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[123I]Metaiodobenzylguanidine (MIBG) Cardiac Scintigraphy and Automated Classification Techniques in Parkinsonian Disorders

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Abstract

Purpose

To provide reliable and reproducible heart/mediastinum (H/M) ratio cut-off values for parkinsonian disorders using two machine learning techniques, Support Vector Machines (SVM) and Random Forest (RF) classifier, applied to [123I]MIBG cardiac scintigraphy.

Procedures

We studied 85 subjects, 50 with idiopathic Parkinson’s disease, 26 with atypical Parkinsonian syndromes (P), and 9 with essential tremor (ET). All patients underwent planar early and delayed cardiac scintigraphy after [123I]MIBG (111 MBq) intravenous injection. Images were evaluated both qualitatively and quantitatively; the latter by the early and delayed H/M ratio obtained from regions of interest (ROIt1 and ROIt2) drawn on planar images. SVM and RF classifiers were finally used to obtain the correct cut-off value.

Results

SVM and RF produced excellent classification performances: SVM classifier achieved perfect classification and RF also attained very good accuracy. The better cut-off for H/M value was 1.55 since it remains the same for both ROIt1 and ROIt2. This value allowed to correctly classify PD from P and ET: patients with H/M ratio less than 1.55 were classified as PD while those with values higher than 1.55 were considered as affected by parkinsonism and/or ET. No difference was found when early or late H/M ratio were considered separately thus suggesting that a single early evaluation could be sufficient to obtain the final diagnosis.

Conclusions

Our results evidenced that the use of SVM and CT permitted to define the better cut-off value for H/M ratios both in early and in delayed phase thus underlining the role of [123I]MIBG cardiac scintigraphy and the effectiveness of H/M ratio in differentiating PD from other parkinsonism or ET. Moreover, early scans alone could be used for a reliable diagnosis since no difference was found between early and late. Definitely, a larger series of cases is needed to confirm this data.

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References

  1. Berardelli A, Wenning GK, Antonini A, Berg D, Bloem BR, Bonifati V, Brooks D, Burn DJ, Colosimo C, Fanciulli A, Ferreira J, Gasser T, Grandas F, Kanovsky P, Kostic V, Kulisevsky J, Oertel W, Poewe W, Reese JP, Relja M, Ruzicka E, Schrag A, Seppi K, Taba P, Vidailhet M (2013) EFNS/MDS-ES recommendations for the diagnosis of Parkinson’s disease. Eur J Neurol 20:16–34

    CAS  PubMed  Google Scholar 

  2. Postuma RB, Poewe W, Litvan I, Lewis S, Lang AE, Halliday G, Goetz CG, Chan P, Slow E, Seppi K, Schaffer E, Rios-Romenets S, Mi T, Maetzler C, Li Y, Heim B, Bledsoe IO, Berg D (2018) Validation of the MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord 33(10):1601–1608

    CAS  PubMed  Google Scholar 

  3. Hustad E, Skogholt AH, Hveem K, Aasly JO (2018) The accuracy of the clinical diagnosis of Parkinson disease. The HUNT study. J Neurol 265:2120–2124. https://doi.org/10.1007/s00415-018-8969-6

    Article  PubMed  PubMed Central  Google Scholar 

  4. Rizzo G, Copetti M, Arcuti S, Martino D, Fontana A, Logroscino G (2016) Accuracy of clinical diagnosis of Parkinson’s disease. A systematic review and meta-analysis. Neurology 86:566–576

    PubMed  Google Scholar 

  5. Adler CH, Beach TG, Hentz JG, Shill HA, Caviness JN, Driver-Dunckley E, Sabbagh MN, Sue LI, Jacobson SA, Belden CM, Dugger BN (2014) Low clinical diagnostic accuracy of early vs advanced Parkinson disease: clinicopathologic study. Neurology 83:406–412

