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.
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.
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.
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Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images are more than pictures, they are data. Radiology 278:563–577
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
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
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
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
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
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
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
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
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
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
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
Saha A, Yu X, Sahoo D, Mazurowski MA (2017) Effects of MRI scanner parameters on breast cancer radiomics. Expert Syst Appl 87:384–391
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
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
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
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
Song S, Zheng Y, He Y (2017) A review of methods for bias correction in medical images. Biomed Eng Rev 1:1
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
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
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
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
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
Nyúl LG, Udupa JK (1999) On standardizing the MR image intensity scale. Magn Reson Med 42:1072–1081
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
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
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
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
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
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
Johnson W, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118–127
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
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
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.
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
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
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
Madabhushi A, Udupa JK (2005) Interplay between intensity standardization and inhomogeneity correction in MR image processing. IEEE Trans Med Imaging 24:561–576
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
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
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
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
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
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
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
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
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
We thank Sophie Lassalle, radiographer manager, for her help in acquiring the data. We are grateful to the anonymous reviewers for their helpful comments.
Pia Akl was funded by ‘Bourse Curie M2 2018’ by Institut Curie.
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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
- Radiologic phantom
- Image processing