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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Review Article

Performance Analysis of Various Nanocontrast Agents and CAD Systems for Cancer Diagnosis

Author(s): Ruba Thanapandiyaraj *, Tamilselvi Rajendran and Parisa Beham Mohammedgani

Volume 15, Issue 9, 2019

Page: [831 - 852] Pages: 22

DOI: 10.2174/1573405614666180924124736

Price: $65

Abstract

Background: Cancer is a disease which involves the abnormal cell growth that has the potential of dispersal to other parts of the body. Among various conventional anatomical imaging techniques for cancer diagnosis, Magnetic Resonance Imaging (MRI) provides the best spatial resolution and is noninvasive. Current efforts are directed at enhancing the capabilities of MRI in oncology by adding contrast agents.

Discussion: Recently, the superior properties of nanomaterials (extremely smaller in size, good biocompatibility and ease in chemical modification) allow its application as a contrast agent for early and specific cancer detection through the MRI. The precise detection of cancer region from any imaging modality will lead to a thriving treatment for cancer patients. The better localization of radiation dose can be attained from MRI by using suitable image processing algorithms. As there are many works that have been proposed for automatic detection for cancers, the effort is also put in to provide an effective survey of Computer Aided Diagnosis (CAD) system for different types of cancer detection with increased efficiency based on recent research works. Even though there are many surveys on MRI contrast agents, they only focused on a particular type of cancer. This study deeply presents the use of nanocontrast agents in MRI for different types of cancer diagnosis.

Conclusion: The main aim of this paper is to critically review the available compounds used as nanocontrast agents in MRI modality for different types of cancers. It also includes the review of different methods for cancer cell detection and classification. A comparative analysis is performed to analyze the effect of different CAD systems.

Keywords: Cancer, MRI, contrast agents, nano-MRI, CAD, oncology.