    PubMed  PubMed Central  Google Scholar 

  6. Shimizu S, Hirao K, Kanetaka H, Namioka N, Hatanaka H, Hirose D, Fukasawa R, Umahara T, Sakurai H, Hanyu H (2016) Utility of the combination of DAT SPECT and MIBG myocardial scintigraphy in differentiating dementia with Lewy bodies from Alzheimer’s disease. Eur J Nucl Med Mol Imaging 43:184–192

    CAS  PubMed  Google Scholar 

  7. Pahwa R, Lyons KE (2010) Early diagnosis of Parkinson’s disease: recommendations from diagnostic clinical guidelines. Am J Manag Care 16(Suppl):S94–S99

    PubMed  Google Scholar 

  8. Wenning GK, Geser F, Krismer F, Seppi K, Duerr S, Boesch S, Köllensperger M, Goebel G, Pfeiffer KP, Barone P, Pellecchia MT, Quinn NP, Koukouni V, Fowler CJ, Schrag A, Mathias CJ, Giladi N, Gurevich T, Dupont E, Ostergaard K, Nilsson CF, Widner H, Oertel W, Eggert KM, Albanese A, del Sorbo F, Tolosa E, Cardozo A, Deuschl G, Hellriegel H, Klockgether T, Dodel R, Sampaio C, Coelho M, Djaldetti R, Melamed E, Gasser T, Kamm C, Meco G, Colosimo C, Rascol O, Meissner WG, Tison F, Poewe W (2013) European multiple system atrophy study group. The natural history of multiple system atrophy: a prospective European cohort study. Lancet Neurol 12:264–274

    PubMed  PubMed Central  Google Scholar 

  9. Wijemanne S, Jankovic J (2015) Dopa-responsive dystonia: clinical and genetic heterogeneity. Nat Rev Neurol 11:414–424

    CAS  PubMed  Google Scholar 

  10. Galpern WR, Corrigan-Curay J, Lang AE (2012) Sham neurosurgical procedures in clinical trials for neurodegenerative diseases: scientific and ethical considerations. Lancet Neurol 11:643–650

    PubMed  Google Scholar 

  11. Wager TD, Atlas LY (2015) The neuroscience of placebo effects: connecting context, learning and health. Nat Rev Neurosci 16:403–418

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Kalia LV, Lang AE (2015) Parkinson’s disease. Lancet 386:896–912

    CAS  PubMed  Google Scholar 

  13. Contrafatto D, Mostile G, Nicoletti A, Dibilio V, Raciti L, Lanzafame S, Luca A, Distefano A, Zappia M (2012) [123I]FP-CIT-SPECT asymmetry index to differentiate Parkinson’s disease from vascular parkinsonism. Acta Neurol Scand 126:12–16

    CAS  PubMed  Google Scholar 

  14. Kagi G, Bhatia KP, Tolosa E (2010) The role of DAT-SPECT in movement disorders. J Neurol Neurosurg Psychiatry 81:5–12

    CAS  PubMed  Google Scholar 

  15. Zijlmans JC (2010) The role of imaging in the diagnosis of vascular parkinsonism. Neuroimaging Clin N Am 20:69–76

    PubMed  Google Scholar 

  16. Palumbo B, Fravolini ML, Nuvoli S, Spanu A, Paulus KS, Schillaci O, Madeddu G (2010) Comparison of two neural network classifiers in the differential diagnosis of essential tremor and Parkinson’s disease by 123I-FP-CIT brain SPECT. Eur J Nucl Med Mol Imaging 37:2146–2153

    PubMed  Google Scholar 

  17. Tolosa E, Borght TV, Moreno E (2007) Accuracy of DaTSCAN (123IIoflupane) SPECT in diagnosis of patients with clinically uncertain parkinsonism: 2-year follow-up of an open-label study. Mov Disord 22:2346–2351

    PubMed  Google Scholar 

  18. Suwijn SR, van Boheemen CJ, de Haan RJ et al (2015) The diagnostic accuracy of dopamine transporter SPECT imaging to detect nigrostriatal cell loss in patients with Parkinson’s disease or clinically uncertain parkinsonism: a systematic review. EJNMMI Res 5:1–8

