ArticleJournal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine2026
Wavelet Spectrum in a Multi-Channel Network May Reduce Biopsy Rates on Diagnosis of Breast Tumors With BI-RADS Category 4a or Higher.
Article in Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesBI-RADS 4a+ breast tumors have a high rate of unnecessary biopsies due to ambiguous diagnostic features. This study aims to develop and evaluate a computer-aided diagnosis method integrating wavelet spectrum analysis with deep learning for benign-malignant classification of these tumors, so as to reduce unnecessary invasive biopsies.
methodsA retrospective study was conducted on 390 patients with breast lesions categorized as BI-RADS 4a or higher. We proposed a computerized diagnostic method that combines wavelet spectrum analysis with deep learning. A 3-channel ultrasound image was constructed by integrating B-mode, high-frequency (13 MHz), and low-frequency (7 MHz) wavelet components. This multi-channel input was processed by a dual-channel deep learning network for tumor classification. A strict patient-specific biopsy-sparing criterion was also established and evaluated.
resultsThe integration of wavelet spectral features improved tumor classification performance by an average of 2% compared to using B-mode images alone. The proposed model achieved an overall accuracy of 84.52%. Under a patient-specific evaluation framework more aligned with clinical practice, the model demonstrated a biopsy-sparing rate of 26.01% while maintaining a low missed diagnosis rate of 1.786% at the most stringent threshold. The biopsy-sparing to missed diagnosis rate ratio remained high across thresholds, confirming the robustness of the patient-based assessment.
conclusionThe integration of wavelet spectrum analysis into a multi-channel deep learning network effectively improves the classification of BI-RADS 4a or higher breast tumors. This method demonstrates significant potential as a non-invasive adjunct tool to enhance diagnostic precision and reduce unnecessary biopsies in clinical practice.
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