Artikel ; Online: Real-time breast lesion classification combining diffuse optical tomography frequency domain data and BI-RADS assessment.
2024 Band 17, Heft 5, Seite(n) e202300483
Abstract: 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 ... ...
Abstract | 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. |
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Mesh-Begriff(e) | Humans ; Tomography, Optical/methods ; Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/pathology ; Female ; Image Processing, Computer-Assisted/methods ; Time Factors ; Neural Networks, Computer |
Sprache | Englisch |
Erscheinungsdatum | 2024-03-02 |
Erscheinungsland | Germany |
Dokumenttyp | Journal Article ; Research Support, Non-U.S. Gov't |
ZDB-ID | 2390063-5 |
ISSN | 1864-0648 ; 1864-063X |
ISSN (online) | 1864-0648 |
ISSN | 1864-063X |
DOI | 10.1002/jbio.202300483 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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