Impact of Tracer Retention Levels on Visual Analysis of Cerebral [18F]- Florbetaben Pet Images

Author(s): Giampiero Giovacchini, Elisabetta Giovannini, Elisa Borsò, Patrizia Lazzeri, Valerio Duce, Ornella Ferrando, Franca Foppiano, Andrea Ciarmiello*

Journal Name: Current Radiopharmaceuticals

Volume 14 , Issue 1 , 2021


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Abstract:

Background: To compare visual and semi-quantitative analysis of brain [18F]Florbetaben PET images in Mild Cognitive Impairment (MCI) patients and relate this finding to the degree of ß-amyloid burden.

Methods: A sample of 71 amnestic MCI patients (age 74 ± 7.3 years, Mini Mental State Examination 24.2 ± 5.3) underwent cerebral [18F]Florbetaben PET/CT. Images were visually scored as positive or negative independently by three certified readers blinded to clinical and neuropsychological assessment. Amyloid positivity was also assessed by semiquantitative approach by means of a previously published threshold (SUVr ≥ 1.3). Fleiss kappa coefficient was used to compare visual analysis (after consensus among readers) and semi-quantitative analysis. Statistical significance was taken at P<0.05.

Results: After the consensus reading, 43/71 (60.6%) patients were considered positive. Cases that were interpreted as visually positive had higher SUVr than visually negative patients (1.48 ± 0.19 vs 1.11 ± 0.09) (P<0.05). Agreement between visual analysis and semi-quantitative analysis was excellent (k=0.86, P<0.05). Disagreement occurred in 7/71 patients (9.9%) (6 false positives and 1 false negative). Agreement between the two analyses was 90.0% (18/20) for SUVr < 1.1, 83% (24/29) for SUVr between 1.1 and 1.5, and 100% (22/22) for SUVr > 1.5 indicating lowest agreement for the group with intermediate amyloid burden.

Conclusion: Inter-rater agreement of visual analysis of amyloid PET images is high. Agreement between visual analysis and SUVr semi-quantitative analysis decreases in the range of 1.1< SUVr <=1.5, where the clinical scenario is more challenging.

Keywords: PET images, cognitive impairment, Alzheimer's disease, brain PET, nuclear medicine, neurology.

