Top

Published in:

01-08-2015 | Breast

Evaluation of Kinetic Entropy of Breast Masses Initially Found on MRI using Whole-lesion Curve Distribution Data: Comparison with the Standard Kinetic Analysis

Authors: Akiko Shimauchi, Hiroyuki Abe, David V. Schacht, Jian Yulei, Federico D. Pineda, Sanaz A. Jansen, Rajiv Ganesh, Gillian M. Newstead

Published in: European Radiology | Issue 8/2015

Abstract

Objectives

To quantify kinetic heterogeneity of breast masses that were initially detected with dynamic contrast-enhanced MRI, using whole-lesion kinetic distribution data obtained from computer-aided evaluation (CAE), and to compare that with standard kinetic curve analysis.

Methods

Clinical MR images from 2006 to 2011 with breast masses initially detected with MRI were evaluated with CAE. The relative frequencies of six kinetic patterns (medium-persistent, medium-plateau, medium-washout, rapid-persistent, rapid-plateau, rapid-washout) within the entire lesion were used to calculate kinetic entropy (KE), a quantitative measure of enhancement pattern heterogeneity. Initial uptake (IU) and signal enhancement ratio (SER) were obtained from the most-suspicious kinetic curve. Mann-Whitney U test and ROC analysis were conducted for differentiation of malignant and benign masses.

Results

Forty benign and 37 malignant masses comprised the case set. IU and SER were not significantly different between malignant and benign masses, whereas KE was significantly greater for malignant than benign masses (p = 0.748, p = 0.083, and p < 0.0001, respectively). Areas under ROC curve for IU, SER, and KE were 0.479, 0.615, and 0.662, respectively.

Conclusion

Quantification of kinetic heterogeneity of whole-lesion time-curve data with KE has the potential to improve differentiation of malignant from benign breast masses on breast MRI.

Key points

• Kinetic heterogeneity can be quantified by computer-aided evaluation of breast MRI

• Kinetic entropy was greater in malignant masses than benign masses

• Kinetic entropy has the potential to improve differentiation of breast masses

Kuhl CK, Mielcareck P, Klaschik S et al (1999) Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology 211:101–110PubMedCrossRef

Schnall MD, Blume J, Bluemke DA et al (2006) Diagnostic architectural and dynamic features at breast MR imaging: multicenter study. Radiology 238:42–53PubMedCrossRef

Szabo BK, Aspelin P, Wiberg MK, Bone B (2003) Dynamic MR imaging of the breast. Analysis of kinetic and morphologic diagnostic criteria. Acta Radiol 44:379–386PubMed

Wang LC, DeMartini WB, Partridge SC, Peacock S, Lehman CD (2009) MRI-detected suspicious breast lesions: predictive values of kinetic features measured by computer-aided evaluation. AJR Am J Roentgenol 193:826–831PubMedCrossRef

Jansen SA, Shimauchi A, Zak L et al (2009) Kinetic curves of malignant lesions are not consistent across MRI systems: need for improved standardization of breast dynamic contrast-enhanced MRI acquisition. AJR Am J Roentgenol 193:832–839PubMedCentralPubMedCrossRef

Woodhams R, Matsunaga K, Kan S et al (2005) ADC mapping of benign and malignant breast tumors. Magn Reson Med Sci 4:35–42PubMedCrossRef

Partridge SC, Demartini WB, Kurland BF, Eby PR, White SW, Lehman CD (2010) Differential diagnosis of mammographically and clinically occult breast lesions on diffusion-weighted MRI. J Magn Reson Imaging 31:562–570PubMedCrossRef

Hatakenaka M, Soeda H, Yabuuchi H et al (2008) Apparent diffusion coefficients of breast tumors: clinical application. Magn Reson Med Sci 7:23–29PubMedCrossRef

Baltzer PA, Benndorf M, Dietzel M, Gajda M, Camara O, Kaiser WA (2010) Sensitivity and specificity of unenhanced MR mammography (DWI combined with T2-weighted TSE imaging, ueMRM) for the differentiation of mass lesions. Eur Radiol 20:1101–1110PubMedCrossRef

10.

Spick C, Pinker-Domenig K, Rudas M, Helbich TH, Baltzer PA (2014) MRI-only lesions: application of diffusion-weighted imaging obviates unnecessary MR-guided breast biopsies. Eur Radiol 24:1204–1210PubMedCrossRef

11.

Marini C, Iacconi C, Giannelli M, Cilotti A, Moretti M, Bartolozzi C (2007) Quantitative diffusion-weighted MR imaging in the differential diagnosis of breast lesion. Eur Radiol 17:2646–2655PubMedCrossRef

12.

