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European Radiology Experimental
Published in: European Radiology Experimental 1/2019

Open Access 01-12-2019 | Positron Emission Tomography | Original article

Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features

Authors: Wolf-Dieter Vogl, Katja Pinker, Thomas H. Helbich, Hubert Bickel, Günther Grabner, Wolfgang Bogner, Stephan Gruber, Zsuzsanna Bago-Horvath, Peter Dubsky, Georg Langs

Published in: European Radiology Experimental | Issue 1/2019

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Abstract

Background

Multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed a data-driven machine learning approach for a CAD system combining dynamic contrast-enhanced (DCE)-MRI, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose (18F-FDG)-PET.

Methods

The CAD incorporated a random forest (RF) classifier combined with mpPET/MRI intensity-based features for lesion segmentation and shape features, kinetic and spatio-temporal texture features, for lesion classification. The CAD pipeline detected and segmented suspicious regions and classified lesions as benign or malignant. The inherent feature selection method of RF and alternatively the minimum-redundancy-maximum-relevance feature ranking method were used.

Results

In 34 patients, we report a detection rate of 10/12 (83.3%) and 22/22 (100%) for benign and malignant lesions, respectively, a Dice similarity coefficient of 0.665 for segmentation, and a classification performance with an area under the curve at receiver operating characteristics analysis of 0.978, a sensitivity of 0.946, and a specificity of 0.936. Segmentation but not classification performance of DCE-MRI improved with information from DWI and FDG-PET. Feature ranking revealed that kinetic and spatio-temporal texture features had the highest contribution for lesion classification. 18F-FDG-PET and morphologic features were less predictive.