    CAS  Google Scholar 

  19. Gupta D, Kuruvilla A (2011) Vascular parkinsonism: what makes it different? Postgrad Med J 87:829–836

    PubMed  Google Scholar 

  20. Vallabhajosula S, Nikolopoulou A (2011) Radioiodinated metaiodobenzylguanidine (MIBG): radiochemistry, biology, and pharmacology. Semin Nucl Med 41:324–333

    PubMed  Google Scholar 

  21. Yamashina S, Yamazaki J (2007) Neuronal imaging using SPECT. Eur J Nucl Med Mol Imaging 34:939–950

    PubMed  Google Scholar 

  22. Nuvoli S, Spanu A, Piras MR, Nieddu A, Mulas A, Rocchitta G, Galleri G, Serra PA, Madeddu G (2017) 123I-ioflupane brain SPECT and 123I-MIBG cardiac planar scintigraphy combined use in uncertain parkinsonian disorders. Medicine (Baltimore) 96:e6967

    CAS  Google Scholar 

  23. Flotats A, Carrió I, Agostini D, le Guludec D, Marcassa C, Schäfers M, Somsen GA, Unlu M, Verberne HJ, EANM Cardiovascular Committee, European Council of Nuclear Cardiology (2010) EANM Cardiovascular Committee; European Council of Nuclear Cardiology. Proposal for standardization of 123I-metaiodobenzylguanidine (MIBG) cardiac sympathetic imaging by the EANM Cardiovascular Committee and the European Council of Nuclear Cardiology. Eur J Nucl Med Mol Imaging 37:1802–1812

    PubMed  Google Scholar 

  24. Martins da Silva MI, Vidigal Ferreira MJ, Morão Moreirac AP (2013) Iodine-123-metaiodobenzylguanidine scintigraphy in risk stratification of sudden death in heart failure. Rev Port Cardiol 32:509–516

    PubMed  Google Scholar 

  25. Verberne HJ, Brewster LM, Somsen GA, van Eck-Smit BLF (2008) Prognostic value of myocardial 123Imetaiodobenzylguanidine (MIBG) parameters in patients with heart failure: a systematic review. Eur Heart J 29:1147–1159

    PubMed  Google Scholar 

  26. Oka H, Toyoda C, Yogo M, Mochio S (2011) Reduced cardiac 123I-MIBG uptake reflects cardiac sympathetic dysfunction in de novo Parkinson’s disease. J Neural Transm 118:1323–1327

    PubMed  Google Scholar 

  27. Orimo S, Suzuki M, Inaba A, Mizusawa H (2012) 123I-MIBG myocardial scintigraphy for differentiating Parkinson’s disease from other neurodegenerative parkinsonism: a systematic review and meta-analysis. Parkinsonism Relat Disord 18:494–500

    PubMed  Google Scholar 

  28. Shin DH, Lee PH, Bang OY, Joo IS, Huh K (2006) Clinical implications of cardiac-MIBG SPECT in the differentiation of parkinsonian syndromes. J Clin Neurol 2:51–57

    PubMed  PubMed Central  Google Scholar 

  29. Kim JS, Lee PH, Lee KS, Park JW, Kim YI, Chung YA, Kim SH, Kim SH, Kim J, Choi YY, Kim HT (2006) Cardiac [123I]metaiodobenzylguanidine scintigraphy for vascular parkinsonism. Mov Disord 21:1990–1994

    PubMed  Google Scholar 

  30. Kalra S, Grosset DG, Benamer HTS (2010) Differentiating vascular parkinsonism from idiopathic Parkinson’s disease: a systematic review. Mov Disord 25:149–156

    PubMed  Google Scholar 

  31. Miyamoto T, Miyamoto M, Suzuki K, Nishibayashi M, Iwanami M, Hirata K (2008) 123I-MIBG cardiac scintigraphy provides clues to the underlying neurodegenerative disorder in idiopathic REM sleep behavior disorder. Sleep 31:717–723

