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Intelligent multi-modal shear wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): a retrospective, international, multicentre analysis

Published:October 22, 2022DOI:https://doi.org/10.1016/j.ejca.2022.09.018

      Highlights

      • Breast ultrasound results in false-positive findings causing unnecessary biopsies.
      • We developed and tested an intelligent multi-modal shear wave elastography algorithm.
      • Performance was higher compared to breast ultrasound and traditional elastography.
      • Unnecessary biopsies were significantly reduced by 50.3% with 0% undetected malignancies.
      • Prospective validation of the algorithm in a diverse setting seems warranted.

      Abstract

      Background

      Breast ultrasound identifies additional carcinomas not detected in mammography but has a higher rate of false-positive findings. We evaluated whether use of intelligent multi-modal shear wave elastography (SWE) can reduce the number of unnecessary biopsies without impairing the breast cancer detection rate.

      Methods

      We trained, tested, and validated machine learning algorithms using SWE, clinical, and patient information to classify breast masses. We used data from 857 women who underwent B-mode breast ultrasound, SWE, and subsequent histopathologic evaluation at 12 study sites in seven countries from 2016 to 2019. Algorithms were trained and tested on data from 11 of the 12 sites and externally validated using the additional site's data. We compared findings to the histopathologic evaluation and compared the diagnostic performance between B-mode breast ultrasound, traditional SWE, and intelligent multi-modal SWE.

      Results

      In the external validation set (n = 285), intelligent multi-modal SWE showed a sensitivity of 100% (95% CI, 97.1–100%, 126 of 126), a specificity of 50.3% (95% CI, 42.3–58.3%, 80 of 159), and an area under the curve of 0.93 (95% CI, 0.90–0.96). Diagnostic performance was significantly higher compared to traditional SWE and B-mode breast ultrasound (P < 0.001). Unlike traditional SWE, positive-predictive values of intelligent multi-modal SWE were significantly higher compared to B-mode breast ultrasound. Unnecessary biopsies were reduced by 50.3% (79 versus 159, P < 0.001) without missing cancer compared to B-mode ultrasound.

      Conclusion

      The majority of unnecessary breast biopsies might be safely avoided by using intelligent multi-modal SWE. These results may be helpful to reduce diagnostic burden for patients, providers, and healthcare systems.

      Keywords

      Abbreviations:

      ACR (American College of Radiology), BI-RADS (Breast Imaging Reporting and Data System), CI (confidence interval), AUC (area under the curve), ROC (receiver-operating characteristic), ML (machine learning), LR (logistic regression)
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      References