Conclusion

Our CAD enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. It may aid as a novel computational tool for exploring different modalities/features and their contributions for the detection and classification of breast lesions.
Appendix
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Literature
1.
Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM (2010) Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 127:2893–2917CrossRef
2.
3.
Pinker K, Bogner W, Baltzer P et al (2014) Improved differentiation of benign and malignant breast tumors with multiparametric 18fluorodeoxyglucose positron emission tomography magnetic resonance imaging: a feasibility study. Clin Cancer Res 20:3540–3549CrossRef
4.
Ayer T, Ayvaci MU, Liu ZX, Alagoz O, Burnside ES (2010) Computer-aided diagnostic models in breast cancer screening. Imaging Med 2:313–323CrossRef
5.
6.
Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31:198–211CrossRef
7.
Vyborny CJ, Giger ML, Nishikawa RM (2000) Computer-aided detection and diagnosis of breast cancer. Radiol Clin North Am 38:725–740
8.
Morris E, Comstock C, Lee C, Lehman C, Ikeda D, Newstead G (2013) ACR BI-RADS® magnetic resonance imaging. ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System Reston. American College of Radiology, VA, USA
9.
Stoutjesdijk MJ, Fütterer JJ, Boetes C, van Die LE, Jager G, Barentsz JO (2005) Variability in the description of morphologic and contrast enhancement characteristics of breast lesions on magnetic resonance imaging. Investig Radiol 40:355–362
10.
11.
Pinker K, Grabner G, Bogner W et al (2009) A combined high temporal and high spatial resolution 3 Tesla MR imaging protocol for the assessment of breast lesions: initial results. Invest Radiol 44:553–558
12.
Bogner W, Pinker-Domenig K, Bickel H et al (2012) Readout-segmented echo-planar imaging improves the diagnostic performance of diffusion-weighted MR breast examinations at 3.0 T. Radiology 263:64–76CrossRef
13.
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54:2033–2044
14.
Somer EJ, Benatar NA, O'Doherty MJ, Smith MA, Marsden PK (2007) Use of the CT component of PET-CT to improve PET-MR registration: demonstration in soft-tissue sarcoma. Phys Med Biol 52:6991–7006CrossRef
15.
Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16:641–647CrossRef
16.
Chen W, Giger ML, Bick U, Newstead GM (2006) Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med Phys 33:2878–2887CrossRef
17.
Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67:786–804CrossRef
18.
Chen W, Giger ML, Li H, Bick U, Newstead GM (2007) Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med 58:562–571CrossRef
19.
Agner SC, Soman S, Libfeld E et al (2011) Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J Digit Imaging 24:446–463CrossRef
20.
Woods BJ, Clymer BD, Kurc T et al (2007) Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data. J Magn Reson Imaging 25:495–501CrossRef
21.
Nie K, Chen JH, Yu HJ, Chu Y, Nalcioglu O, Su MY (2008) Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol 15:1513–1525CrossRef
22.
Gilhuijs KG, Giger ML, Bick U (1998) Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. Med Phys 25:1647–1654CrossRef
23.
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302
24.
Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238CrossRef
25.
Menze BH, Kelm BM, Masuch R et al (2009) A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics 10:213CrossRef
26.
Pinker K, Bogner W, Baltzer P et al (2014) Improved diagnostic accuracy with multiparametric magnetic resonance imaging of the breast using dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging, and 3-dimensional proton magnetic resonance spectroscopic imaging. Invest Radiol 49:421–430
27.
Oliver A, Freixenet J, Marti J et al (2010) A review of automatic mass detection and segmentation in mammographic images. Med Image Anal 14:87–110CrossRef
28.
Elter M, Horsch A (2009) CADx of mammographic masses and clustered microcalcifications: a review. Med Phys 36:2052–2068
29.
Chen W, Giger ML, Bick U (2006) A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. Acad Radiol 13:63–72CrossRef
30.
31.
Zheng Y, Englander S, Baloch S et al (2009) STEP: spatiotemporal enhancement pattern for MR-based breast tumor diagnosis. Med Phys 36:3192–3204CrossRef
32.
Agner SC, Xu J, Madabhushi A (2013) Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging. Med Phys 40:032305CrossRef
33.
Twellmann T, Lichte O, Nattkemper TW (2005) An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data. IEEE Trans Med Imaging 24:1256–1266CrossRef
34.
Vignati A, Giannini V, De Luca M et al (2011) Performance of a fully automatic lesion detection system for breast DCE-MRI. J Magn Reson Imaging 34:1341–1351CrossRef
35.
Yao J, Chen J, Chow C (2009) Breast tumor analysis in dynamic contrast-enhanced MRI using texture features and wavelet transform. IEEE J Sel Top Signal Process 3:94–100CrossRef
36.
Gubern-Mérida A, Martí R, Melendez J et al (2015) Automated localization of breast cancer in DCE-MRI. Med Image Anal 20:265–274
37.
Han D, Bayouth J, Song Q et al (2011) Globally optimal tumor segmentation in PET-CT images: a graph-based co-segmentation method. In: Székely G, Hahn HK (eds) Information Processing in Medical Imaging. IPMI 2011. Lecture Notes in Computer Science, vol 6801. Springer, Berlin, Heidelberg, pp 245–256
38.
Meinel LA, Stolpen AH, Berbaum KS, Fajardo LL, Reinhardt JM (2007) Breast MRI lesion classification: improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system. J Magn Reson Imaging 25:89–95CrossRef
39.
Gibbs P, Turnbull LW (2003) Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 50:92–98CrossRef
40.
Levman J, Leung T, Causer P, Plewes D, Martel AL (2008) Classification of dynamic contrast-enhanced magnetic resonance breast lesions by support vector machines. IEEE Trans Med Imaging 27:688–696CrossRef
41.
Szabó BK, Wiberg MK, Boné B, Aspelin P (2004) Application of artificial neural networks to the analysis of dynamic MR imaging features of the breast. Eur Radiol 14:1217–1225
42.
McLaren CE, Chen WP, Nie K, Su MY (2009) Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques. Acad Radiol 16:842–851CrossRef
43.
Chen W, Giger ML, Newstead GM et al (2010) Computerized assessment of breast lesion malignancy using DCE-MRI robustness study on two independent clinical datasets from two manufacturers. Acad Radiol 17:822–829CrossRef
44.
Magometschnigg HF, Baltzer PA, Fueger B et al (2015) Diagnostic accuracy of 18F-FDG PET/CT compared with that of contrast-enhanced MRI of the breast at 3 T. Eur J Nucl Med Mol Imaging 42:1656–1665CrossRef
45.
Pinker-Domenig K, Bogner W, Gruber S et al (2012) High resolution MRI of the breast at 3 T: which BI-RADS® descriptors are most strongly associated with the diagnosis of breast cancer. Eur Radiol 22:322–330CrossRef
Metadata
Title
Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features
Authors
Wolf-Dieter Vogl
Katja Pinker
Thomas H. Helbich
Hubert Bickel
Günther Grabner
Wolfgang Bogner
Stephan Gruber
Zsuzsanna Bago-Horvath
Peter Dubsky
Georg Langs
Publication date
01-12-2019
Publisher
Springer International Publishing
Published in
European Radiology Experimental / Issue 1/2019
Electronic ISSN: 2509-9280
DOI
https://doi.org/10.1186/s41747-019-0096-3

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