[1]
Mountz, J.M.; Laymon, C.M.; Cohen, A.D.; Zhang, Z.; Price, J.C.; Boudhar, S.; McDade, E.; Aizenstein, H.J.; Klunk, W.E.; Mathis, C.A. Comparison of qualitative and quantitative imaging characteristics of [11C]PiB and [18F]flutemetamol in normal control and Alzheimer’s subjects. Neuroimage Clin., 2015, 9, 592-598.
[http://dx.doi.org/10.1016/j.nicl.2015.10.007] [PMID: 26640770]
[2]
Mirra, S.S.; Heyman, A.; McKeel, D.; Sumi, S.M.; Crain, B.J.; Brownlee, L.M.; Vogel, F.S.; Hughes, J.P.; van Belle, G.; Berg, L. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology, 1991, 41(4), 479-486.
[http://dx.doi.org/10.1212/WNL.41.4.479] [PMID: 2011243]
[3]
Hyman, B.T.; Trojanowski, J.Q. Consensus recommendations for the postmortem diagnosis of Alzheimer disease from the National Institute on Aging and the Reagan Institute Working Group on diagnostic criteria for the neuropathological assessment of Alzheimer disease. J. Neuropathol. Exp. Neurol., 1997, 56(10), 1095-1097.
[http://dx.doi.org/10.1097/00005072-199710000-00002] [PMID: 9329452]
[4]
Klunk, W.E.; Engler, H.; Nordberg, A.; Wang, Y.; Blomqvist, G.; Holt, D.P.; Bergström, M.; Savitcheva, I.; Huang, G.F.; Estrada, S.; Ausén, B.; Debnath, M.L.; Barletta, J.; Price, J.C.; Sandell, J.; Lopresti, B.J.; Wall, A.; Koivisto, P.; Antoni, G.; Mathis, C.A.; Långström, B. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann. Neurol., 2004, 55(3), 306-319.
[http://dx.doi.org/10.1002/ana.20009] [PMID: 14991808]
[5]
Mathis, C.A.; Wang, Y.; Klunk, W.E. Imaging beta-amyloid plaques and neurofibrillary tangles in the aging human brain. Curr. Pharm. Des., 2004, 10(13), 1469-1492.
[http://dx.doi.org/10.2174/1381612043384772] [PMID: 15134570]
[6]
Sabri, O.; Seibyl, J.; Rowe, C.; Barthel, H. Beta-amyloid imaging with florbetaben. Clin. Transl. Imaging, 2015, 3(1), 13-26.
[http://dx.doi.org/10.1007/s40336-015-0102-6] [PMID: 25741488]
[7]
Curtis, C.; Gamez, J.E.; Singh, U.; Sadowsky, C.H.; Villena, T.; Sabbagh, M.N.; Beach, T.G.; Duara, R.; Fleisher, A.S.; Frey, K.A.; Walker, Z.; Hunjan, A.; Holmes, C.; Escovar, Y.M.; Vera, C.X.; Agronin, M.E.; Ross, J.; Bozoki, A.; Akinola, M.; Shi, J.; Vandenberghe, R.; Ikonomovic, M.D.; Sherwin, P.F.; Grachev, I.D.; Farrar, G.; Smith, A.P.; Buckley, C.J.; McLain, R.; Salloway, S. Phase 3 trial of flutemetamol labeled with radioactive fluorine 18 imaging and neuritic plaque density. JAMA Neurol., 2015, 72(3), 287-294.
[http://dx.doi.org/10.1001/jamaneurol.2014.4144] [PMID: 25622185]
[8]
Yang, L.; Rieves, D.; Ganley, C. Brain amyloid imaging--FDA approval of florbetapir F18 injection. N. Engl. J. Med., 2012, 367(10), 885-887.
[http://dx.doi.org/10.1056/NEJMp1208061] [PMID: 22931256]
[9]
Price, J.C.; Klunk, W.E.; Lopresti, B.J.; Lu, X.; Hoge, J.A.; Ziolko, S.K.; Holt, D.P.; Meltzer, C.C.; DeKosky, S.T.; Mathis, C.A. Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J. Cereb. Blood Flow Metab., 2005, 25(11), 1528-1547.
[http://dx.doi.org/10.1038/sj.jcbfm.9600146] [PMID: 15944649]
[10]
