Real‐time breast lesion classification combining diffuse optical tomography frequency domain data and BI‐RADS assessment

Ultrasound (US)‐guided diffuse optical tomography (DOT) has demonstrated potential for breast cancer diagnosis, in which real‐time or near real‐time diagnosis with high accuracy is desired. However, DOT's relatively slow data processing and image reconstruction speeds have hindered real‐time diagnosis. Here, we propose a real‐time classification scheme that combines US breast imaging reporting and data system (BI‐RADS) readings and DOT frequency domain measurements. A convolutional neural network is trained to generate malignancy probability scores from DOT measurements. Subsequently, these scores are integrated with BI‐RADS assessments using a support vector machine classifier, which then provides the final diagnostic output. An area under the receiver operating characteristic curve of 0.978 is achieved in distinguishing between benign and malignant breast lesions in patient data without image reconstruction. Ultrasound breast imaging reporting and data system (BI‐RADS) assessments and diffuse optical tomography (DOT) are combined with machine learning to create a real‐time, highly accurate diagnostic tool. A convolutional neural network is trained to generate malignancy probability scores from DOT measurements. Subsequently, these scores are integrated with BI‐RADS assessments using a support vector machine classifier, which provides the final diagnostic output. Achieving an impressive area under the receiver operating characteristic curve of 0.978, this approach shows a promise to improve breast lesion classification without time‐consuming image reconstruction.