Abstract
Purpose
While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a machine learning scheme using basic and advanced MR sequences for distinguishing different types of brain tumors.
Methods
The study cohort included 141 patients (41 glioblastoma, 38 metastasis, 50 meningioma, and 12 primary central nervous system lymphoma). A computer-assisted classification scheme, combining morphologic MRI, perfusion MRI, and DTI metrics, was developed and used for tumor classification. The proposed multistep scheme consists of pre-processing, ROI definition, features extraction, feature selection, and classification. Feature subset selection was performed using support vector machines (SVMs). Classification performance was assessed by leave-one-out cross-validation. Given an ROI, the entire classification process was done automatically via computer and without any human intervention.
Results
A binary hierarchical classification tree was chosen. In the first step, selected features were chosen for distinguishing glioblastoma from the remaining three classes, followed by separation of meningioma from metastasis and PCNSL, and then to discriminate PCNSL from metastasis. The binary SVM classification accuracy, sensitivity and specificity for glioblastoma, metastasis, meningiomas, and primary central nervous system lymphoma were 95.7, 81.6, and 91.2%; 92.7, 95.1, and 93.6%; 97, 90.8, and 58.3%; and 91.5, 90, and 96.9%, respectively.
Conclusion
A machine learning scheme using data from anatomical and advanced MRI sequences resulted in high-performance automatic tumor classification algorithm. Such a scheme can be integrated into clinical decision support systems to optimize tumor classification.
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References
Chang SC, Lai PH, Chen WL, Weng HH, Ho JT, Wang JS, Chang CY, Pan HB, Yang CF (2002) Diffusion-weighted MRI features of brain abscess and cystic or necrotic brain tumors: comparison with conventional MRI. Clin Imaging 26:227–236
Lai PH, Hsu SS, Ding SW, Ko CW, Fu JH, Weng MJ et al (2007) Proton magnetic resonance spectroscopy and diffusion-weighted imaging in intracranial cystic mass lesions. Surg Neurol 68(Suppl 1):S25–S36
Tsolaki E, Kousi E, Svolos P, Kapsalaki E, Theodorou K, Kappas C, Tsougos I (2014) Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques. World J Radiol 6:72–81
Mabray MC, Barajas RF Jr, Cha S (2015) Modern brain tumor imaging. Brain Tumor Res Treat 3:8–23
Law M, Cha S, Knopp EA, Johnson G, Arnett J, Litt AW (2002) High-grade gliomas and solitary metastases: differentiation by using perfusion and proton spectroscopic MR imaging. Radiology 222:715–721
Svolos P, Kousi E, Kapsalaki E, Theodorou K, Fezoulidis I, Kappas C et al (2014) The role of diffusion and perfusion weighted imaging in the differential diagnosis of cerebral tumors: a review and future perspectives. Cancer Imaging 14:20
Svolos P, Tsolaki E, Kapsalaki E, Theodorou K, Fountas K, Fezoulidis I, Tsougos I (2013) Investigating brain tumor differentiation with diffusion and perfusion metrics at 3-T MRI using pattern recognition techniques. Magn Reson Imaging 31:1567–1577
Yang G, Jones TL, Barrick TR, Howe FA (2014) Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p:q tensor decomposition of diffusion tensor imaging. NMR Biomed 27:1103–1111
Zacharaki EI, Wang S, Chawla S, Soo YD, Wolf R, Melhem ER et al (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62:1609–1618
Zacharaki EI, Kanas VG, Davatzikos C (2011) Investigating machine learning techniques for MRI-based classification of brain neoplasms. Int J Comput Assist Radiol Surg 6:821–828
Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17:87–97
Tustison N, Gee J (2009) N4ITK: Nick’s N3 ITK implementation for MRI bias field correction. The Insight Journal
Leemans A, Jeurissen B, Sijbers J, Jones DK (2009) ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. 17th Annual Meeting of Intl Soc Mag Reson Med, Hawaii, USA, p 3537
Le BD, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N et al (2001) Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 13:534–546
Papageorgiou TS, Chourmouzi D, Drevelengas A, Kouskouras K, Siountas A (2015) Diffusion tensor imaging in brain tumors: a study on gliomas and metastases. Phys Med 10
Lorenz C (2004) Automated perfusion-weighted MRI metrics via localized arterial input functions. Massachusetts Institute of Technology
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
Li B, Meng MQ (2012) Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection. IEEE Trans Inf Technol Biomed 16:323–329