Ito, H.; Shimada, H.; Shinotoh, H.; Takano, H.; Sasaki, T.; Nogami, T.; Suzuki, M.; Nagashima, T.; Takahata, K.; Seki, C.; Kodaka, F.; Eguchi, Y.; Fujiwara, H.; Kimura, Y.; Hirano, S.; Ikoma, Y.; Higuchi, M.; Kawamura, K.; Fukumura, T.; Böö, E.L.; Farde, L.; Suhara, T. Quantitative Analysis of Amyloid Deposition in Alzheimer Disease Using PET and the Radiotracer ¹¹C-AZD2184. J. Nucl. Med., 2014, 55(6), 932-938.
[http://dx.doi.org/10.2967/jnumed.113.133793] [PMID: 24732152]
[11]
Hatashita, S.; Yamasaki, H.; Suzuki, Y.; Tanaka, K.; Wakebe, D.; Hayakawa, H. [18F]Flutemetamol amyloid-beta PET imaging compared with [11C]PIB across the spectrum of Alzheimer’s disease. Eur. J. Nucl. Med. Mol. Imaging, 2014, 41(2), 290-300.
[http://dx.doi.org/10.1007/s00259-013-2564-y] [PMID: 24085503]
[12]
Ng, S.; Villemagne, V.L.; Berlangieri, S.; Lee, S.T.; Cherk, M.; Gong, S.J.; Ackermann, U.; Saunder, T.; Tochon-Danguy, H.; Jones, G.; Smith, C.; O’Keefe, G.; Masters, C.L.; Rowe, C.C. Visual assessment versus quantitative assessment of 11C-PIB PET and 18F-FDG PET for detection of Alzheimer’s disease. J. Nucl. Med., 2007, 48(4), 547-552.
[http://dx.doi.org/10.2967/jnumed.106.037762] [PMID: 17401090]
[13]
Rosario, B.L.; Weissfeld, L.A.; Laymon, C.M.; Mathis, C.A.; Klunk, W.E.; Berginc, M.D.; James, J.A.; Hoge, J.A.; Price, J.C. Inter-rater reliability of manual and automated region-of-interest delineation for PiB PET. Neuroimage, 2011, 55(3), 933-941.
[http://dx.doi.org/10.1016/j.neuroimage.2010.12.070] [PMID: 21195782]
[14]
Ciarmiello, A.; Tartaglione, A.; Giovannini, E.; Riondato, M.; Giovacchini, G.; Ferrando, O.; De Biasi, M.; Passera, C.; Carabelli, E.; Mannironi, A.; Foppiano, F.; Alfano, B.; Mansi, L. Amyloid burden identifies neuropsychological phenotypes at increased risk of progression to Alzheimer’s disease in mild cognitive impairment patients. Eur. J. Nucl. Med. Mol. Imaging, 2019, 46(2), 288-296.
[http://dx.doi.org/10.1007/s00259-018-4149-2] [PMID: 30244387]
[15]
Petersen, R.C.; Smith, G.E.; Waring, S.C.; Ivnik, R.J.; Tangalos, E.G.; Kokmen, E. Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol., 1999, 56(3), 303-308.
[http://dx.doi.org/10.1001/archneur.56.3.303] [PMID: 10190820]
[16]
Morris, J.C.; Heyman, A.; Mohs, R.C.; Hughes, J.P.; van Belle, G.; Fillenbaum, G.; Mellits, E.D.; Clark, C. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology, 1989, 39(9), 1159-1165.
[http://dx.doi.org/10.1212/WNL.39.9.1159] [PMID: 2771064]
[17]
Román, G.C.; Tatemichi, T.K.; Erkinjuntti, T.; Cummings, J.L.; Masdeu, J.C.; Garcia, J.H.; Amaducci, L.; Orgogozo, J.M.; Brun, A.; Hofman, A. Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology, 1993, 43(2), 250-260.
[http://dx.doi.org/10.1212/WNL.43.2.250] [PMID: 8094895]
[18]
Tiepolt, S.; Barthel, H.; Butzke, D.; Hesse, S.; Patt, M.; Gertz, H.J.; Reininger, C.; Sabri, O. Influence of scan duration on the accuracy of β-amyloid PET with florbetaben in patients with Alzheimer’s disease and healthy volunteers. Eur. J. Nucl. Med. Mol. Imaging, 2013, 40(2), 238-244.
[http://dx.doi.org/10.1007/s00259-012-2268-8] [PMID: 23104671]
[19]
Becker, G.A.; Ichise, M.; Barthel, H.; Luthardt, J.; Patt, M.; Seese, A.; Schultze-Mosgau, M.; Rohde, B.; Gertz, H.J.; Reininger, C.; Sabri, O. PET quantification of 18F-florbetaben binding to β-amyloid deposits in human brains. J. Nucl. Med., 2013, 54(5), 723-731.