Graphical Abstract

[1]
De Smet K, Campbell P, Van Der Straeten C. The hip resurfacing handbook A practical guide to the use and management of modern hip resurfacings Sawston. Woodhead Publishing 2013.
[2]
Brindle K. New approaches for imaging tumour responses to treatment. Nat Rev Cancer 2008; 8(2): 94-107.
[http://dx.doi.org/10.1038/nrc2289] [PMID: 18202697]
[3]
Weissleder R, Pittet MJ. Imaging in the era of molecular oncology. Nature 2008; 452(7187): 580-9.
[http://dx.doi.org/10.1038/nature06917] [PMID: 18385732]
[4]
Aime S, Dastru W, Gobetto R, Santelia D, Viale A. In:Semmler W, Schwaiger M, Eds. Handbook of Experimental Pharmacology 185/I. Berlin: Springer-Verlag Heidelberg. 2008; pp. 247-72.
[http://dx.doi.org/10.1007/978-3-540-72718-7_12]
[5]
Schröder L. Xenon for NMR biosensing-inert but alert. Phys Med 2013; 29(1): 3-16.
[http://dx.doi.org/10.1016/j.ejmp.2011.11.001] [PMID: 22119272]
[6]
Aime S, Castelli DD, Crich SG, Gianolio E, Terreno E. Pushing the sensitivity envelope of lanthanide-based Magnetic Resonance Imaging (MRI) contrast agents for molecular imaging applications. Acc Chem Res 2009; 42(7): 822-31.
[http://dx.doi.org/10.1021/ar800192p] [PMID: 19534516]
[7]
Lin W, Hyeon T, Lanza GM, Zhang M, Meade TJ. Magnetic nanoparticles for early detection of cancer by magnetic resonance imaging. MRS Bull 2009; 34(6): 441-8.
[http://dx.doi.org/10.1557/mrs2009.120] [PMID: 26166945]
[8]
Villaraza AJL, Bumb A, Brechbiel MW. Macromolecules, dendrimers, and nanomaterials in magnetic resonance imaging: the interplay between size, function, and pharmacokinetics. Chem Rev 2010; 110(5): 2921-59.
[http://dx.doi.org/10.1021/cr900232t] [PMID: 20067234]
[9]
Rosa L, Blackledge J, Boretti A. Nano-Magnetic Resonance Imaging (Nano-MRI) gives personalized medicine a new perspective. Biomedicines 2017; 5(1): 7.
[http://dx.doi.org/10.3390/biomedicines5010007] [PMID: 28536350]
[10]
Alexei Bogdanov Jr and Mary L. Mazzanti.Molecular magnetic resonance contrast agents for the detection of cancer: past and present. Semin Oncol 2011; 38(1): 42-54.
[11]
Blasiak B, Frank CJM. Van Veggel, Tomanek B. Applications of nanoparticles for MRI cancer diagnosis and therapy. J Nanomater 2013; 2013: 1-12.
[http://dx.doi.org/10.1155/2013/148578]
[12]
Revia RA, Zhang M. Magnetite nanoparticles for cancer diagnosis, treatment, and treatment monitoring: recent advances. Mater Today (Kidlington) 2016; 19(3): 157-68.
[http://dx.doi.org/10.1016/j.mattod.2015.08.022] [PMID: 27524934]
[13]
Latorre M, Rinaldi C. Applications of magnetic nanoparticles in medicine: magnetic fluid hyperthermia. P R Health Sci J 2009; 28(3): 227-38.
[PMID: 19715115]
[14]
Roco M. Nanoscale science and engineering: unifying and transforming tools. AIChE J 2004; 50(5): 890-7.
[http://dx.doi.org/10.1002/aic.10087]
[15]
Meyers JD, Doane T, Burda C, Basilion JP. Nanoparticles for imaging and treating brain cancer. Nanomedicine (Lond) 2013; 8(1): 123-43.
[http://dx.doi.org/10.2217/nnm.12.185] [PMID: 23256496]
[16]
Kelkar SS, Reineke TM. Theranostics: combining imaging and therapy. Bioconjug Chem 2011; 22(10): 1879-903.
[http://dx.doi.org/10.1021/bc200151q] [PMID: 21830812]
[17]
Shimada K, Isoda H, Hirokawa Y, Arizono S, Shibata T, Togashi K. Comparison of gadolinium-EOB-DTPA-enhanced and diffusion-weighted liver MRI for detection of small hepatic metastases. Eur Radiol 2010; 20(11): 2690-8.
[http://dx.doi.org/10.1007/s00330-010-1842-3] [PMID: 20563726]
[18]
Faucher L, Guay-Bégin A-A, Lagueux J, Côté M-F, Petitclerc E, Fortin M-A. Ultra-small gadolinium oxide nanoparticles to image brain cancer cells in vivo with MRI. Contrast Media Mol Imaging 2011; 6(4): 209-18.