        • Ohuchi N.
        • Suzuki A.
        • Sobue T.
        • Kawai M.
        • Yamamoto S.
        • Zheng Y.F.
        • et al.
        Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the Japan Strategic Anti-cancer Randomized Trial (J-START): a randomised controlled trial.
        Lancet. 2016; 387: 341-348https://doi.org/10.1016/S0140-6736(15)00774-6
        • Berg W.A.
        • Zhang Z.
        • Lehrer D.
        • Jong R.A.
        • Pisano E.D.
        • Barr R.G.
        • et al.
        Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk.
        JAMA, J Am Med Assoc. 2012; 307: 1394-1404https://doi.org/10.1001/jama.2012.388
        • Buchberger W.
        • Geiger-Gritsch S.
        • Knapp R.
        • Gautsch K.
        • Oberaigner W.
        Combined screening with mammography and ultrasound in a population-based screening program.
        Eur J Radiol. 2018; 101: 24-29https://doi.org/10.1016/j.ejrad.2018.01.022
        • Berg W.A.
        • Cosgrove D.O.
        • Doré C.J.
        • Schäfer F.K.W.
        • Svensson W.E.
        • Hooley R.J.
        • et al.
        Shear-wave elastography improves the specificity of breast US: the BE1 multinational study of 939 masses.
        Radiology. 2012; 262: 435-449https://doi.org/10.1148/radiol.11110640
        • Golatta M.
        • Pfob A.
        • Büsch C.
        • Bruckner T.
        • Alwafai Z.
        • Balleyguier C.
        • et al.
        The potential of shear wave elastography to reduce unnecessary biopsies in breast cancer diagnosis: an international, diagnostic, multicenter trial.
        Ultraschall der Med. 2021; https://doi.org/10.1055/a-1543-6156
        • Golatta M.
        • Pfob A.
        • Büsch C.
        • Bruckner T.
        • Alwafai Z.
        • Balleyguier C.
        • et al.
        The potential of combined shear wave and strain elastography to reduce unnecessary biopsies in breast cancer diagnostics – an international, multicentre trial.
        Eur J Cancer. 2022; 161: 1-9https://doi.org/10.1016/j.ejca.2021.11.005
        • Yu K.H.
        • Beam A.L.
        • Kohane I.S.
        Artificial intelligence in healthcare.
        Nat Biomed Eng. 2018; 2: 719-731https://doi.org/10.1038/s41551-018-0305-z
        • Rajkomar A.
        • Dean J.
        • Kohane I.
        Machine learning in medicine.
        N Engl J Med. 2019; 380: 1347-1358https://doi.org/10.1056/NEJMra1814259
        • Pfob A.
        • Mehrara B.J.
        • Nelson J.A.
        • Wilkins E.G.
        • Pusic A.L.
        • Sidey-Gibbons C.
        Towards patient-centered decision-making in breast cancer surgery.
        Ann Surg. 2021; https://doi.org/10.1097/sla.0000000000004862
        • Pfob A.
        • Sidey-Gibbons C.
        • Lee H.B.
        • Tasoulis M.K.
        • Koelbel V.
        • Golatta M.
        • et al.
        Identification of breast cancer patients with pathologic complete response in the breast after neoadjuvant systemic treatment by an intelligent vacuum-assisted biopsy.
        Eur J Cancer. 2021; 143: 134-146https://doi.org/10.1016/j.ejca.2020.11.006
        • Sidey-Gibbons C.
        • Pfob A.
        • Asaad M.
        • Boukovalas S.
        • Lin Y.-L.
        • Selber J.C.
        • et al.
        Development of machine learning algorithms for the prediction of financial toxicity in localized breast cancer following surgical treatment.
        JCO Clin Cancer Informatics. 2021; 5: 338-347https://doi.org/10.1200/cci.20.00088
        • American College of Radiology
        ACR BI-RADS atlas: breast imaging reporting and data system.
        5th ed. 2013 (Reston, Virginia)
        • Barr R.G.
        Breast elastography: how to perform and integrate into a “best-practice” patient treatment algorithm.
        J Ultrasound Med. 2020; 39: 7-17https://doi.org/10.1002/jum.15137
        • Barr R.G.
        • Nakashima K.
        • Amy D.
        • Cosgrove D.
        • Farrokh A.
        • Schafer F.
        • et al.
        WFUMB guidelines and recommendations for clinical use of ultrasound elastography: Part 2: Breast.
        Ultrasound Med Biol. 2015; 41: 1148-1160https://doi.org/10.1016/j.ultrasmedbio.2015.03.008
        • Liu Y.
        • Chen P.H.C.
        • Krause J.
        • Peng L.
        How to read articles that use machine learning: users' guides to the medical literature.
        JAMA, J Am Med Assoc. 2020; 322: 1806-1816https://doi.org/10.1001/jama.2019.16489
        • Cohen J.F.
        • Korevaar D.A.
        • Altman D.G.
        • Bruns D.E.
        • Gatsonis C.A.
        • Hooft L.
