Top

European Journal of Nuclear Medicine and Molecular Imaging

Published in:

01-06-2018 | Original Article

Extraction, selection and comparison of features for an effective automated computer-aided diagnosis of Parkinson’s disease based on [¹²³I]FP-CIT SPECT images

Authors: Francisco P. M. Oliveira, Diogo Borges Faria, Durval C. Costa, Miguel Castelo-Branco, João Manuel R. S. Tavares

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 6/2018

Abstract

Purpose

This work aimed to assess the potential of a set of features extracted from [¹²³I]FP-CIT SPECT brain images to be used in the computer-aided “in vivo” confirmation of dopaminergic degeneration and therefore to assist clinical decision to diagnose Parkinson’s disease.

Methods

Seven features were computed from each brain hemisphere: five standard features related to uptake ratios on the striatum and two features related to the estimated volume and length of the striatal region with normal uptake. The features were tested on a dataset of 652 [¹²³I]FP-CIT SPECT brain images from the Parkinson’s Progression Markers Initiative. The discrimination capacities of each feature individually and groups of features were assessed using three different machine learning techniques: support vector machines (SVM), k-nearest neighbors and logistic regression.

Results

Cross-validation results based on SVM have shown that, individually, the features that generated the highest accuracies were the length of the striatal region (96.5%), the putaminal binding potential (95.4%) and the striatal binding potential (93.9%) with no statistically significant differences among them. The highest classification accuracy was obtained using all features simultaneously (accuracy 97.9%, sensitivity 98% and specificity 97.6%). Generally, slightly better results were obtained using the SVM with no statistically significant difference to the other classifiers for most of the features.

Conclusions

The length of the striatal region uptake is clinically useful and highly valuable to confirm dopaminergic degeneration “in vivo” as an aid to the diagnosis of Parkinson’s disease. It compares fairly well to the standard uptake ratio-based features, reaching, at least, similar accuracies and is easier to obtain automatically. Thus, we propose its day to day clinical use, jointly with the uptake ratio-based features, in the computer-aided diagnosis of dopaminergic degeneration in Parkinson’s disease.

Tolosa E, Wenning G, Poewe W. The diagnosis of Parkinson’s disease. Lancet Neurol. 2006;5:75–86. https://doi.org/10.1016/S1474-4422(05)70285-4.CrossRefPubMed

Varrone A, Halldin C. New developments of dopaminergic imaging in Parkinson’s disease. Q J Nucl Med Mol Imaging. 2012;56:68–82.PubMed

O’Brien JT, Colloby S, Fenwick J, Williams ED, Firbank M, Burn D, et al. Dopamine transporter loss visualized with FP-CIT SPECT in the differential diagnosis of dementia with Lewy bodies. Arch Neurol. 2004;61:919–25. https://doi.org/10.1001/archneur.61.6.919.CrossRefPubMed

Kasanuki K, Iseki E, Ota K, Kondo D, Ichimiya Y, Sato K, et al. ¹²³I-FP-CIT SPECT findings and its clinical relevance in prodromal dementia with Lewy bodies. Eur J Nucl Med Mol Imaging. 2017;44:358–65. https://doi.org/10.1007/s00259-016-3466-6.CrossRefPubMed

Cuberas-Borrós G, Lorenzo-Bosquet C, Aguadé-Bruix S, Hernández-Vara J, Pifarré-Montaner P, Miquel F, et al. Quantitative evaluation of striatal I-123-FP-CIT uptake in essential tremor and parkinsonism. Clin Nucl Med. 2011;36:991–6. https://doi.org/10.1097/RLU.0b013e3182291a7b.CrossRefPubMed

Badiavas K, Molyvda E, Iakovou I, Tsolaki M, Psarrakos K, Karatzas N. SPECT imaging evaluation in movement disorders: far beyond visual assessment. Eur J Nucl Med Mol Imaging. 2011;38:764–73. https://doi.org/10.1007/s00259-010-1664-1.CrossRefPubMed

Mirzaei S, Zakavi R, Rodrigues M, Schwarzgruber T, Brücke T, Bakala J, et al. Fully automated 3D basal ganglia activity measurement in dopamine transporter scintigraphy (Spectalyzer). Ann Nucl Med. 2010;24:295–300. https://doi.org/10.1007/s12149-010-0353-2.CrossRefPubMed

Habraken JBA, Booij J, Slomka P, Sokole EB, van Royen EA. Quantification and visualization of defects of the functional dopaminergic system using an automatic algorithm. J Nucl Med. 1999;40:1091–7.PubMed

Jensen PS, Ziebell M, Skouboe G, Khalid U, Rd N, Thomsen G, et al. Validation of a method for accurate and highly reproducible quantification of brain dopamine transporter SPECT studies. J Nucl Med Technol. 2011;39:271–8. https://doi.org/10.2967/jnmt.111.090324.CrossRefPubMed

10.

