Table_1_Deep Learning-Based Radiomics of B-Mode Ultrasonography and Shear-Wave Elastography: Improved Performance in Breast Mass Classification.DOCX
Shear-wave elastography (SWE) can improve the diagnostic specificity of the B-model ultrasonography (US) in breast cancer. However, whether deep learning-based radiomics signatures based on the B-mode US (B-US-RS) or SWE (SWE-RS) could further improve the diagnostic performance remains to be investigated. We aimed to develop the B-US-RS and SWE-RS and determine their performances in classifying breast masses.
Materials and MethodsThis retrospective study included 291 women (mean age ± standard deviation, 40.9 ± 12.3 years) from two centers who had US-visible solid breast masses and underwent biopsy and/or surgical resection between June 2015 and July 2017. B-mode US and SWE images of the 198 masses in 198 patients (training cohort) from center 1 were segmented, respectively, to construct B-US-RS and SWE-RS using the least absolute shrinkage and selection operator regression and tested in an independent validation cohort of 65 masses in 65 patients from center 1 and in an external validation cohort of 28 masses in 28 patients from center 2. The performances of B-US-RS and SWE-RS were assessed using receiver operating characteristic (ROC) analysis and compared with that of radiologist assessment [Breast Imaging Reporting and Data System (BI-RADS)] and quantitative SWE parameters [maximum elasticity (Emax), mean elasticity (Emean), elasticity ratio (Eratio), and elastic modulus standard deviation (ESD)] by using the McNemar test.
ResultsThe single best-performing quantitative SWE parameter, Emax, had a higher specificity than BI-RADS assessment in the training and independent validation cohorts (P < 0.001 for both). The areas under the ROC curves (AUCs) of B-US-RS and SWE-RS both were 0.99 (95% CI = 0.99–1.00) in the training cohort, 1.00 (95% CI = 1.00–1.00) in the independent validation cohort, and 1.00 (95% CI = 1.00–1.00) in the external validation cohort. The specificities of B-US-RS and SWE-RS were higher than that of Emax in the training (P < 0.001 for both) and independent validation cohorts (P = 0.02 for both).
ConclusionThe B-US-RS and SWE-RS outperformed the quantitative SWE parameters and BI-RADS assessment for classifying breast masses. The integration of the deep learning-based radiomics approach would help improve the classification ability of B-mode US and SWE for breast masses.
History
References
- https://doi.org//10.1016/S0140-6736(15)00787-4
- https://doi.org//10.1148/radiol.13121606
- https://doi.org//10.7863/jum.2009.28.1.105
- https://doi.org//10.1148/rg.305095144
- https://doi.org//10.1148/radiol.13130561
- https://doi.org//10.1007/s00330-011-2341-x
- https://doi.org//10.1148/radiol.11110640
- https://doi.org//10.1148/radiol.2381041336
- https://doi.org//10.1002/jum.14849
- https://doi.org//10.7863/jum.2012.31.5.773
- https://doi.org//10.1016/j.ultrasmedbio.2014.05.020
- https://doi.org//10.1053/j.sult.2017.05.010
- https://doi.org//10.1148/radiol.14132443
- https://doi.org//10.3389/fonc.2019.00102
- https://doi.org//10.1038/nrclinonc.2017.141
- https://doi.org//10.1148/radiol.2015151169
- https://doi.org//10.1146/annurev-bioeng-071516-044442
- https://doi.org//10.1016/j.media.2017.07.005
- https://doi.org//10.1148/radiol.2018180547
- https://doi.org//10.1148/rg.2017170077
- https://doi.org//10.1038/s41467-020-15027-z
- https://doi.org//10.1148/radiol.14130818
- https://doi.org//10.1186/bcr2787
- https://doi.org//10.1016/s1076-6332(03)00671-8
- https://doi.org//10.1148/radiol.2019190737
- https://doi.org//10.1158/1078-0432.CCR-18-3190
- https://doi.org//10.1109/MSP.2019.2900993
- https://doi.org//10.1002/sim.3148
- https://doi.org//10.2307/2531595
- https://doi.org//10.1001/jama.271.9.703
- https://doi.org//10.1007/s00330-012-2682-0
- https://doi.org//10.1038/s41598-018-31906-4
- https://doi.org//10.1109/TBME.2018.2844188
- https://doi.org//10.1016/j.ultras.2016.08.004
- https://doi.org//10.1007/s00330-012-2686-9
- https://doi.org//10.1007/s00330-012-2736-3