    PubMed  PubMed Central  Google Scholar 

  32. Kane JPM, Roberts G, Petrides GS, Lloyd JJ, O’Brien JT, Thomas AJ (2019) 123I-MIBG scintigraphy utility and cut-off value in a clinically representative dementia cohort. Parkinsonism Relat Disord 26:79–84. https://doi.org/10.1016/j.parkreldis.2019.01.024

    Article  Google Scholar 

  33. Deuschl G, Bain P, Brin M (1998) Consensus statement of the Movement Disorder Society on tremor. Ad hoc Scientific Committee. Mov Disord 13(Suppl 3):2–23

    PubMed  Google Scholar 

  34. Hoehn M, Yahr MD (1967) Parkinsonism: onset, progression, and mortality. Neurology 17:427–442

    CAS  PubMed  Google Scholar 

  35. Takatsu H, Nishida H, Matsuo H et al (2000) Cardiac sympathetic denervation from the early stage of Parkinson’s disease: clinical and experimental studies with radiolabeled MIBG. J Nucl Med 41:71–77

    CAS  PubMed  Google Scholar 

  36. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27 Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

    Google Scholar 

  37. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297

    Google Scholar 

  38. Shawe-Taylor J, Bartlett PL (1998) Structural risk minimization over data-dependent hierarchies. IEEE Trans Inf Theory 44:1926–1940

    Google Scholar 

  39. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, springer series in statistics. Springer New York Inc., New York

    Google Scholar 

  40. Criminisi A, Shotton J, Konukoglu E (2012) Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends in Computer Graphics and Vision 7:81–227

    Google Scholar 

  41. Nuvoli S, Palumbo B, Malaspina S, Madeddu G, Spanu A (2018) 123I-ioflupane SPET and 123I-MIBG in the diagnosis of Parkinson’s disease and parkinsonian disorders and in the differential diagnosis between Alzheimer’s and Lewy’s bodies dementias. Hell J Nucl Med 21:60–68

    PubMed  Google Scholar 

  42. Amit Y, Geman D (1997) Shape quantization and recognition with randomized trees. Neural Comput 9:1545–1588

    Google Scholar 

  43. Breiman L, Friedman JH, Olshen RA et al (1984) Classification and regression trees. Chapman and Hall/CRC

  44. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Google Scholar 

  45. Knudsen K, Borghammer P (2018) Imaging the autonomic nervous system in Parkinson’s disease. Curr Neurol Neurosci Rep 18:79

    PubMed  Google Scholar 

  46. Okada Y, Shiraishi M, Nakamura H, Maki F, Sasaki N, Hasegawa Y, Sasaki O, Nakashima Y (2018) Usefulness of the combination of iodine-123-metaiodobenzylguanidine scintigraphy and iodine-123-ioflupane scintigraphy in new-onset Parkinson’s disease. Nucl Med Commun 39:983–988

    PubMed  Google Scholar 

  47. Yoshita M (1998) Differentiation of idiopathic Parkinson’s disease from striatonigral degeneration and progressive supranuclear palsy using iodine-123 meta-iodobenzylguanidine myocardial scintigraphy. J Neurol Sci 155:60–67

    CAS  PubMed  Google Scholar 

  48. Südmeyer M, Antke C, Zizek T et al (2011) Diagnostic accuracy of combined FP-CIT, IBZM, and MIBG scintigraphy in the differential diagnosis of degenerative parkinsonism: a multidimensional statistical approach. J Nucl Med 52:733–740

    PubMed  Google Scholar 

  49. Sakamoto F, Shiraishi S, Tsuda N et al (2016) 123I-MIBG myocardial scintigraphy for the evaluation of Lewy body disease: are delayed images essential? Is visual assessment useful? Br J Radiol 10:20160144

