Optimal breast cancer diagnostic strategy using combined ultrasound and diffuse optical tomography.
Autor: | Uddin KMS; Biomedical Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA., Zhang M; Electrical and System Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA., Anastasio M; Biomedical Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA.; Department of Bioengineering, University of Illinois at Urbana-Champaign, 1406 W Green St, Urbana, IL 61801, USA., Zhu Q; Biomedical Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA. |
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Jazyk: | angličtina |
Zdroj: | Biomedical optics express [Biomed Opt Express] 2020 Apr 24; Vol. 11 (5), pp. 2722-2737. Date of Electronic Publication: 2020 Apr 24 (Print Publication: 2020). |
DOI: | 10.1364/BOE.389275 |
Abstrakt: | Ultrasound (US)-guided near-infrared diffuse optical tomography (DOT) has demonstrated great potential as an adjunct breast cancer diagnosis tool to US imaging alone, especially in reducing unnecessary benign biopsies. However, DOT data processing and image reconstruction speeds remain slow compared to the real-time speed of US. Real-time or near real-time diagnosis with DOT is an important step toward the clinical translation of US-guided DOT. Here, to address this important need, we present a two-stage diagnostic strategy that is both computationally efficient and accurate. In the first stage, benign lesions are identified in near real-time by use of a random forest classifier acting on the DOT measurements and the radiologists' US diagnostic scores. Any lesions that cannot be reliably classified by the random forest classifier will be passed on to the second stage which begins with image reconstruction. Functional information from the reconstructed hemoglobin concentrations is employed by a Support Vector Machine (SVM) classifier for diagnosis at the end of the second stage. This two-step classification approach which combines both perturbation data and functional features, results in improved classification, as denoted by the receiver operating characteristic (ROC) curve. Using this two-step approach, the area under the ROC curve (AUC) is 0.937 ± 0.009, with a sensitivity of 91.4% and specificity of 85.7%. In comparison, using functional features and US score yields an AUC of 0.892 ± 0.027, with a sensitivity of 90.2% and specificity of 74.5%. Most notably, the specificity is increased by more than 10% due to the implementation of the random forest classifier. Competing Interests: The authors declare that there are no conflicts of interest related to this article. (© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.) |
Databáze: | MEDLINE |
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