        • et al.
        STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration.
        BMJ Open. 2016; 6: e012799https://doi.org/10.1136/bmjopen-2016-012799
        • Collins G.S.
        • Reitsma J.B.
        • Altman D.G.
        • Moons K.G.M.
        Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.
        Ann Intern Med. 2015; 162: 55-63https://doi.org/10.7326/M14-0697
        • Sidey-Gibbons J.A.M.
        • Sidey-Gibbons C.J.
        Machine learning in medicine: a practical introduction.
        BMC Med Res Methodol. 2019; 19: 1-18https://doi.org/10.1186/s12874-019-0681-4
        • Pfob A.
        • Mehrara B.J.
        • Nelson J.A.
        • Wilkins E.G.
        • Pusic A.L.
        • Sidey-Gibbons C.
        Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001).
        Breast. 2021; 60: 111-122https://doi.org/10.1016/j.breast.2021.09.009
        • Pfob A.
        • Sidey-Gibbons C.
        • Rauch G.
        • Thomas B.
        • Schaefgen B.
        • Kuemmel S.
        • et al.
        Intelligent vacuum-assisted biopsy to identify breast cancer patients with pathologic complete response (ypT0 and ypN0) after neoadjuvant systemic treatment for omission of breast and axillary surgery.
        J Clin Oncol. 2022; 40 (15): 1903https://doi.org/10.1200/JCO.21.02439
        • Tibshirani R.
        The lasso method for variable selection in the Cox model.
        Stat Med. 1997; 16 (2-3): 385-395https://doi.org/10.1002/(sici)1097-0258(19970228)16:4<385::aid-sim380>3.0.co
        • Friedman J.
        • Hastie T.
        • Tibshirani R.
        Regularization paths for generalized linear models via coordinate descent.
        J Stat Software. 2010; 33: 1-22
        • Riedmiller M.
        • Braun H.
        Direct adaptive method for faster backpropagation learning: the RPROP algorithm.
        in: 1993 IEEE int. Conf. Neural networks. Publ by IEEE, 1993: 586-591https://doi.org/10.1109/icnn.1993.298623
        • Lecun Y.
        • Bengio Y.
        • Hinton G.
        Deep learning.
        Nature. 2015; 521: 436-444https://doi.org/10.1038/nature14539
        • Gower J.C.
        A general coefficient of similarity and some of its properties.
        Biometrics. 1971; 27: 857https://doi.org/10.2307/2528823
        • Sakia R.M.
        The box-cox transformation technique: a review.
        Stat. 1992; 41: 169https://doi.org/10.2307/2348250
        • Bergstra J.
        • Bengio Y.
        Random search for hyper-parameter optimization yoshua bengio.
        J Mach Learn Res. 2012; 13: 281-305
        • Kuhn M.
        Classification and regression training - the “caret” package.
        2020 (accessed April 27, 2021)
        • Fisher A.
        • Rudin C.
        • Dominici F.
        All models are wrong.
        in: But many are useful: learning a variable's importance by studying an entire class of prediction models simultaneously. vol. 20. 2019
        • Harrell F.E.
        • Lee K.L.
        • Mark D.B.
        Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.
        Stat Med. 1996; 15 (2-4): 361-387https://doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO
        • Spiegelhalter D.J.
        Probabilistic prediction in patient management and clinical trials.
        Stat Med. 1986; 5: 421-433https://doi.org/10.1002/sim.4780050506
        • Sprague B.L.
        • Stout N.K.
        • Schechter C.
        • Van Ravesteyn N.T.
        • Cevik M.
        • Alagoz O.
        • et al.
        Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts.
        Ann Intern Med. 2015; 162: 157-166https://doi.org/10.7326/M14-0692
        • Lee J.M.
        • Arao R.F.
        • Sprague B.L.
        • Kerlikowske K.
        • Lehman C.D.
        • Smith R.A.
        • et al.
        Performance of screening ultrasonography as an adjunct to screening mammography in women across the spectrum of breast cancer risk.
        JAMA Intern Med. 2019; 179: 658-667https://doi.org/10.1001/jamainternmed.2018.8372
        • Sung H.
        • Ferlay J.
        • Siegel R.L.
        • Laversanne M.
        • Soerjomataram I.
        • Jemal A.
        • et al.
        Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.
        CA Cancer J Clin. 2021; 71: 209-249https://doi.org/10.3322/caac.21660
        • Kimball C.C.
        • Nichols C.I.
        • Vose J.G.
        The payer and patient cost burden of open breast conserving procedures following percutaneous breast biopsy.
        Breast Cancer Basic Clin Res. 2018; 12117822341877776https://doi.org/10.1177/1178223418777766
