Abstract
This study proposes a novel dual S-shaped logistic model for automatically quantifying the characteristic kinetic curves of breast lesions and for distinguishing malignant from benign breast tumors on dynamic contrast enhanced (DCE) magnetic resonance (MR) images. D(α,β) is the diagnostic parameter derived from the logistic model. Significant differences were found in D(α,β) between the malignant benign groups. Fisher’s Linear Discriminant analysis correctly classified more than 90% of the benign and malignant kinetic breast data using the derived diagnostic parameter (D(α,β)). Receiver operating characteristic curve analysis of the derived diagnostic parameter (D(α,β)) indicated high sensitivity and specificity to differentiate malignancy from benignancy. The dual S-shaped logistic model was effectively used to fit the kinetic curves of breast lesions in DCE-MR. Separation between benign and malignant breast lesions was achieved with sufficient accuracy by using the derived diagnostic parameter D(α,β) as the lesion’s feature. The proposed method therefore has the potential for computer-aided diagnosis in breast tumors.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
American Cancer Society. Cancer Facts & Figures, 2009. Atlanta: American Cancer Society, Inc., 2009. 9–11
American Cancer Society. Breast Cancer Facts & Figures 2009–2010. Atlanta: American Cancer Society, Inc., 2009. 2–8
Kneeshaw P J, Lowry M, Manton D, et al. Differentiation of benign from malignant breast disease associated with screening detected microcalcifications using dynamic contrast-enhanced magnetic resonance imaging. Breast, 2006, 15: 29–38
Bazzocchi M, Zuiani C, Painizza P, et al. Contrast-enhanced breast MRI in patients with suspicious microcalcifications on mammography: results of a multi-centre trial. AJR Am J Roentgenol, 2006, 186: 1723–1732
Hata T, Takahashi H, Watanabe K, et al. Magnetic resonance imaging for preoperative evaluation of breast cancer: a comparative study with mammography and ultrasound. J Am Coll Surg, 2004, 198: 190–197
Pickles M D, Lowry M, Manton D J, et al. Role of dynamic contrast-enhanced MRI in monitoring early response of locally advanced breast cancer to neoadjuvant chemotherapy. Breast Cancer Res Treat, 2005, 91: 1–10
Wasser K, Klein S K, Fink C, et al. Evaluation of neoadjuvant chemotherapeutic response of breast cancer using dynamic MRI with high temporal resolution. Eur Radiol, 2003, 13: 80–87
Padhani A R, Hayes C, Assersohn L, et al. Prediction of clinicopathological response of breast cancer to primary chemotherapy at contrast-enhanced MR imaging: initial clinical results. Radiology, 2006, 239: 361–374
Fischer U, von Heyden D, Vosshenrich R, et al. Signal characteristics of benign and malignant breast lesions in dynamic 2D-MRT of the breast. Fortschritte auf dem Gebiete der Rontgenstrahlen und der Neuen Bildgebenden Verfahren, 1993, 158: 287–292
Gibbs P, Liney G P, Lowry M, et al. Differentiation of benign and malignant sub-1cm breast lesions using dynamic contrast-enhanced MRI. Breast, 2004, 13: 115–121
Goto M, Ito H, Akazawa K, et al. Diagnosis of breast tumors by contrast-enhanced MR imaging: comparison between the diagnostic performance of dynamic enhancement patterns and morphological features. J Magn Reson Imaging, 2007, 25: 104–112
Kelcz F, Santyr G E, Cron G O, et al. Application of a quantitative model to differentiate benign from malignant breast lesions detected by dynamic contrast-enhanced MRI. J Magn Reson Imaging, 1996, 6: 743–752
Tofts P S, Kermode A G. Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. Magn Reson Med, 1991, 17: 357–367
Tofts P S, Brix G, Buckley D L, et al. Estimating kinetic parameters from dynamic contrast-enhanced T1-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging, 1999, 10: 223–232
Hassid Y, Furman-Haran E, Margalit R, et al. Noninvasive magnetic resonance imaging of transport and interstitial fluid pressure in ectopic human lung tumors. Cancer Res, 2006, 66: 4159–4166
Reed R K, Rubin K. Transcapillary exchange: role and importance of the interstitial fluid pressure and the extracellular matrix. Cardiovasc Res, 2010, 87: 211–217
