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A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models

  • Ashirbani Saha
  • Michael R. Harowicz
  • Weiyao Wang
  • Maciej A. Mazurowski
Original Article – Cancer Research

Abstract

Purpose

To determine whether multivariate machine learning models of algorithmically assessed magnetic resonance imaging (MRI) features from breast cancer patients are associated with Oncotype DX (ODX) test recurrence scores.

Methods

A set of 261 female patients with invasive breast cancer, pre-operative dynamic contrast enhanced magnetic resonance (DCE-MR) images and available ODX score at our institution was identified. A computer algorithm extracted a comprehensive set of 529 features from the DCE-MR images of these patients. The set of patients was divided into a training set and a test set. Using the training set we developed two machine learning-based models to discriminate (1) high ODX scores from intermediate and low ODX scores, and (2) high and intermediate ODX scores from low ODX scores. The performance of these models was evaluated on the independent test set.

Results

High against low and intermediate ODX scores were predicted by the multivariate model with AUC 0.77 (95% CI 0.56–0.98, p < 0.003). Low against intermediate and high ODX score was predicted with AUC 0.51 (95% CI 0.41–0.61, p = 0.75).

Conclusion

A moderate association between imaging and ODX score was identified. The evaluated models currently do not warrant replacement of ODX with imaging alone.

Keywords

Breast cancer MRI Oncotype DX Imaging features Radiomics Feature selection Logistic regression 

Abbreviations

Oncotype DX

ODX

MRI

Magnetic resonance imaging

DCE-MR

Dynamic contrast enhanced magnetic resonance

IHC

Immunohistochemical

NFS

Non-fat-saturated

FGT

Fibroglandular tissue

ROC

Receiver operating characteristics

AUC

Area under receiver operating characteristics

Notes

Funding

This study received funding from North Carolina Biotechnology Center (2016-BIG-6520) and National Institutes of Health (R01EB021360).

Compliance with ethical standards

Conflict of interest

Authors have no conflicts of interest to declare.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

The requirement for informed consent was waived by the institutional review board.