Guo Y, Cai YQ, Cai ZL et al (2002) Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging 16:172–178PubMedCrossRef

13.

Kuhl CK (2000) MRI of breast tumors. Eur Radiol 10:46–58PubMedCrossRef

14.

Wedegärtner U, Bick U, Wörtler K, Rummeny E, Bongartz G (2001) Differentiation between benign and malignant findings on MR-mammography: usefulness of morphological criteria. Eur Radiol 11:1645–1650PubMedCrossRef

15.

Buadu LD, Murakami J, Murayama S et al (1996) Breast lesions: correlation of contrast medium enhancement patterns on MR images with histopathologic findings and tumor angiogenesis. Radiology 200:639–649PubMedCrossRef

16.

Kinkel K, Helbich TH, Esserman LJ et al (2000) Dynamic high-spatial-resolution MR imaging of suspicious breast lesions: diagnostic criteria and interobserver variability. AJR Am J Roentgenol 175:35–43PubMedCrossRef

17.

Mussurakis S, Buckley DL, Coady AM, Turnbull LW, Horsman A (1996) Observer variability in the interpretation of contrast enhanced MRI of the breast. Br J Radiol 69:1009–1016PubMedCrossRef

18.

Pan J, Dogan BE, Carkaci S et al (2013) Comparing performance of the CADstream and the DynaCAD breast MRI CAD systems : CADstream vs. DynaCAD in breast MRI. J Digit Imaging 26:971–976PubMedCentralPubMedCrossRef

19.

Renz DM, Baltzer PA, Kullnig PE et al (2008) Clinical value of computer-assisted analysis in MR mammography. A comparison between two systems and three observers with different levels of experience. Röfo 180:968–976PubMed

20.

Liu YH, Xu L, Liu LH et al (2014) 3.0 T MR-CAD: Clinical Value in Diagnosis of Breast Tumor Compared with Conventional MRI. J Cancer 5:585–589PubMedCentralPubMedCrossRef

21.

Lehman CD, Blume JD, DeMartini WB, Hylton NM, Herman B, Schnall MD (2013) Accuracy and interpretation time of computer-aided detection among novice and experienced breast MRI readers. AJR Am J Roentgenol 200:W683–W689PubMedCrossRef

22.

(2013) ACR BI-RADS: magnetic resonance imaging, 2nd edn. In: ACR Breast Imaging Reporting and Data System: breast imaging atlas. 5th edn. Reston

23.

Lehman CD, Peacock S, DeMartini WB, Chen X (2006) A new automated software system to evaluate breast MR examinations: improved specificity without decreased sensitivity. AJR Am J Roentgenol 187:51–56PubMedCrossRef

24.

Hauth EA, Jaeger HJ, Maderwald S, Muehler A, Kimmig R, Forsting M (2008) Quantitative 2- and 3-dimensional analysis of pharmacokinetic model-derived variables for breast lesions in dynamic, contrast-enhanced MR mammography. Eur J Radiol 66:300–308PubMedCrossRef

25.

Chen W, Giger ML, Lan L, Bick U (2004) Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. Med Phys 31:1076–1082PubMedCrossRef

26.

Gilhuijs KG, Deurloo EE, Muller SH, Peterse JL, Schultze Kool LJ (2002) Breast MR imaging in women at increased lifetime risk of breast cancer: clinical system for computerized assessment of breast lesions initial results. Radiology 225:907–916PubMedCrossRef

27.

Jansen SA, Fan X, Karczmar GS et al (2008) DCEMRI of breast lesions: is kinetic analysis equally effective for both mass and nonmass-like enhancement? Med Phys 35:3102–3109PubMedCentralPubMedCrossRef

28.

Newell D, Nie K, Chen JH et al (2010) Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement. Eur Radiol 20:771–781PubMedCentralPubMedCrossRef

29.

Banik S, Rangayyan RM, Desautels JE (2013) Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms. Int J Comput Assist Radiol Surg 8:121–134PubMedCrossRef

30.

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 17:822–829PubMedCentralPubMedCrossRef

31.

Kierans AS, Bennett GL, Mussi TC et al (2013) Characterization of malignancy of adnexal lesions using ADC entropy: comparison with mean ADC and qualitative DWI assessment. J Magn Reson Imaging 37:164–171PubMedCrossRef

32.

Wang TC, Huang YH, Huang CS et al (2013) Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis. Magn Reson Imaging. doi:10.1016/j.mri.2013.12.002 PubMedCentral

33.

