Breast mass detection and classification using PRISM™ explainable Network based Machine Learning (XNML™) platform for Quantitative Transmission (QT) ultrasound tomography

摘要

Overdiagnosis and overtreatment are two major risks involved with mammography-based breast screening which, in addition to its 3D variant, is currently the only approved breast screening technology. Quantitative Transmission (QT) ultrasound is an upcoming breast imaging modality that has the ability to generate quantitative speed-of-sound based maps of the whole breast enabling unprecedented imaging biomarkers. On top of that, machine learning (ML)-based methods for breast tissue/cancer classification have shown promise because of their unique advantages in innovative feature mining from complex datasets. In this paper, we present the results of the deployment of novel data-driven, yet explainable methods implemented in Albeado's PRISM™ AI/ML platform that delivers rapid and accurate breast mass detection when applied to QT imaging. Using PRISM™, we first deployed computer vision methods to segment breast tissue and identify regions of interest (ROI) for the three-dimensional volumetric speed-of-sound maps, which allows for further classification into benign and malignant masses using unsupervised methods. Our strategy is to segment breast images into candidate units, extract radiomic features for each unit, and then distinguish normal tissue from pathological tissue. In order to evaluate our lesion detection framework, we rank the lesions according to their radiomic features and compare the top-ranking candidates to radiologist annotations. For malignant cases, lesions are consistently identified (95% recall). Our results indicate that the presented radiomics-based method is a viable candidate for breast mass detection and classification in QT imaging and serves as a framework for further development.