[PMID: 21861281]
[19]
Liu Y, Chen Z, Liu C, Yu D, Lu Z, Zhang N. Gadolinium-loaded polymeric nanoparticles modified with Anti-VEGF as multifunctional MRI contrast agents for the diagnosis of liver cancer. Biomaterials 2011; 32(22): 5167-76.
[http://dx.doi.org/10.1016/j.biomaterials.2011.03.077] [PMID: 21521627]
[20]
Faucher L, Tremblay M, Lagueux J, Gossuin Y, Fortin M-A. Rapid synthesis of PEGylated ultrasmall gadolinium oxide nanoparticles for cell labeling and tracking with MRI. ACS Appl Mater Interfaces 2012; 4(9): 4506-15.
[http://dx.doi.org/10.1021/am3006466] [PMID: 22834680]
[21]
Kim T, Momin E, Choi J, et al. Mesoporous silica-coated hollow manganese oxide nanoparticles as positive T1 contrast agents for labeling and MRI tracking of adipose-derived mesenchymal stem cells. J Am Chem Soc 2011; 133(9): 2955-61.
[http://dx.doi.org/10.1021/ja1084095] [PMID: 21314118]
[22]
Na H, Lee J, An K, et al. Development of a T1 contrast agent for magnetic resonance imaging using MnO nanoparticles. Angew Chem 2007; 119: 5493-7.
[http://dx.doi.org/10.1002/ange.200604775]
[23]
Wang Y-XJ, Hussain SM, Krestin GP. Superparamagnetic iron oxide contrast agents: physicochemical characteristics and applications in MR imaging. Eur Radiol 2001; 11(11): 2319-31.
[http://dx.doi.org/10.1007/s003300100908] [PMID: 11702180]
[24]
Varallyay P, Nesbit G, Muldoon LL, et al. Comparison of two superparamagnetic viral-sized iron oxide particles ferumoxides and ferumoxtran-10 with a gadolinium chelate in imaging intracranial tumors. AJNR Am J Neuroradiol 2002; 23(4): 510-9.
[PMID: 11950637]
[25]
Lee H-Y, Lee S-H, Xu C, et al. Synthesis and characterization of PVP-coated large core iron oxide nanoparticles as an MRI contrast agent. Nanotechnology 2008; 19(16)165101
[http://dx.doi.org/10.1088/0957-4484/19/16/165101] [PMID: 21394237]
[26]
Yang L, Peng X-H, Wang YA, et al. Receptor-targeted nanoparticles for in vivo imaging of breast cancer. Clin Cancer Res 2009; 15(14): 4722-32.
[http://dx.doi.org/10.1158/1078-0432.CCR-08-3289] [PMID: 19584158]
[27]
Lu J, Ma S, Sun J, et al. Manganese ferrite nanoparticle micellar nanocomposites as MRI contrast agent for liver imaging. Biomaterials 2009; 30(15): 2919-28.
[http://dx.doi.org/10.1016/j.biomaterials.2009.02.001] [PMID: 19230966]
[28]
Tomanek B, Iqbal U, Blasiak B, et al. Evaluation of brain tumor vessels specific contrast agents for glioblastoma imaging. Neuro-oncol 2012; 14(1): 53-63.
[http://dx.doi.org/10.1093/neuonc/nor183] [PMID: 22013169]
[29]
Wu G, Wang X, Deng G, et al. Novel peptide targeting integrin αvβ3-rich tumor cells by magnetic resonance imaging. J Magn Reson Imaging 2011; 34(2): 395-402.
[http://dx.doi.org/10.1002/jmri.22620] [PMID: 21780231]
[30]
Keshtkar M, Shahbazi-Gahrouei D, Mehrgardi M, Aghaei M. Synthesis and cytotoxicity assessment of gold-coated magnetic iron oxide nanoparticles. J Biomed Phys Eng 2016eISSN: 2251
[31]
Khurshid H, Hadjipanayis CG, Chen H, et al. Core/shell structured iron/iron-oxide nanoparticles as excellent MRI contrast enhancement agents. J Magn Magn Mater 2013; 331: 17-20.
[http://dx.doi.org/10.1016/j.jmmm.2012.10.049]
[32]
Khaniabadi PM, Majid AMS, Asif M, Khaniabadi BM, Shahbazi-Gahrouei D, Jaafar MS. Breast cancer cell targeted MR molecular imaging probe: anti-MUC1 antibody-based magnetic nanoparticles. J Phys Conf Ser 2017; 851012014
[http://dx.doi.org/10.1088/1742-6596/851/1/012014]
[33]
