Skip to main content

A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study

Abstract

Objective

Quantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and scanners and suffers from bias field inhomogeneity. Radiomic studies are impacted by these effects that affect radiomic feature values. This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies.

Materials and methods

T1, T2, and T1-DCE MR images of two breast phantoms were acquired using two scanners and three dual breast coils. Images were retrospectively corrected for bias field inhomogeneity and further normalised using Z score or histogram matching. Extracted radiomic features were harmonised between coils by the ComBat method. The whole pipeline was assessed qualitatively and quantitatively using statistical comparisons on two series of radiomic feature values computed in the gel mimicking the normal breast tissue or in dense lesions.

Results

Intra and inter-acquisition variabilities were strongly reduced by the standardisation pipeline. Harmonisation by ComBat lowered the percentage of radiomic features significantly different between the three coils from 87% after bias field correction and MR normalisation to 3% in the gel, while preserving or improving performance of lesion classification in the phantoms.

Discussion

A dedicated standardisation pipeline was developed to reduce variabilities in breast MRI, which paves the way for robust multi-scanner radiomic studies but needs to be assessed on patient data.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. 1.

    Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images are more than pictures, they are data. Radiology 278:563–577

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    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 (2016) 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 281:382–391

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Bickelhaupt S, Paech D, Kickingereder P, Steudle F, Lederer W, Daniel H, Götz M, Gählert N, Tichy D, Wiesenfarth M, Laun FB, Maier-Hein KH, Schlemmer H-P, Bonekamp D (2017) Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography. J Magn Reson Imaging 46:604–616

    PubMed  Google Scholar 

  4. 4.

    Park H, Lim Y, Ko ES, Cho HH, Lee JE, Han BK, Ko EY, Choi JS, Park KW (2018) Radiomics signature on magnetic resonance imaging: Association with disease-free survival in patients with invasive breast cancer. Clin Cancer Res 24:4705–4714

    PubMed  Google Scholar 

  5. 5.

    Fan M, Wu G, Cheng H, Zhang J, Shao G, Li L (2017) Radiomic analysis of DCE-MRI for prediction of response to neoadjuvant chemotherapy in breast cancer patients. Eur J Radiol 94:140–147

    CAS  PubMed  Google Scholar 

  6. 6.

    Braman NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, Pletcha D, Madabhushi A (2017) Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 19:57

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Thibault G, Tudorica A, Afzal A, Chui SYC, Naik A, Troxell ML, Kemmer KA, Oh KY, Roy N, Jafarian N, Holtorf ML, Huang W, Song X (2017) DCE-MRI texture features for early prediction of breast cancer therapy response. Tomogr 3:23–32

    Google Scholar 

  8. 8.

    Liu Z, Li Z, Qu J, Zhang R, Zhou X, Li L, Sun K, Tang Z, Jiang H, Li H, Xiong Q, Ding Y, Zhao X, Wang K, Liu Z, Tian J (2019) Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Clin Cancer Res 25:3538–3547

    CAS  PubMed  Google Scholar 

  9. 9.

    Granzier RWY, van Nijnatten TJA, Woodruff HC, Smidt ML, Lobbes MBI (2019) Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: a systematic review. Eur J Radiol 121:108736

    CAS  PubMed  Google Scholar 

  10. 10.

    Eun NL, Kang D, Son EJ, Park JS, Youk JH, Kim J-A, Gweon HM (2020) Texture analysis with 3.0-T MRI for association of response to neoadjuvant chemotherapy in breast cancer. Radiology 294:31–41

    PubMed  Google Scholar 

  11. 11.

    Traverso A, Wee L, Dekker A, Gillies R (2018) Repeatability and reproducibility of radiomic features: a systematic review. Int J Radiat Oncol Biol Phys 102:1143–1158

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Waugh SA, Lerski RA, Bidaut L, Thompson AM (2011) The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms. Med Phys 38:5058–5066

    PubMed  Google Scholar 

  13. 13.

    Saha A, Yu X, Sahoo D, Mazurowski MA (2017) Effects of MRI scanner parameters on breast cancer radiomics. Expert Syst Appl 87:384–391

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Buch K, Kuno H, Qureshi MM, Li B, Sakai O (2018) Quantitative variations in texture analysis features dependent on MRI scanning parameters: a phantom model. J Appl Clin Med Phys 19:253–264

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Rai R, Holloway LC, Brink C, Field M, Christiansen RL, Sun Y, Barton MB, Liney GP (2020) Multicentre evaluation of MRI-based radiomics features: a phantom study. Med Phys. https://doi.org/10.1002/mp.14173

    Article  PubMed  Google Scholar 

  16. 16.

