characterization of breast cancer. Though consistently shown to detect mammographically occult cancers, especially in women with dense breasts, breast ultrasound has been noted to have high false-positive rates. In this work, we present an artificial intelligence (AI) system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. To develop and validate this system, we curated a dataset consisting of 288,767 ultrasound exams from 143,203 patients examined at NYU Langone Health, between 2012 and 2019. On a test set consisting of 44,755 exams, the AI system achieved an area under the receiver operating characteristic curve (AUROC) of 0.976. In a reader study, the AI system achieved a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924±0.02 radiologists). With the help of the AI, radiologists decreased their false positive rates by 37.4% and reduced the number of requested biopsies by 27.8%, while maintaining the same level of sensitivity. To confirm its generalizability, we evaluated our system on an independent external test dataset where it achieved an AUROC of 0.911. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis worldwide.
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