Application of Machine Learning Based on Ultrasonic RF Time Series and 2D Image Features in the Diagnosis of BI-RADS 4A Lesions of Breast.

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Abstract

This study aimed to develop and validate machine-learning (ML) models that integrate ultrasonic radiofrequency (RF) time-series signals with gray-scale image features for the preoperative differentiation of breast lesions classified as category 4A of the Breast Imaging Reporting and Data System. A dataset comprising RF signals, 2D ultrasound features, and pathological diagnoses from 130 BI-RADS 4A lesions (128 patients) was analyzed. Five ML models (logistic regression [LR], support vector machine [SVM], k-nearest neighbor [k-NN], and gradient boosting [GB]) were evaluated. Among 31 features (28 RF-derived and 5 2D image features), 6 key features were selected through feature selection. The LR model achieved the highest area under the curve (0.81, 95% confidence interval: 0.66-1.00), though no statistically significant differences were observed among models (DeLong test, p > .05). Artificial intelligence-assisted diagnosis improved accuracy across physician seniority levels (p < .05): junior (≤3 years: 52.28% versus baseline 27.28%), intermediate (4-10 years: 79.54% versus 45.46%), and senior (≥10 years: 81.91% versus 63.63%). The integration of RF time series and 2D features via LR demonstrates potential to reduce unnecessary biopsies by enhancing diagnostic precision, particularly for less experienced clinicians.

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