    Chirra P (2018) Empirical evaluation of cross-site reproducibility in radiomic features for characterizing tumor appearance on prostate MRI. In: Progress in Biomedical Optics and Imaging—Proceedings of SPIE, SPIE-Intl Soc Optical Eng, p 10

  17. 17.

    Ford J, Dogan N, Young L, Yang F (2018) Quantitative radiomics: impact of pulse sequence parameter selection on MRI-based textural features of the brain. Contrast Media Mol Imaging 2018:1729071

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Song S, Zheng Y, He Y (2017) A review of methods for bias correction in medical images. Biomed Eng Rev 1:1

    Google Scholar 

  19. 19.

    Frackiewicz M, Borys D, Gorczewski K, Serafin W, Palus H, Kijonka M (2018) The evaluation of correction algorithms of intensity nonuniformity in breast MRI images: a phantom study. In: Proceedings of the Tenth Int. Conf. Mach. Vis. (ICMV 2017), SPIE-Intl Soc Optical Eng, Vienna, p 15

  20. 20.

    Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: Improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Lin M, Chan S, Chen J-H, Chang D, Nie K, Chen S-T, Lin C-J, Shih T-C, Nalcioglu O, Su M-Y (2010) A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI. Med Phys 38:5–14

    CAS  PubMed Central  Google Scholar 

  22. 22.

    Shinohara RT, Sweeney EM, Goldsmith J, Shiee N, Mateen FJ, Calabresi PA, Jarso S, Pham DL, Reich DS, Crainiceanu CM (2014) Statistical normalization techniques for magnetic resonance imaging. NeuroImage Clin 6:9–19

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Fortin JP, Sweeney EM, Muschelli J, Crainiceanu CM, Shinohara RT (2016) Removing inter-subject technical variability in magnetic resonance imaging studies. Neuroimage 132:198–212

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Nyúl LG, Udupa JK (1999) On standardizing the MR image intensity scale. Magn Reson Med 42:1072–1081

    PubMed  Google Scholar 

  25. 25.

    Shah M, Xiao Y, Subbanna N, Francis S, Arnold DL, Collins DL, Arbel T (2011) Evaluating intensity normalization on MRIs of human brain with multiple sclerosis. Med Image Anal 15:267–282

    PubMed  Google Scholar 

  26. 26.

    Goya-Outi J, Orlhac F, Calmon R, Alentorn A, Nioche C, Philippe C, Puget S, Boddaert N, Buvat I, Grill J, Frouin V, Frouin F (2018) Computation of reliable textural indices from multimodal brain MRI: Suggestions based on a study of patients with diffuse intrinsic pontine glioma. Phys Med Biol 63:105003

    PubMed  Google Scholar 

  27. 27.

    Lacroix M, Frouin F, Dirand A-S, Nioche C, Orlhac F, Bernaudin J-F, Brillet P-Y, Buvat I (2020) Correction for magnetic field inhomogeneities and normalization of voxel values are needed to better reveal the potential of MR radiomic features in lung cancer. Front Oncol 10:43

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Um H, Tixier F, Bermudez D, Deasy JO, Young RJ, Veeraraghavan H (2019) Impact of image preprocessing on the scanner dependence of multi-parametric MRI radiomic features and covariate shift in multi-institutional glioblastoma datasets. Phys Med Biol 64:165011

    PubMed  Google Scholar 

  29. 29.

    Moradmand H, Aghamiri SMR, Ghaderi R (2020) Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma. J Appl Clin Med Phys 21:179–190

    PubMed  Google Scholar 

  30. 30.

    Shiradkar R, Ghose S, Jambor I, Taimen P, Ettala O, Purysko AS, Madabhushi A (2018) Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings. J Magn Reson Imaging 48:1626–1636

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Johnson W, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118–127

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Orlhac F, Boughdad S, Philippe C, Stalla-Bourdillon H, Nioche C, Champion L, Soussan M, Frouin F, Frouin V, Buvat I (2018) A postreconstruction harmonization method for multicenter radiomic studies in PET. J Nucl Med 59:1321–1328

    CAS  PubMed  Google Scholar 

  33. 33.

    Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I (2019) Validation of a method to compensate multicenter effects affecting CT radiomics. Radiology 291:53–59

    PubMed  Google Scholar 

  34. 34.

    Orlhac F, Lecler A, Savatovski J, Goya-Outi J, Nioche C, Charbonneau F, Ayache N, Frouin F, Duron L, Buvat I (2020) How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. Eur Radiol 1–9.