Sun C, Fang C, Stephen Z, et al. Tumor-targeted drug delivery and MRI contrast enhancement by chlorotoxin-conjugated iron oxide nanoparticles. Nanomedicine (Lond) 2008; 3(4): 495-505.
[http://dx.doi.org/10.2217/17435889.3.4.495] [PMID: 18694312]
[34]
Koh DM, Brown G, Riddell AM, et al. Detection of colorectal hepatic metastases using MnDPDP MR imaging and diffusion-weighted imaging (DWI) alone and in combination. Eur Radiol 2008; 18(5): 903-10.
[http://dx.doi.org/10.1007/s00330-007-0847-z] [PMID: 18193234]
[35]
van Kessel CS, Veldhuis WB, van den Bosch MAAJ, van Leeuwen MS. MR liver imaging with Gd-EOB-DTPA: a delay time of 10 minutes is sufficient for lesion characterisation. Eur Radiol 2012; 22(10): 2153-60.
[http://dx.doi.org/10.1007/s00330-012-2486-2] [PMID: 22645040]
[36]
Brismar TB, Dahlström N, Edsborg N, Persson A, Smedby O, Albiin N. Liver vessel enhancement by Gd-BOPTA and Gd-EOB-DTPA: a comparison in healthy volunteers. Acta Radiol 2009; 50(7): 709-15.
[http://dx.doi.org/10.1080/02841850903055603] [PMID: 19701821]
[37]
Bianchi A, Dufort S, Lux F, et al. Targeting and in vivo imaging of non-small-cell lung cancer using nebulized multimodal contrast agents. Proc Natl Acad Sci USA 2014; 111(25): 9247-52.
[http://dx.doi.org/10.1073/pnas.1402196111] [PMID: 24927562]
[38]
Zhou Z, Qutaish M, Han Z, et al. MRI detection of breast cancer micrometastases with a fi-bronectin-targeting contrast agent. Nat Commun 2015; 6: 7984.
[http://dx.doi.org/10.1038/ncomms8984]
[39]
Fukuda Y, Ando K, Ishikura R, et al. Superparamagnetic iron oxide (SPIO) MRI contrast agent for bone marrow imaging: differentiating bone metastasis and osteomyelitis. Magn Reson Med Sci 2006; 5(4): 191-6.
[http://dx.doi.org/10.2463/mrms.5.191] [PMID: 17332709]
[40]
Schmitz SA, Coupland SE, Gust R, et al. Superparamagnetic iron oxide-enhanced MRI of atherosclerotic plaques in Watanabe hereditable hyperlipidemic rabbits. Invest Radiol 2000; 35(8): 460-71.
[http://dx.doi.org/10.1097/00004424-200008000-00002] [PMID: 10946973]
[41]
Ruehm SG, Corot C, Vogt P, Kolb S, Debatin JF. Magnetic resonance imaging of atherosclerotic plaque with ultrasmall superparamagnetic particles of iron oxide in hyperlipidemic rabbits. Circulation 2001; 103(3): 415-22.
[http://dx.doi.org/10.1161/01.CIR.103.3.415] [PMID: 11157694]
[42]
Shahbazi-Gahrouei D, Williams M, Rizvi S, Allen BJ. In vivo studies of Gd-DTPA-monoclonal antibody and gd-porphyrins: potential magnetic resonance imaging contrast agents for melanoma. J Magn Reson Imaging 2001; 14(2): 169-74.
[http://dx.doi.org/10.1002/jmri.1168] [PMID: 11477676]
[43]
U.S Food & Drug Administration. Available from: https://www. fda.gov/Drugs/DrugSafety/PostmarketDrugSafetyInfo
[44]
National Multiple Sclerosis Society. Available from: https://www.nationalmssociety.org/About-the-Society/News/FDA
[45]
Bahadure NB, Ray AK, Thethi HP. Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int J Biomed Imaging 2017; 20179749108
[http://dx.doi.org/10.1155/2017/9749108] [PMID: 28367213]
[46]
Varuna Shree N, Kumar TNR. Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inform 2018; 5(1): 23-30.
[http://dx.doi.org/10.1007/s40708-017-0075-5] [PMID: 29313301]
[47]
Dipali B, Birnale B, Patil SN. Brain tumor MRI image segmentation using FCM and SVM. IJESC 2016; 6(12): 3939-42.
[48]
Singh G, Ansari M. Efficient detection of brain tumor from MRIs using K-means segmentation and normalized histogram. In: 1st India International Conference on Information Processing (IICIP). Delhi, India. 2016; pp. 1-6.
[49]
Rani N, Vashisth S. Brain tumor detection and classification with feed forward back-prop neural network. Int J Comput Appl 2016; 146(12): 1-6.