[http://dx.doi.org/10.2967/jnumed.112.107185] [PMID: 23471310]
[20]
Seibyl, J.; Catafau, A.M.; Barthel, H.; Ishii, K.; Rowe, C.C.; Leverenz, J.B.; Ghetti, B.; Ironside, J.W.; Takao, M.; Akatsu, H.; Murayama, S.; Bullich, S.; Mueller, A.; Koglin, N.; Schulz-Schaeffer, W.J.; Hoffmann, A.; Sabbagh, M.N.; Stephens, A.W.; Sabri, O. Impact of Training Method on the Robustness of the Visual Assessment of 18F-Florbetaben PET Scans: Results from a Phase-3 Study. J. Nucl. Med., 2016, 57(6), 900-906.
[http://dx.doi.org/10.2967/jnumed.115.161927] [PMID: 26823561]
[21]
Martínez, G.; Vernooij, R.W.; Fuentes Padilla, P.; Zamora, J.; Flicker, L.; Bonfill Cosp, X. 18F PET with florbetaben for the early diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst. Rev., 2017, 11CD012883
[http://dx.doi.org/10.1002/14651858.CD012883] [PMID: 29164600]
[22]
Yamane, T.; Ishii, K.; Sakata, M.; Ikari, Y.; Nishio, T.; Ishii, K.; Kato, T.; Ito, K.; Senda, M. J-ADNI Study Group. Inter-rater variability of visual interpretation and comparison with quantitative evaluation of 11C-PiB PET amyloid images of the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) multicenter study. Eur. J. Nucl. Med. Mol. Imaging, 2017, 44(5), 850-857.
[http://dx.doi.org/10.1007/s00259-016-3591-2] [PMID: 27966045]
[23]
Friston, K.J.; Holmes, A.P.; Worsley, K.J.; Poline, J.P.; Frith, C.D.; Frackowiak, R.S.J. Statistical parametricmaps in functional imaging: a general linear approach. Hum. Brain Mapp., 1994, 2, 189-210.
[http://dx.doi.org/10.1002/hbm.460020402]
[24]
Sasaki, K.; Maikusa, N.; Imabayashi, E.; Yuasa, T.; Matsuda, H. The feasibility of 11C-PIB-PET/CT for amyloid plaque burden: validation of the effectiveness of CT-based partial volume correction. Brain Behav., 2016, 6(10)e00532
[http://dx.doi.org/10.1002/brb3.532] [PMID: 27781145]
[25]
Lancaster, J.L.; Woldorff, M.G.; Parsons, L.M.; Liotti, M.; Freitas, C.S.; Rainey, L.; Kochunov, P.V.; Nickerson, D.; Mikiten, S.A.; Fox, P.T. Automated Talairach atlas labels for functional brain mapping. Hum. Brain Mapp., 2000, 10(3), 120-131.
[http://dx.doi.org/10.1002/1097-0193(200007)10:3<120::AID-HBM30>3.0.CO;2-8] [PMID: 10912591]
[26]
Braak, H.; Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol., 1991, 82(4), 239-259.
[http://dx.doi.org/10.1007/BF00308809] [PMID: 1759558]
[27]
Ni, R.; Gillberg, P.G.; Bergfors, A.; Marutle, A.; Nordberg, A. Amyloid tracers detect multiple binding sites in Alzheimer’s disease brain tissue. Brain, 2013, 136(Pt 7), 2217-2227.
[http://dx.doi.org/10.1093/brain/awt142] [PMID: 23757761]
[28]
Sabri, O.; Sabbagh, M.N.; Seibyl, J.; Barthel, H.; Akatsu, H.; Ouchi, Y.; Senda, K.; Murayama, S.; Ishii, K.; Takao, M.; Beach, T.G.; Rowe, C.C.; Leverenz, J.B.; Ghetti, B.; Ironside, J.W.; Catafau, A.M.; Stephens, A.W.; Mueller, A.; Koglin, N.; Hoffmann, A.; Roth, K.; Reininger, C.; Schulz-Schaeffer, W.J. Florbetaben Phase 3 Study Group. Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer’s disease: phase 3 study. Alzheimers Dement., 2015, 11(8), 964-974.
[http://dx.doi.org/10.1016/j.jalz.2015.02.004] [PMID: 25824567]