  35. 35.

    Whitney HM, Li H, Ji Y, Liu P, Giger ML (2020) Harmonization of radiomic features of breast lesions across international DCE-MRI datasets. J Med Imaging 7:1

    Google Scholar 

  36. 36.

    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 46:1017–1027

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Nioche C, Orlhac F, Boughdad S, Reuze S, Goya-Outi J, Robert C, Pellot-Barakat C, Soussan M, Frouin F, erique, Buvat I, (2018) LIFEx: A freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786–4789

    CAS  PubMed  Google Scholar 

  38. 38.

    Madabhushi A, Udupa JK (2005) Interplay between intensity standardization and inhomogeneity correction in MR image processing. IEEE Trans Med Imaging 24:561–576

    PubMed  Google Scholar 

  39. 39.

    Reinhold JC, Dewey BE, Carass A, Prince JL (2019) Evaluating the impact of intensity normalization on MR image synthesis. In: Proceedings of Medical Imaging 2019: Image Processing, International Society for Optics and Photonics, San Diego, p 126

  40. 40.

    Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR, Davatzikos C, Depeursinge A, Desseroit MC, Dinapoli N, Dinh CV, Echegaray S (2020) The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328–338

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, McInnis M, Phillips ML, Trivedi MH, Weissman MM, Shinohara RT (2018) Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 167:104–120

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Fortin P, Parker D, Tunç B, Watanabe T, Elliott M, Ruparel K, Roalf D, Satterthwaite T, Gur R, Gur R, Schultz R, Verma R, Shinohara R (2017) Harmonization of multi-site diffusion tensor imaging data. Neuroimage 161:149–170

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Isaksson LJ, Raimondi S, Botta F, Pepa M, Gugliandolo SG, De Angelis SP, Marvaso G, Petralia G, De Cobelli O, Gandini S, Cremonesi M, Cattani F, Summers P, Jereczek-Fossa BA (2020) Effects of MRI image normalization techniques in prostate cancer radiomics. Phys Med 71:7–13

    PubMed  Google Scholar 

  44. 44.

    Chatterjee A, Vallieres M, Dohan A, Levesque IR, Ueno Y, Saif S, Reinhold C, Seuntjens J (2019) Creating robust predictive radiomic models for data from independent institutions using normalization. IEEE Trans Radiat Plasma Med Sci 3:210–215

    Google Scholar 

  45. 45.

    Castaldo R, Pane K, Nicolai E, Salvatore M, Franzese M (2020) The impact of normalization approaches to automatically detect radiogenomic phenotypes characterizing breast cancer receptors status. Cancers 12:518

    CAS  PubMed Central  Google Scholar 

  46. 46.

    Bianchini L, Botta F, Origgi D, Rizzo S, Mariani M, Summers P, García-Polo P, Cremonesi M, Lascialfari A (2020) PETER PHAN: An MRI phantom for the optimisation of radiomic studies of the female pelvis. Phys Med 71:71–81

    PubMed  Google Scholar 

  47. 47.

    Valladares A, Beyer T, Rausch I (2020) Physical imaging phantoms for simulation of tumor heterogeneity in PET, CT, and MRI: an overview of existing designs. Med Phys 47:2023–2037

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Sophie Lassalle, radiographer manager, for her help in acquiring the data. We are grateful to the anonymous reviewers for their helpful comments.

Funding

Pia Akl was funded by ‘Bourse Curie M2 2018’ by Institut Curie.

Author information

Affiliations

Authors

Contributions

Saint Martin: Conception and study design, Analysis and interpretation of data, Drafting of manuscript, Critical revision; Orlhac: Conception and study design, Analysis and interpretation of data, Critical revision; Akl: Acquisition of data, Analysis and interpretation of data, Critical revision; Khalid: Acquisition of data, Analysis and interpretation of data, Critical revision; Nioche: Analysis and interpretation of data, Critical revision; Buvat: Analysis and interpretation of data, Critical revision; Malhaire: Conception and study design, Acquisition of data, Critical revision; Frouin: Conception and study design, Analysis and interpretation of data, Critical revision.

Corresponding author

Correspondence to Marie-Judith Saint Martin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file 1 (PDF 293 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Saint Martin, MJ., Orlhac, F., Akl, P. et al. A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study. Magn Reson Mater Phy 34, 355–366 (2021). https://doi.org/10.1007/s10334-020-00892-y

Download citation

Keywords

  • Breast
  • MRI
  • Reproducibility
  • Radiologic phantom
  • Image processing