References

  1. (2006) NSABP study confirms oncotype DX predicts chemotherapy benefit in breast cancer patients. Oncology 20:789–790Google Scholar
  2. Arasu VA, Chen RCY, Newitt DN, Chang CB, Tso H, Hylton NM, Joe BN (2011) Can signal enhancement ratio (SER) reduce the number of recommended biopsies without affecting cancer yield in occult MRI-detected lesions? Acad Radiol 18:716–721CrossRefPubMedPubMedCentralGoogle Scholar
  3. Ashraf AB, Daye D, Gavenonis S, Mies C, Feldman M, Rosen M, Kontos D (2014) Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. Radiology 272:374–384CrossRefPubMedPubMedCentralGoogle Scholar
  4. Blaschke E, Abe H (2015) MRI phenotype of breast cancer: kinetic assessment for molecular subtypes. J Magn Reson Imaging 42:920–924CrossRefPubMedGoogle Scholar
  5. Coates AS, Winer EP, Goldhirsch A, Gelber RD, Gnant M, Piccart-Gebhart M, Thürlimann B, Senn HJ, Panel M, André F (2015) Tailoring therapies—improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015. Ann Oncol 26:1533–1546CrossRefPubMedPubMedCentralGoogle Scholar
  6. Delong ER, Delong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837–845Google Scholar
  7. Dowsett M, Sestak I, Lopez-Knowles E, Sidhu K, Dunbier AK, Cowens JW, Ferree S, Storhoff J, Schaper C, Cuzick J (2013) Comparison of PAM50 risk of recurrence score with onco type DX and IHC4 for predicting risk of distant recurrence after endocrine therapy. J Clin Oncol 31:2783–2790CrossRefPubMedGoogle Scholar
  8. Fan M, Li H, Wang S, Zheng B, Zhang J, Li L 2017. Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer. PLOS One 12:e0171683CrossRefPubMedPubMedCentralGoogle Scholar
  9. Gage MM, Rosman M, Mylander WC, Giblin E, Kim H-S, Cope L, Umbricht C, Wolff AC, Tafra L (2015) A validated model for identifying patients unlikely to benefit from the 21-gene recurrence score assay. Clin Breast Cancer 15:467–472CrossRefPubMedPubMedCentralGoogle Scholar
  10. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefPubMedGoogle Scholar
  11. Harowicz MR, Robinson TJ, Dinan MA, Saha A, Marks JR, Marcom PK, Mazurowski MA (2017) Algorithms for prediction of the Oncotype DX recurrence score using clinicopathologic data: a review and comparison using an independent dataset. Breast Cancer Res Treat 162:1–10CrossRefPubMedPubMedCentralGoogle Scholar
  12. Kim JJ, Kim JY, Kang HJ, Shin JK, Kang T, Lee SW, Bae YT (2017) Computer-aided diagnosis-generated kinetic features of breast cancer at preoperative MR imaging: association with disease-free survival of patients with primary operable invasive breast cancer. Radiology 284:45–54CrossRefPubMedGoogle Scholar
  13. Klein ME, Dabbs DJ, Shuai Y, Brufsky AM, Jankowitz R, Puhalla SL, Bhargava R (2013) Prediction of the Oncotype DX recurrence score: use of pathology-generated equations derived by linear regression analysis. Mod Pathol 26:658CrossRefPubMedPubMedCentralGoogle Scholar
  14. Leijenaar RT, Carvalho S, Velazquez ER, Van Elmpt WJC, Parmar C, Hoekstra OS, Hoekstra CJ, Boellaard R, Dekker ALAJ, Gillies RJ, Aerts HJWL, Lambin P (2013) Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol 52(7):1391–1397CrossRefPubMedGoogle Scholar
  15. Li H, Zhu Y, Burnside ES, Drukker K, Hoadley KA, Fan C, Conzen SD, Whitman GJ, Sutton EJ, Net JM, Ganott M, Huang E, Morris EA, Perou CM, Ji Y, Giger ML (2016a) MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of mammaprint, oncotype DX, and PAM50 gene assays. Radiology 281:382–391CrossRefPubMedPubMedCentralGoogle Scholar
  16. Li H, Zhu Y, Burnside ES, Huang E, Drukker K, Hoadley KA, Fan C, Conzen SD, Zuley M, Net JM, Sutton E, Whitman GJ, Morris E, Perou CM, Ji Y, Giger ML (2016b) Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer 2:16012CrossRefPubMedPubMedCentralGoogle Scholar
  17. Mazurowski MA (2015) Radiogenomics: what it is and why it is important. J Am Coll Radiol 12:862–866CrossRefPubMedGoogle Scholar
  18. Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI (2014) Radiogenomic analysis of breast cancer: Luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology 273:365–372CrossRefPubMedGoogle Scholar
  19. Mazurowski MA, Grimm LJ, Zhang J, Macrom PK, Yoon S, Kim C, Ghate S, Johnson K (2015) Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms. Eur J Radiol 84(11):2117–2122Google Scholar
  20. Nielsen TO, Parker JS, Leung S, Voduc D, Ebbert M, Vickery T, Davies SR, Snider J, Stijleman IJ, Reed J (2010) A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor–positive breast cancer. Clin Cancer Res:1078–0432Google Scholar
  21. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, Wolmark N (2004) A Multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351(27):2817–2826CrossRefPubMedGoogle Scholar
  22. Paik S, Tang G, Shak S, Kim C, Baker J, Kim W, Cronin M, Baehner FL, Watson D, Bryant J (2006) Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor–positive breast cancer. J Clin Oncol 24:3726–3734CrossRefPubMedGoogle Scholar
  23. Saha A, Grimm LJ, Harowicz M, Ghate SV, Kim C, Walsh R, Mazurowski MA (2016) Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics. Med Phys 43:4558–4564CrossRefPubMedGoogle Scholar
  24. Saha A, Yu X, Sahoo D, Mazurowski MA (2017) Effects of MRI scanner parameters on breast cancer radiomics. Expert Syst Appl 87:384–391CrossRefGoogle Scholar
  25. Sutton EJ, Oh JH, Dashevsky BZ, Veeraraghavan H, Apte AP, Thakur SB, Deasy JO, Morris EA (2015) Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay. J Magn Reson Imaging 42:1398–1406CrossRefPubMedPubMedCentralGoogle Scholar
  26. Tang P, Wang J, Hicks DG, Wang X, Schiffhauer L, Mcmahon L, Yang Q, Shayne M, Huston A, Skinner KA (2010) A lower Allred score for progesterone receptor is strongly associated with a higher recurrence score of 21-gene assay in breast cancer. Cancer Investig 28:978–982CrossRefGoogle Scholar
  27. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320CrossRefPubMedPubMedCentralGoogle Scholar
  28. Uematsu T, Kasami M, Yuen S (2009) Triple-negative breast cancer: correlation between MR imaging and pathologic findings. Radiology 250:638–647CrossRefPubMedGoogle Scholar
  29. Wan T, Bloch BN, Plecha D, Thompson CL, Gilmore H, Jaffe C, Harris L, Madabhushi A (2016) A radio-genomics approach for identifying high risk estrogen receptor-positive breast cancers on DCE-MRI: preliminary results in predicting OncotypeDX risk scores. Sci Rep 6:21394.  https://doi.org/10.1038/srep21394
  30. Wittner BS, Sgroi DC, Ryan PD, Bruinsma TJ, Glas AM, Male A, Dahiya S, Habin K, Bernards R, Haber DA (2008) Analysis of the MammaPrint breast cancer assay in a predominantly postmenopausal cohort. Clin Cancer Res 14:2988–2993CrossRefPubMedPubMedCentralGoogle Scholar
  31. Wu J, Sun X, Wang J, Cui Y, Kato F, Shirato H, Ikeda DM, Li R (2017) Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: model discovery and external validation. J Magn Reson Imaging 46(4):1017–1027Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of RadiologyDuke University School of MedicineDurhamUSA
  2. 2.Department of MathematicsDuke UniversityDurhamUSA
  3. 3.Department of Electrical and Computer EngineeringDuke UniversityDurhamUSA
  4. 4.Duke University Medical Physics Graduate ProgramDurhamUSA

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