[29]
Collij, L.E.; Konijnenberg, E.; Reimand, J.; Kate, M.T.; Braber, A.D.; Lopes Alves, I.; Zwan, M.; Yaqub, M.; van Assema, D.M.E.; Wink, A.M.; Lammertsma, A.A.; Scheltens, P.; Visser, P.J.; Barkhof, F.; van Berckel, B.N.M. Assessing Amyloid Pathology in Cognitively Normal Subjects Using 18F-Flutemetamol PET: Comparing Visual Reads and Quantitative Methods. J. Nucl. Med., 2019, 60(4), 541-547.
[http://dx.doi.org/10.2967/jnumed.118.211532] [PMID: 30315145]
[30]
Zwan, M.D.; Ossenkoppele, R.; Tolboom, N.; Beunders, A.J.; Kloet, R.W.; Adriaanse, S.M.; Boellaard, R.; Windhorst, A.D.; Raijmakers, P.; Adams, H.; Lammertsma, A.A.; Scheltens, P.; van der Flier, W.M.; van Berckel, B.N. Comparison of simplified parametric methods for visual interpretation of 11C-Pittsburgh compound-B PET images. J. Nucl. Med., 2014, 55(8), 1305-1307.
[http://dx.doi.org/10.2967/jnumed.114.139121] [PMID: 24898026]
[31]
Lammertsma, A.A. Forward to the Past: The Case for Quantitative PET Imaging. J. Nucl. Med., 2017, 58(7), 1019-1024.
[http://dx.doi.org/10.2967/jnumed.116.188029] [PMID: 28522743]
[32]
Carson, R.E.; Channing, M.A.; Blasberg, R.G.; Dunn, B.B.; Cohen, R.M.; Rice, K.C.; Herscovitch, P. Comparison of bolus and infusion methods for receptor quantitation: application to [18F]cyclofoxy and positron emission tomography. J. Cereb. Blood Flow Metab., 1993, 13(1), 24-42.
[http://dx.doi.org/10.1038/jcbfm.1993.6] [PMID: 8380178]
[33]
Bullich, S.; Barthel, H.; Koglin, N.; Becker, G.A.; De Santi, S.; Jovalekic, A.; Stephens, A.W.; Sabri, O. Validation of Noninvasive Tracer Kinetic Analysis of 18F-Florbetaben PET Using a Dual-Time-Window Acquisition Protocol. J. Nucl. Med., 2018, 59(7), 1104-1110.
[http://dx.doi.org/10.2967/jnumed.117.200964] [PMID: 29175981]
[34]
Ong, K.T.; Villemagne, V.L.; Bahar-Fuchs, A.; Lamb, F.; Langdon, N.; Catafau, A.M.; Stephens, A.W.; Seibyl, J.; Dinkelborg, L.M.; Reininger, C.B.; Putz, B.; Rohde, B.; Masters, C.L.; Rowe, C.C. Aβ imaging with 18F-florbetaben in prodromal Alzheimer’s disease: a prospective outcome study. J. Neurol. Neurosurg. Psychiatry, 2015, 86(4), 431-436.
[http://dx.doi.org/10.1136/jnnp-2014-308094] [PMID: 24970906]
[35]
Bullich, S.; Villemagne, V.L.; Catafau, A.M.; Jovalekic, A.; Koglin, N.; Rowe, C.C.; De Santi, S. Optimal Reference Region to Measure Longitudinal Amyloid-β Change with 18F-Florbetaben PET. J. Nucl. Med., 2017, 58(8), 1300-1306.
[http://dx.doi.org/10.2967/jnumed.116.187351] [PMID: 28183994]
[36]
Nayate, A.P.; Dubroff, J.G.; Schmitt, J.E.; Nasrallah, I.; Kishore, R.; Mankoff, D.; Pryma, D.A. Alzheimer’s Disease Neuroimaging Initiative. Use of Standardized Uptake Value Ratios Decreases Interreader Variability of [18F] Florbetapir PET Brain Scan Interpretation. AJNR Am. J. Neuroradiol., 2015, 36(7), 1237-1244.
[http://dx.doi.org/10.3174/ajnr.A4281] [PMID: 25767185]
[37]
Bullich, S.; Seibyl, J.; Catafau, A.M.; Jovalekic, A.; Koglin, N.; Barthel, H.; Sabri, O.; De Santi, S. Optimized classification of 18F-Florbetaben PET scans as positive and negative using an SUVR quantitative approach and comparison to visual assessment. Neuroimage Clin., 2017, 15, 325-332.