Baltzer PA, Renz DM, Kullnig PE, Gajda M, Camara O, Kaiser WA (2009) Application of computer-aided diagnosis (CAD) in MR-mammography (MRM): do we really need whole lesion time curve distribution analysis? Acad Radiol 16:435–442PubMedCrossRef

34.

Karahaliou A, Vassiou K, Arikidis NS, Skiadopoulos S, Kanavou T, Costaridou L (2010) Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis. Br J Radiol 83:296–309PubMedCentralPubMedCrossRef

35.

Preim U, Glaßer S, Preim B, Fischbach F, Ricke J (2012) Computer-aided diagnosis in breast DCE-MRI–quantification of the heterogeneity of breast lesions. Eur J Radiol 81:1532–1538PubMedCrossRef

36.

Azlan CA, Di Giovanni P, Ahearn TS, Semple SI, Gilbert FJ, Redpath TW (2010) B1 transmission-field inhomogeneity and enhancement ratio errors in dynamic contrast-enhanced MRI (DCE-MRI) of the breast at 3 T. J Magn Reson Imaging 31:234–239PubMedCrossRef

37.

Rahbar H, Partridge SC, Demartini WB, Gutierrez RL, Parsian S, Lehman CD (2012) Improved B1 homogeneity of 3 Tesla breast MRI using dual-source parallel radiofrequency excitation. J Magn Reson Imaging 35:1222–1226PubMedCentralPubMedCrossRef

38.

Schmidt MA, Borri M, Scurr E et al (2013) Breast dynamic contrast-enhanced examinations with fat suppression: are contrast-agent uptake curves affected by magnetic field inhomogeneity? Eur Radiol 23:1537–1545PubMedCrossRef

39.

Kaiser WA, Zeitler E (1989) MR imaging of the breast: fast imaging sequences with and without Gd-DTPA. Preliminary observations. Radiology 170:681–686PubMedCrossRef

40.

Mussurakis S, Gibbs P, Horsman A (1998) Peripheral enhancement and spatial contrast uptake heterogeneity of primary breast tumours: quantitative assessment with dynamic MRI. J Comput Assist Tomogr 22:35–46PubMedCrossRef

41.

Stomper PC, Herman S, Klippenstein DL et al (1995) Suspect breast lesions: findings at dynamic gadolinium-enhanced MR imaging correlated with mammographic and pathologic features. Radiology 197:387–395PubMedCrossRef

42.

Shimauchi A, Jansen SA, Abe H, Jaskowiak N, Schmidt RA, Newstead GM (2010) Breast cancers not detected at MRI: review of false-negative lesions. AJR Am J Roentgenol 194:1674–1679PubMedCrossRef

43.

Schnall MD, Rosten S, Englander S, Orel SG, Nunes LW (2001) A combined architectural and kinetic interpretation model for breast MR images. Acad Radiol 8:591–597PubMedCrossRef

44.

Tozaki M (2004) Interpretation of breast MRI: correlation of kinetic and morphological parameters with pathological findings. Magn Reson Med Sci 3:189–197PubMedCrossRef

Title: Evaluation of Kinetic Entropy of Breast Masses Initially Found on MRI using Whole-lesion Curve Distribution Data: Comparison with the Standard Kinetic Analysis
Authors: Akiko Shimauchi
Hiroyuki Abe
David V. Schacht
Jian Yulei
Federico D. Pineda
Sanaz A. Jansen
Rajiv Ganesh
Gillian M. Newstead
Publication date: 01-08-2015
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 8/2015
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-015-3635-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Evaluation of Kinetic Entropy of Breast Masses Initially Found on MRI using Whole-lesion Curve Distribution Data: Comparison with the Standard Kinetic Analysis

Abstract

Objectives

Methods

Results

Conclusion

Key points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusion

Key points

Please log in to get access to this content

Other articles of this Issue 8/2015

Evaluation of several FDG PET parameters for prediction of soft tissue tumour grade at primary diagnosis and recurrence

Comparison of contrast-enhanced ultrasound and contrast-enhanced computed tomography in evaluating the treatment response to transcatheter arterial chemoembolization of hepatocellular carcinoma using modified RECIST

Appendiceal diameter as a predictor of appendicitis in children: improved diagnosis with three diagnostic categories derived from a logistic predictive model

In vivo analysis of physiological 3D blood flow of cerebral veins

CT findings of minimally invasive adenocarcinoma (MIA) of the lung and comparison of solid portion measurement methods at CT in 52 patients

Ultrasound versus magnetic resonance imaging for Morton neuroma: systematic review and meta-analysis