        • Golatta M.
        • Schweitzer-Martin M.
        • Harcos A.
        • Schott S.
        • Gomez C.
        • Stieber A.
        • et al.
        Evaluation of virtual touch tissue imaging quantification, a new shear wave velocity imaging method, for breast lesion assessment by ultrasound.
        BioMed Res Int. 2014; (2014)https://doi.org/10.1155/2014/960262
        • Pfob A.
        • Sidey-Gibbons C.
        • Barr R.G.
        • Duda Volker
        • Alwafai Z.
        • Balleyguier Corinne
        • et al.
        The importance of multi-modal imaging and clinical information for humans and AI-based algorithms to classify breast masses (INSPiRED 003): an international, multicenter analysis.
        Eur Radiol. 2022; 1: 1-15https://doi.org/10.1007/S00330-021-08519-Z
        • Moon W.K.
        • Huang Y.S.
        • Lee Y.W.
        • Chang S.C.
        • Lo C.M.
        • Yang M.C.
        • et al.
        Computer-aided tumor diagnosis using shear wave breast elastography.
        Ultrasonics. 2017; 78: 125-133https://doi.org/10.1016/j.ultras.2017.03.010
        • Zhang Q.
        • Song S.
        • Xiao Y.
        • Chen S.
        • Shi J.
        • Zheng H.
        Dual-mode artificially-intelligent diagnosis of breast tumours in shear-wave elastography and B-mode ultrasound using deep polynomial networks.
        Med Eng Phys. 2019; 64: 1-6https://doi.org/10.1016/J.MEDENGPHY.2018.12.005
        • Misra S.
        • Jeon S.
        • Managuli R.
        • Lee S.
        • Kim G.
        • Yoon C.
        • et al.
        Bi-modal transfer learning for classifying breast cancers via combined B-mode and ultrasound strain imaging.
        IEEE Trans Ultrason Ferroelectrics Freq Control. 2022; 69: 222-232https://doi.org/10.1109/TUFFC.2021.3119251
        • Zhang X.
        • Liang M.
        • Yang Z.
        • Zheng C.
        • Wu J.
        • Ou B.
        • et al.
        Deep learning-based radiomics of B-mode ultrasonography and shear-wave elastography: improved performance in breast mass classification.
        Front Oncol. 2020; 10https://doi.org/10.3389/FONC.2020.01621
        • Fujioka T.
        • Katsuta L.
        • Kubota K.
        • Mori M.
        • Kikuchi Y.
        • Kato A.
        • et al.
        Classification of breast masses on ultrasound shear wave elastography using convolutional neural networks.
        Ultrason Imag. 2020; 42: 213-220https://doi.org/10.1177/0161734620932609
        • Zhang Q.
        • Xiao Y.
        • Dai W.
        • Suo J.
        • Wang C.
        • Shi J.
        • et al.
        Deep learning based classification of breast tumors with shear-wave elastography.
        Ultrasonics. 2016; 72: 150-157https://doi.org/10.1016/J.ULTRAS.2016.08.004
        • Tang Y.
        • Liang M.
        • Tao L.
        • Deng M.
        • Li T.
        Machine learning–based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study.
        Quant Imag Med Surg. 2022; 12: 1223https://doi.org/10.21037/QIMS-21-341
        • Harrison C.
        • Loe B.S.
        • Lis P.
        • Sidey-Gibbons C.
        Maximizing the potential of patient-reported assessments by using the open-source concerto platform with computerized adaptive testing and machine learning.
        J Med Internet Res. 2020; 22: e20950https://doi.org/10.2196/20950
        • Granja C.
        • Janssen W.
        • Johansen M.A.
        Factors determining the success and failure of ehealth interventions: systematic review of the literature.
        J Med Internet Res. 2018; 20https://doi.org/10.2196/10235
        • Greenhalgh T.
        • Wherton J.
        • Papoutsi C.
        • Lynch J.
        • Hughes G.
        • A'Court C.
        • et al.
        Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies.
        J Med Internet Res. 2017; 19https://doi.org/10.2196/jmir.8775
        • Pfob A.
        • Sidey-Gibbons C.
        • Schuessler M.
        • Lu S.-C.
        • Xu C.
        • Dubsky P.
        • et al.
        Contrast of digital and health literacy between IT and health care specialists highlights the importance of multidisciplinary teams for digital health—a pilot study.
        JCO Clin Cancer Informatics. 2021; 5: 734-745https://doi.org/10.1200/cci.21.00032
        • Pfob A.
        • Barr R.G.
        • Duda V.
        • Büsch C.
        • Bruckner T.
        • Spratte J.
        • et al.
        A new practical decision rule to better differentiate BI-rads 3 or 4 breast masses on breast ultrasound.
        J Ultrasound Med. 2022; 41: 427-436https://doi.org/10.1002/jum.15722