[50]
Alfonse M, Salem AB. An automatic classification of brain tumors through MRI using support vector machine. Egypt Comp Sci J 2016; 40(3): 11-21.
[51]
Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJ, Isgum I. Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 2016; 35(5): 1252-61.
[http://dx.doi.org/10.1109/TMI.2016.2548501] [PMID: 27046893]
[52]
Pereira S. Pinto a, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI Images. IEEE Trans Med Imaging 2016; 35(5): 2140-51.
[http://dx.doi.org/10.1109/TMI.2016.2538465] [PMID: 26960222]
[53]
Madheswaran M, Dhas DAS. Classification of brain MRI images using support vector machine with various Kernels. Biomed Res 2015; 26(3): 505-13.
[54]
Vaishnavee KB, Amshakala K. An automated MRI brain image segmentation and tumor detection using SOM- clustering and Proximal Support Vector Machine classifier. In: IEEE International Conference on Engineering and Technology (ICETECH). Coimbatore, India. 2015; pp. 1-6.
[http://dx.doi.org/10.1109/ICETECH.2015.7275030]
[55]
Nandpuru HB, Salankar SS, Bora VR. MRI brain cancer classification using support vector machine. In: IEEE Students'Conference on Electrical, Electronics and Computer Science. Bhopal, India. 2014; pp. 1-6.
[56]
Marrone S, Piantadosi G, Fuscoy R, Petrilloy A, Sansone M, Sansone C. Breast segmentation using Fuzzy C-Means and anatomical priors in DCE-MRI. In: 23rd International Conference on Pattern Recognition (ICPR). Cancun, Mexico: Cancun Center 2016.
[57]
BenAmeur ST, Wendling L. Dorra Sellami. Detection and analysis of breast masses from MRIs and dual energy contrast enhanced mammography. In: International Image Processing Applications and Systems Conference IPAS’16. Hammamet, Tunisia. 2016; pp. 1-5.
[58]
Chaiyakhan K, Kerdprasop N, Kerdprasop K. Feature selection techniques forbreast cancer image classification with support vector machine. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists. Hong Kong. 2016; pp. 1-6.
[59]
Gnonnou C, Smaoui N. Segmentation and 3D reconstruction of MRI images for breast cancer detection. In: International Image Processing, Applications and Systems Conference Sfax, Tunisia. 2014; 1-6.
[60]
Moftah HM, Azar AT, Al-Shammari ET, Ghali NI, Hassanien AE, Shoman M. Adaptive k-means clustering algorithm for MR breast image segmentation. Neural Comput Appl 2013; 24(7-8): 1917-28.
[http://dx.doi.org/10.1007/s00521-013-1437-4]
[61]
Levman J, Leung T, Causer P, Plewes D, Martel AL. Classification of dynamic contrast-enhanced magnetic resonance breast lesions by support vector machines. IEEE Trans Med Imaging 2008; 27(5): 688-96.
[http://dx.doi.org/10.1109/TMI.2008.916959] [PMID: 18450541]
[62]
Arbach L, Stolpenb A, Reinhardta JM. Classification of breast MRI lesions using a backpropagation neural network (BNN). In: 2nd IEEE International Symposium on Biomedical Imaging: Nano toMacro (IEEE Cat No. 04EX821). Arlington, VA, USA 2004; 253-6.
[63]
Dimililer K, Ugur B, Ever YK. Tumor detection on ct lung images using image enhancement. Online J Sci Technol 2017; 7(1): 133-8.
[64]
Asuntha A, Brindha A, Indirani S, Srinivasan A. Lung cancer detection using SVM algorithm and optimization techniques. JCHPS 2016; 9(4): 3198-203.
[65]
Madhubala G, Aroquiaraj IL. Lung cancer image segmentation and classification using soft computing techniques. Int J Comput Int Sys 2016; 6(2): 120-6.
[66]
Sakthineela PK, Muhammadusathikraja MS. Early stage diagnosis of lung cancer using ct-scan images based on cellular learning automate. IJIRAE 2016; 3(4): 41-5.
[67]