[http://dx.doi.org/10.1016/j.nicl.2017.04.025] [PMID: 28560157]
[38]
Schmidt, M.E.; Chiao, P.; Klein, G.; Matthews, D.; Thurfjell, L.; Cole, P.E.; Margolin, R.; Landau, S.; Foster, N.L.; Mason, N.S.; De Santi, S.; Suhy, J.; Koeppe, R.A.; Jagust, W. Alzheimer’s Disease Neuroimaging Initiative. The influence of biological and technical factors on quantitative analysis of amyloid PET: Points to consider and recommendations for controlling variability in longitudinal data. Alzheimers Dement., 2015, 11(9), 1050-1068.
[http://dx.doi.org/10.1016/j.jalz.2014.09.004] [PMID: 25457431]
[39]
Thal, D.R.; Rüb, U.; Orantes, M.; Braak, H. Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology, 2002, 58(12), 1791-1800.
[http://dx.doi.org/10.1212/WNL.58.12.1791] [PMID: 12084879]
[40]
Knight, W.D.; Okello, A.A.; Ryan, N.S.; Turkheimer, F.E.; Rodríguez Martinez de Llano, S.; Edison, P.; Douglas, J.; Fox, N.C.; Brooks, D.J.; Rossor, M.N. Carbon-11-Pittsburgh compound B positron emission tomography imaging of amyloid deposition in presenilin 1 mutation carriers. Brain, 2011, 134(Pt 1), 293-300.
[http://dx.doi.org/10.1093/brain/awq310] [PMID: 21084313]
[41]
Lilja, J.; Leuzy, A.; Chiotis, K.; Savitcheva, I.; Sörensen, J.; Nordberg, A. Spatial Normalization of 18F-Flutemetamol PET Images Using an Adaptive Principal-Component Template. J. Nucl. Med., 2019, 60(2), 285-291.
[http://dx.doi.org/10.2967/jnumed.118.207811] [PMID: 29903930]
[42]
Akamatsu, G.; Ikari, Y.; Ohnishi, A.; Nishida, H.; Aita, K.; Sasaki, M.; Yamamoto, Y.; Sasaki, M.; Senda, M. Automated PET-only quantification of amyloid deposition with adaptive template and empirically pre-defined ROI. Phys. Med. Biol., 2016, 61(15), 5768-5780.
[http://dx.doi.org/10.1088/0031-9155/61/15/5768] [PMID: 27405579]
[43]
Donaghy, P.C.; Firbank, M.J.; Thomas, A.J.; Lloyd, J.; Petrides, G.; Barnett, N.; Olsen, K.; O’Brien, J.T. Clinical and imaging correlates of amyloid deposition in dementia with Lewy bodies. Mov. Disord., 2018, 33(7), 1130-1138.
[http://dx.doi.org/10.1002/mds.27403] [PMID: 29672930]
[44]
Hutton, C.; Declerck, J.; Mintun, M.A.; Pontecorvo, M.J.; Devous, M.D., Sr; Joshi, A.D. Alzheimer’s Disease Neuroimaging Initiative. Quantification of 18F-florbetapir PET: comparison of two analysis methods. Eur. J. Nucl. Med. Mol. Imaging, 2015, 42(5), 725-732.
[http://dx.doi.org/10.1007/s00259-015-2988-7] [PMID: 25652817]
[45]
Scheinin, N.M.; Wikman, K.; Jula, A.; Perola, M.; Vahlberg, T.; Rokka, J.; Någren, K.; Viitanen, M.; Rinne, J.O. Cortical ¹¹C-PIB uptake is associated with age, APOE genotype, and gender in “healthy aging”. J. Alzheimers Dis., 2014, 41(1), 193-202.
[http://dx.doi.org/10.3233/JAD-132783] [PMID: 24603945]
[46]
Beach, T.G.; Monsell, S.E.; Phillips, L.E.; Kukull, W. Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005-2010. J. Neuropathol. Exp. Neurol., 2012, 71(4), 266-273.
[http://dx.doi.org/10.1097/NEN.0b013e31824b211b] [PMID: 22437338]


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VOLUME: 14
ISSUE: 1
Year: 2021
Published on: 29 July, 2020
Page: [70 - 77]
Pages: 8
DOI: 10.2174/1874471013666200729155717
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