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, NY, USA
Tsougos I, Svolos P, Kousi E, Fountas K, Theodorou K, Fezoulidis I, Kapsalaki E (2012) Differentiation of glioblastoma multiforme from metastatic brain tumor using proton magnetic resonance spectroscopy, diffusion and perfusion metrics at 3 T. Cancer Imaging 12:423–436
Server A, Kulle B, Gadmar OB, Josefsen R, Kumar T, Nakstad PH (2011) Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas. Eur J Radiol 80:462–470
Wang S, Kim S, Chawla S, Wolf RL, Knipp DE, Vossough A, O'Rourke DM, Judy KD, Poptani H, Melhem ER (2011) Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging. AJNR Am J Neuroradiol 32:507–514
Georgiadis P, Cavouras D, Kalatzis I, Daskalakis A, Kagadis GC, Sifaki K, Malamas M, Nikiforidis G, Solomou E (2008) Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features. Comput Methods Prog Biomed 89:24–32
Mohsen H, El-Dahshan ES, El-Horbaty ES, Salem AB (2018) Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal 3:68–71
Georgiadis P, Cavouras D, Kalatzis I, Glotsos D, Athanasiadis E, Kostopoulos S, Sifaki K, Malamas M, Nikiforidis G, Solomou E (2009) Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods. Magn Reson Imaging 27:120–130
Kunimatsu A, Kunimatsu N, Yasaka K, Akai H, Kamiya K, Watadani T et al (2018) Machine learning-based texture analysis of contrast-enhanced MR imaging to differentiate between glioblastoma and primary central nervous system lymphoma. Magn Reson Med Sci:10–0178
Georgiadis P, Kostopoulos S, Cavouras D, Glotsos D, Kalatzis I, Sifaki K, Malamas M, Solomou E, Nikiforidis G (2011) Quantitative combination of volumetric MR imaging and MR spectroscopy data for the discrimination of meningiomas from metastatic brain tumors by means of pattern recognition. Magn Reson Imaging 29:525–535
Tsolaki E, Svolos P, Kousi E, Kapsalaki E, Fountas K, Theodorou K, Tsougos I (2013) Automated differentiation of glioblastomas from intracranial metastases using 3-T MR spectroscopic and perfusion data. Int J Comput Assist Radiol Surg 8:751–761
Inano R, Oishi N, Kunieda T, Arakawa Y, Yamao Y, Shibata S, Kikuchi T, Fukuyama H, Miyamoto S (2014) Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading. Neuroimage Clin 5:396–407
Citak-Er F, Firat Z, Kovanlikaya I, Ture U, Ozturk-Isik E (2018) Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3 T. Comput Biol Med 99:154–160. https://doi.org/10.1016/j.compbiomed.2018.06.009
De LC, Beausang A, Cryan J, Loftus T, Buckley PG, Farrell M et al (2018) Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status. J Neuro-Oncol:10–2895
Cha S (2006) Update on brain tumor imaging: from anatomy to physiology. AJNR Am J Neuroradiol 27:475–487
Hakyemez B, Yildirim N, Erdogan C, Kocaeli H, Korfali E, Parlak M (2006) Meningiomas with conventional MRI findings resembling intraaxial tumors: can perfusion-weighted MRI be helpful in differentiation? Neuroradiology 48:695–702
Kremer S, Grand S, Remy C, Esteve F, Lefournier V, Pasquier B et al (2002) Cerebral blood volume mapping by MR imaging in the initial evaluation of brain tumors. J Neuroradiol 29:105–113
Senturk S, Oguz KK, Cila A (2009) Dynamic contrast-enhanced susceptibility-weighted perfusion imaging of intracranial tumors: a study using a 3-T MR scanner. Diagn Interv Radiol 15:3–12
Wang S, Kim S, Chawla S, Wolf RL, Zhang WG, O'Rourke DM, Judy KD, Melhem ER, Poptani H (2009) Differentiation between glioblastomas and solitary brain metastases using diffusion tensor imaging. Neuroimage 44:653–660
Inoue T, Ogasawara K, Beppu T, Ogawa A, Kabasawa H (2005) Diffusion tensor imaging for preoperative evaluation of tumor grade in gliomas. Clin Neurol Neurosurg 107:174–180
De Belder FE, Oot AR, Van HW, Venstermans C, Menovsky T, Van M et al (2012) Diffusion tensor imaging provides an insight into the microstructure of meningiomas, high-grade gliomas, and peritumoral edema. J Comput Assist Tomogr 36:577–582
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SS and MS have collaborated on this project: SS was responsible for the study design, data collection and part of the pre-processing; MS was responsible for the computational aspect of analysis and for development of the diagnostic model. Both authors took substantial part in the submitted manuscript.
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Shrot, S., Salhov, M., Dvorski, N. et al. Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme. Neuroradiology 61, 757–765 (2019). https://doi.org/10.1007/s00234-019-02195-z
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DOI: https://doi.org/10.1007/s00234-019-02195-z