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Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?

      Rationale and Objectives

      This study aimed to determine whether mammographic features assessed by radiologists and using computer algorithms are prognostic of occult invasive disease for patients showing ductal carcinoma in situ (DCIS) only in core biopsy.

      Materials and Methods

      In this retrospective study, we analyzed data from 99 subjects with DCIS (74 pure DCIS, 25 DCIS with occult invasion). We developed a computer-vision algorithm capable of extracting 113 features from magnification views in mammograms and combining these features to predict whether a DCIS case will be upstaged to invasive cancer at the time of definitive surgery. In comparison, we also built predictive models based on physician-interpreted features, which included histologic features extracted from biopsy reports and Breast Imaging Reporting and Data System-related mammographic features assessed by two radiologists. The generalization performance was assessed using leave-one-out cross validation with the receiver operating characteristic curve analysis.

      Results

      Using the computer-extracted mammographic features, the multivariate classifier was able to distinguish DCIS with occult invasion from pure DCIS, with an area under the curve for receiver operating characteristic equal to 0.70 (95% confidence interval: 0.59–0.81). The physician-interpreted features including histologic features and Breast Imaging Reporting and Data System-related mammographic features assessed by two radiologists showed mixed results, and only one radiologist's subjective assessment was predictive, with an area under the curve for receiver operating characteristic equal to 0.68 (95% confidence interval: 0.57–0.81).

      Conclusions

      Predicting upstaging for DCIS based upon mammograms is challenging, and there exists significant interobserver variability among radiologists. However, the proposed computer-extracted mammographic features are promising for the prediction of occult invasion in DCIS.