    Google Scholar 

  50. Bianconi F, Fravolini ML, Bello-Cerezo R, Minestrini M, Scialpi M, Palumbo B (2018) Evaluation of shape and textural features from CT as prognostic biomarkers in non-small cell lung cancer. Anticancer Res 38:2155–2160

    PubMed  Google Scholar 

  51. Palumbo B, Fravolini ML, Buresta T, Pompili F, Forini N, Nigro P, Calabresi P, Tambasco N (2014) Diagnostic accuracy of Parkinson disease by support vector machine (SVM) analysis of 123I-FP-CIT brain SPECT data: implications of putaminal findings and age. Medicine (Baltimore) 93:e228. https://doi.org/10.1097/MD.0000000000000228

    Article  Google Scholar 

  52. Towey DJ, Bain PG, Nijran KS (2011) Automatic classification of 123I-FP-CIT (DaTSCAN) SPECT images. Nucl Med Commun 32:699–707

    PubMed  Google Scholar 

  53. Taylor JC, Fenner JW (2017) Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification? EJNMMI Phys 4:29. https://doi.org/10.1186/s40658-017-0196-1

    Article  PubMed  PubMed Central  Google Scholar 

  54. Castillo-Barnes D, Ramírez J, Segovia F et al (2018) Robust ensemble classification methodology for I123-Ioflupane SPECT images and multiple heterogeneous biomarkers in the diagnosis of Parkinson's disease. Front Neuroinform 12:53. https://doi.org/10.3389/fninf.2018.00053 eCollection 2018

    Article  PubMed  PubMed Central  Google Scholar 

  55. Goldstein DS, Holmes C, Kopin IJ, Sharabi Y (2011) Intra-neuronal vesicular uptake of catecholamines is decreased in patients with Lewy body diseases. J Clin Invest 121:3320–3330

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Cascianelli S, Scialpi M, Amici S, Forini N, Minestrini M, Fravolini M, Sinzinger H, Schillaci O, Palumbo B (2017) Role of artificial intelligence techniques (automatic classifiers) in molecular imaging modalities in neurodegenerative diseases. Curr Alzheimer Res 14:198–207

    CAS  PubMed  Google Scholar 

  57. Gray KR, Aljabar P, Heckemann RA, Hammers A, Rueckert D, Alzheimer’s Disease Neuroimaging Initiative (2013) Alzheimer’s disease neuroimaging initiative. Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease. Neuroimage 65:167–175

    PubMed  Google Scholar 

  58. Segovia F, Górriz JM, Ramírez J et al (2017) Preprocessing of 18F-DMFP-PET data based on hidden Markov random fields and the Gaussian distribution. Front Aging Neurosci 9:326. https://doi.org/10.3389/fnagi.2017.00326 eCollection 2017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Segovia F, Illán IA, Górriz JM et al (2015) Distinguishing Parkinson’s disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks. Front Comput Neurosci 9:137. https://doi.org/10.3389/fncom.2015.00137 eCollection 2015

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Unit of Nuclear Medicine, Department of Medicine, Surgical and Experimental Science, University of Sassari, Viale San Pietro 8, 07100, Sassari, Italy

    Susanna Nuvoli, Angela Spanu & Giuseppe Madeddu

  2. Department of Engineering, University of Perugia, Perugia, Italy

    Mario Luca Fravolini, Francesco Bianconi & Silvia Cascianelli

  3. Section of Nuclear Medicine and Health Physics, Department of Surgical and Biomedical Sciences, University of Perugia, Perugia, Italy

    Barbara Palumbo

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  1. Susanna Nuvoli
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  2. Angela Spanu
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  3. Mario Luca Fravolini
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  4. Francesco Bianconi
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Correspondence to Susanna Nuvoli.

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Nuvoli, S., Spanu, A., Fravolini, M.L. et al. [123I]Metaiodobenzylguanidine (MIBG) Cardiac Scintigraphy and Automated Classification Techniques in Parkinsonian Disorders. Mol Imaging Biol 22, 703–710 (2020). https://doi.org/10.1007/s11307-019-01406-6

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