Thamilselvan P, Sathiaseelan JGR. Detection and classification of lung cancer MRI images by using enhanced k nearest neighbor algorithm. Indian J Sci Technol 2016; 9(43): 1-7.
[http://dx.doi.org/10.17485/ijst/2016/v9i43/104642]
[68]
Shriwas RS, Dikondawar AD. Lung cancer detection and prediction by using neural network. IIJEC 2015; 3(1): 17-21.
[69]
Suseendran G, Manivannan M. Lung cancer image segmentation using rough set theory. Indian J Med Healthcare 2015; 4(6): 1-8.
[70]
Tun KMM, Khaing AS. Feature extraction and classification of lung cancer nodule using image processing techniques. Int J Eng Res Technol 2014; 3(3): 2204-10.
[71]
Gajdhane VA, Deshpande LM. Detection of lung cancer stages on ct scan images by using various image processing techniquesIOSR-JCE 2014; 16(5 Ver. III): 28-35.
[72]
Ada. Minimal Feature Set Extraction for Classification of Lung Cancer CT-Scan Images. Indian J Res 2013; 3(4): 147-9.
[73]
Sobecki P, Życka-Malesa D, Mykhalevych I, Sklinda K, Przelaskowski A. MRI imaging texture features in prostate lesions classification.EMBEC & NBC 2017.In: Eskola H, Väisänen O, Viik J, Hyttinen J, Eds. EMBEC & NBC 2017. IFMBE Proceedings. Singapore: Springer;. 827-30.
[74]
Triguia R, Mitéran BJ, Walker PM, Sellami L, Hamid AB. Automatic classification and localization of prostate cancer usingmulti-parametric MRI/MRS. Biomed Signal Process Control 2017; 31: 189-98.
[75]
Wang X, Yang W, Weinreb J, et al. Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning. Sci Rep 2017; 7(1): 15415.
[76]
Chung AG, Khalvati F, Shafiee MJ, Haider MA, Wong A. Prostate cancer detection via a quantitative radiomics-driven conditional random field framework. IEEE Access 2015; 3: 2531-41.
[http://dx.doi.org/10.1109/ACCESS.2015.2502220]
[77]
Litjens G, Debats O, Barentsz J, Karssemeijer N, Huisman H. Computer-aided detection of prostate cancer in MRI. IEEE Trans Med Imaging 2014; 33(5): 1083-92.
[http://dx.doi.org/10.1109/TMI.2014.2303821] [PMID: 24770913]
[78]
Niaf É, Flamary R, Rouvière O, Lartizien C, Canu S. Kernel-based learning from both qualitative and quantitative labels: application to prostate cancer diagnosis based on multiparametric MR imaging. IEEE Trans Image Process 2014; 23(3): 979-91.
[http://dx.doi.org/10.1109/TIP.2013.2295759] [PMID: 24464613]
[79]
Artan Y, Oto A, Yetik IS. Cross-device automated prostate cancer localization with multiparametric MRI. IEEE Trans Image Process 2013; 22(12): 5385-94.
[80]
Artan Y, Haider MA, Langer DL, et al. Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields. IEEE Trans Image Process 2010; 19(9): 2444-55.
[http://dx.doi.org/10.1109/TIP.2010.2048612] [PMID: 20716496]
[81]
Ozer S, Langer DL, Liu X, et al. Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI. Med Phys 2010; 37(4): 1873-83.
[http://dx.doi.org/10.1118/1.3359459] [PMID: 20443509]
[82]
Asuntha A, Srinivasan A. Bone cancer detection using artificial neural network. Indian J Soc Res 2018; 17(2): 56-63.
[83]
Durgadevi G, Ramprabu G, Shobana S. Detection of enchodroma tumor in MRI imges using SVM clasifier. Int J Pharm Technol 2017; 9(2): 29861-6.
[84]
Mistry KD, Talati BJ. An approach to detect bone tumor using comparative analysis of segmentation technique. IJIRCCE 2016; 4(5): 8176-84.
[85]
Binhssan A. Enchondroma tumor Detection. Int J Adv Res Comput Commun Eng 2015; 4(6): 1-4.
[86]
Avula M, Lakkakula NP, Raja MP. Bone cancer detection from MRI scan imagery using mean pixel intensity. In: 2014 8th Asia Modelling Symposium. Taipei, Taiwan. 2014; 141-6.

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