      Key Words

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      References

        • Sue G.R.
        • Lannin D.R.
        • Killelea B.
        • et al.
        Predictors of microinvasion and its prognostic role in ductal carcinoma in situ.
        Am J Surg. 2013; 206: 478-481
        • American Cancer Society
        Cancer facts & figures 2015.
        American Atlanta, 2015
        • Ozanne E.M.
        • Shieh Y.
        • Barnes J.
        • et al.
        Characterizing the impact of 25 years of DCIS treatment.
        Breast Cancer Res Treat. 2011; 129: 165-173
        • Erbas B.
        • Provenzano E.
        • Armes J.
        • et al.
        The natural history of ductal carcinoma in situ of the breast: a review.
        Breast Cancer Res Treat. 2006; 97: 135-144
        • Brennan M.E.
        • Turner R.M.
        • Ciatto S.
        • et al.
        Ductal carcinoma in situ at core-needle biopsy: meta-analysis of underestimation and predictors of invasive breast cancer.
        Radiology. 2011; 260: 119-128
        • Dillon M.F.
        • McDermott E.W.
        • Quinn C.M.
        • et al.
        Predictors of invasive disease in breast cancer when core biopsy demonstrates DCIS only.
        J Surg Oncol. 2006; 93: 559-563
        • Kurniawan E.D.
        • Rose A.
        • Mou A.
        • et al.
        Risk factors for invasive breast cancer when core needle biopsy shows ductal carcinoma in situ.
        Arch Surg. 2010; 145: 1098-1104
        • Lee C.-W.
        • Wu H.-K.
        • Lai H.-W.
        • et al.
        Preoperative clinicopathologic factors and breast magnetic resonance imaging features can predict ductal carcinoma in situ with invasive components.
        Eur J Radiol. 2016; 85: 780-789
        • Park H.S.
        • Kim H.Y.
        • Park S.
        • et al.
        A nomogram for predicting underestimation of invasiveness in ductal carcinoma in situ diagnosed by preoperative needle biopsy.
        Breast. 2013; 22: 869-873
        • Park H.S.
        • Park S.
        • Cho J.
        • et al.
        Risk predictors of underestimation and the need for sentinel node biopsy in patients diagnosed with ductal carcinoma in situ by preoperative needle biopsy.
        J Surg Oncol. 2013; 107: 388-392
        • Renshaw A.A.
        Predicting invasion in the excision specimen from breast core needle biopsy specimens with only ductal carcinoma in situ.
        Arch Pathol Lab Med. 2002; 126: 39-41
        • Kopans D.B.
        Breast imaging.
        Lippincott Williams & Wilkins, 2007
        • Dershaw D.
        • Abramson A.
        • Kinne D.
        Ductal carcinoma in situ: mammographic findings and clinical implications.
        Radiology. 1989; 170: 411-415
        • Bria A.
        • Karssemeijer N.
        • Tortorella F.
        Learning from unbalanced data: a cascade-based approach for detecting clustered microcalcifications.
        Med Image Anal. 2014; 18: 241-252
        • El-Naqa I.
        • Yang Y.
        • Wernick M.N.
        • et al.
        A support vector machine approach for detection of microcalcifications.
        IEEE Trans Med Imaging. 2002; 21: 1552-1563
        • Gavrielides M.A.
        • Lo J.Y.
        • Floyd C.E.
        Parameter optimization of a computer-aided diagnosis scheme for the segmentation of microcalcification clusters in mammograms.
        Med Phys. 2002; 29: 475-483
        • Gavrielides M.A.
        • Lo J.Y.
        • Vargas-Voracek R.
        • et al.
        Segmentation of suspicious clustered microcalcifications in mammograms.
        Med Phys. 2000; 27: 13-22
        • Jing H.
        • Yang Y.
        • Nishikawa R.M.
        Detection of clustered microcalcifications using spatial point process modeling.
        Phys Med Biol. 2010; 56: 1
        • Wei L.
        • Yang Y.
        • Nishikawa R.M.
        • et al.
        Relevance vector machine for automatic detection of clustered microcalcifications.
        IEEE Trans Med Imaging. 2005; 24: 1278-1285
        • Zhang E.
        • Wang F.
        • Li Y.
        • et al.
        Automatic detection of microcalcifications using mathematical morphology and a support vector machine.
        Biomed Mater Eng. 2014; 24: 53-59
        • Pai V.R.
        • Gregory N.E.
        • Swinford A.E.
        • et al.
        Ductal carcinoma in situ: computer-aided detection in screening mammography 1.
        Radiology. 2006; 241: 689-694
        • Plant C.
        • Ngo D.
        • Retter F.
        • et al.
        Computer-aided diagnosis of small lesions and non-masses in breast MRI.
        2012
        • Srikantha A.
        Symmetry-based detection and diagnosis of DCIS in breast MRI.
        in: Weickert J. Hein M. Schiele B. Pattern recognition: 35th German Conference, GCPR 2013, Saarbrücken, Germany, September 3–6, 2013. Proceedings. Springer Berlin Heidelberg, Berlin, Heidelberg2013: 255-260
        • Wang L.
        • Harz M.
        • Boehler T.
        • et al.
        A robust and extendable framework towards fully automated diagnosis of nonmass lesions in breast DCE-MRI in 2014.
        IEEE 11th Int Symp Biomed Imaging. 2014;
        • Bagnall M.J.
        • Evans A.J.
        • Wilson A.R.M.
        • et al.
        Predicting invasion in mammographically detected microcalcification.
        Clin Radiol. 2001; 56: 828-832
        • Dinkel H.
        • Gassel A.
        • Tschammler A.
        Is the appearance of microcalcifications on mammography useful in predicting histological grade of malignancy in ductal cancer in situ?.
        Br J Radiol. 2000; 73: 938-944
        • Lee C.H.
        • Carter D.
        • Philpotts L.E.
        • et al.
        Ductal carcinoma in situ diagnosed with stereotactic core needle biopsy: can invasion be predicted? 1.
        Radiology. 2000; 217: 466-470
        • O'Flynn E.
        • Morel J.
        • Gonzalez J.
        • et al.
        Prediction of the presence of invasive disease from the measurement of extent of malignant microcalcification on mammography and ductal carcinoma in situ grade at core biopsy.
        Clin Radiol. 2009; 64: 178-183
        • Sim Y.
        • Litherland J.
        • Lindsay E.
        • et al.
        Upgrade of ductal carcinoma in situ on core biopsies to invasive disease at final surgery: a retrospective review across the Scottish Breast Screening Programme.
        Clin Radiol. 2015; 70: 502-506
        • Baker J.A.
        • Kornguth P.J.
        • Lo J.Y.
        • et al.
        Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon.
        Radiology. 1995; 196: 817-822
        • Pudil P.
        • Ferri F.
        • Novovicova J.
        • et al.
        Floating search methods for feature selection with nonmonotonic criterion functions.
        (In Proceedings of the Twelfth International Conference on Pattern Recognition, IAPR. Citeseer)1994
        • DeLong E.R.
        • DeLong D.M.
        • Clarke-Pearson D.L.
        Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
        Biometrics. 1988; : 837-845
        • Robin X.
        • Turck N.
        • Hainard A.
        • et al.
        pROC: an open-source package for R and S+ to analyze and compare ROC curves.
        BMC Bioinformatics. 2011; 12: 1
        • Mina L.M.
        • Isa N.A.M.
        A review of computer-aided detection and diagnosis of breast cancer in digital mammography.
        J Med Sci. 2015; 15: 110
        • Giger M.L.
        • Karssemeijer N.
        • Schnabel J.A.
        Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer.
        Annu Rev Biomed Eng. 2013; 15: 327-357
        • Jalalian A.
        • Mashohor S.B.
        • Mahmud H.R.
        • et al.
        Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review.
        Clin Imaging. 2013; 37: 420-426
        • Dromain C.
        • Boyer B.
        • Ferre R.
        • et al.
        Computer-aided diagnosis (CAD) in the detection of breast cancer.
        Eur J Radiol. 2013; 82: 417-423
        • Petrick N.
        • Sahiner B.
        • Armato S.G.
        • et al.
        Evaluation of computer-aided detection and diagnosis systems.
        Med Phys. 2013; 40: 087001
        • Cowell C.F.
        • Weigelt B.
        • Sakr R.A.
        • et al.
        Progression from ductal carcinoma in situ to invasive breast cancer: revisited.
        Mol Oncol. 2013; 7: 859-869
        • Davis J.
        • Goadrich M.
        The relationship between Precision-Recall and ROC curves.
        (In Proceedings of the 23rd International Conference on Machine Learning) ACM, 2006