Abdullah N, Mesurolle B, El-Khoury M, et al. Breast imaging reporting and data system lexicon for US: interobserver agreement for assessment of breast masses. Radiology, 2009, 252: 665–672
Kuhl C K, Mielcareck P, Klaschik S, et al. Dynamic breast MR im aging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology, 1999, 211: 101–110
Szabo B K, Aspelin P, Wiberg M K, et al. Dynamic MR imaging of the breast. Analysis of kinetic and morphologic diagnostic criteria. Acta Radiol, 2003, 44: 379–386
Jansen S A, Fan X, Karczmar G S, et al. Differentiation between benign and malignant breast lesions detected by bilateral dynamic contrast-enhanced MRI: a sensitivity and specificity study. Magn Reson Med, 2008, 59: 747–754
Heiberg E V, Perman W H, Herrmann V M, et al. Dynamic sequential 3D gadolinium-enhanced MRI of the whole breast. Magn Reson Imaging, 1996, 14: 337–348
Moate P J, Dougherty L, Schnall M D, et al. A modified logistic model to describe gadolinium kinetics in breast tumors. Magn Reson Imaging, 2004, 22: 467–473
Lagarias J C, Reeds J A, Wright M H, et al. Convergence properties of the nelder-mead simplex method in low dimensions. SIAM J Optimiz, 1998, 9: 112–147
Knopp M V, Weiss E, Sinn H P, et al. Pathophysiologic basis of contrast enhancement in breast tumors. J Magn Reson Imaging, 1999, 10: 260–266
Kinkel K, Hylton N M. Challenges to interpretation of breast MRI. J Magn Reson Imaging, 2001, 13: 821–829
Bone B, Wiberg M K, Parrado C, et al. Mechanism of contrast enhancement in breast lesions at MR imaging. Acta Radiologica, 1998, 39: 494–500
Buckley D L, Drew P J, Mussurakis S, et al. Microvessel density in invasive breast cancer assessed by dynamic Gd-DTPA enhanced MRI. J Magn Reson Imaging, 1997, 7: 461–464
Padhani A R. Dynamic contrast-enhanced MRI studies in human tumours. Br J Radiol, 1999, 72: 427–431
Parker G J, Suckling J, Tanner S F, et al. Probing tumor microvascularity by measurement, analysis and display of contrast agent uptake kinetics. J Magn Reson Imaging, 1997, 7: 564–574
Roberts H C, Roberts T P, Brasch R C, et al. Quantitative measurement of microvascular permeability in human brain tumors achieved using dynamic contrast-enhanced MR imaging: correlation with histologic grade. AJNR Am J Neuroradiol, 2000, 21: 891–899
Donahue K M, Weisskoff R M, Burstein D. Water diffusion and exchange as they influence contrast enhancement. J Magn Reson Imaging, 1997, 7: 102–110
Larsson H B, Rosenbaum S, Fritz-Hansen T. Quantification of the effect of water exchange in dynamic contrast MRI perfusion measurements in the brain and heart. Magn Reson Med, 2001, 46: 272–281
Buckley D L. Letter to the editor: transcytolemal water exchange and its effect on the determination of contrast agent concentration in vivo. Magn Reson Med, 2002, 47: 420–421
Calamante F, Morup M, Hansen L K. Defining a local arterial input function for perfusion MRI using independent component analysis. Magn Reson Med, 2004, 52: 789–797
Wang H N, Cui Y M, He H. A logistic model for magnetic energy storage in solar active regions. Res Astron Astrophys, 2009, 9: 687–693
Peleg M, Corradini M G, Normand M D. The logistic (Verhulst) model for sigmoid microbial growth curves revisited. Food Res Int, 2007, 40: 808–818
Sakuma Y, Okamoto N. A logistic regression predictive model and the outcome of patients with resected lung adenocarcinoma of 2 cm or less in size. Lung Cancer, 2009, 65: 85–90
Mussarakis S, Buckley D L, Horsman A. Dynamic MRI of invasive breast cancer: assessment of three region-of-interest analysis methods. J Comput Assist Tomogr, 1997, 21: 431–438
Gribbestad I S, Nilsen G, Fjosne H E, et al. Comparative signal intensity measurements in dynamic gadolinium-enhanced MR mammography. J Magn Reson Imaging, 1994, 4: 477–480
Author information
Authors and Affiliations
Corresponding authors
Additional information
This article is published with open access at Springerlink.com
Rights and permissions
Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
About this article
Cite this article
Dang, Y., Guo, L., Lv, D. et al. Classification of breast lesions based on a dual S-shaped logistic model in dynamic contrast enhanced magnetic resonance imaging. Sci. China Life Sci. 54, 889–896 (2011). https://doi.org/10.1007/s11427-011-4221-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11427-011-4221-7