Calvini P, Rodriguez G, Inguglia F, Mignone A, Guerra UP, Nobili F. The basal ganglia matching tools package for striatal uptake semi-quantification: description and validation. Eur J Nucl Med Mol Imaging. 2007;34:1240–53. https://doi.org/10.1007/s00259-006-0357-2.CrossRefPubMed

11.

Zubal IG, Early M, Yuan O, Jennings D, Marek K, Seibyl JP. Optimized, automated striatal uptake analysis applied to SPECT brain scans of Parkinson’s disease patients. J Nucl Med. 2007;48:857–64. https://doi.org/10.2967/jnumed.106.037432.CrossRefPubMed

12.

Koch W, Radau PE, Hamann C, Tatsch K. Clinical testing of an optimized software solution for an automated, observer-independent evaluation of dopamine transporter SPECT studies. J Nucl Med. 2005;46:1109–18.PubMed

13.

Morton RJ, Guy MJ, Clauss R, Hinton PJ, Marshall CA, Clarke EA. Comparison of different methods of DatSCAN quantification. Nucl Med Commun. 2005;26:1139–46.CrossRefPubMed

14.

Prashanth R, Roy SD, Mandal PK, Ghosh S. Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging. Expert Syst Appl. 2014;41:3333–42. https://doi.org/10.1016/j.eswa.2013.11.031.CrossRef

15.

Marek K, Jennings D, Lasch S, Siderowf A, Tanner C, Simuni T, et al. The Parkinson progression marker initiative (PPMI). Prog Neurobiol. 2011;95:629–35. https://doi.org/10.1016/j.pneurobio.2011.09.005.CrossRef

16.

Oliveira FPM, Tavares JMRS. Medical image registration: a review. Comput Method Biomech Biomed Engin. 2014;17:73–93. https://doi.org/10.1080/10255842.2012.670855.CrossRef

17.

Oliveira FPM, Faria DB, Costa DC, Tavares JMRS. A robust computational solution for automated quantification of a specific binding ratio based on [¹²³I]FP-CIT SPECT images. Q J Nucl Med Mol Imaging. 2014;58:74–84.PubMed

18.

Ibáñez L, Schroeder W, Ng L, Cates J, Consortium IS. The ITK software guide. Kitware, 2005. http://www.itk.org/.

19.

Tossici-Bolt L, Hoffmann SMA, Kemp PM, Mehta RL, Fleming JS. Quantification of [¹²³I]FP-CIT SPECT brain images: an accurate technique for measurement of the specific binding ratio. Eur J Nucl Med Mol Imaging. 2006;33:1491–9. https://doi.org/10.1007/s00259-006-0155-x.CrossRefPubMed

20.

Innis RB, Cunningham VJ, Delforge J, Fujita M, Gjedde A, Gunn RN, et al. Consensus nomenclature for in vivo imaging of reversibly binding radioligands. J Cerebr Blood Flow Metab. 2007;27:1533–9. https://doi.org/10.1038/sj.jcbfm.9600493.CrossRef

21.

Schroeder W, Martin K, Lorensen B. Visualization toolkit: An object-oriented approach to 3D graphics. 4th ed. Clifton Park: Kitwarel; 2006.

22.

Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2011;2:1–27. https://doi.org/10.1145/1961189.1961199.CrossRef

23.

Nobili F, Naseri M, Carli FD, Asenbaum S, Booij J, Darcourt J, et al. Automatic semi-quantification of [¹²³I]FP-CIT SPECT scans in healthy volunteers using BasGan version 2: results from the ENC-DAT database. Eur J Nucl Med Mol Imaging. 2013;40:565–73. https://doi.org/10.1007/s00259-012-2304-8.CrossRefPubMed

24.

Varrone A, Dickson JC, Tossici-Bolt L, Sera T, Asenbaum S, Booij J, et al. European multicentre database of healthy controls for [123I]FP-CIT SPECT (ENC-DAT): age-related effects, gender differences and evaluation of different methods of analysis. Eur J Nucl Med Mol Imaging. 2013;40:213–27. https://doi.org/10.1007/s00259-012-2276-8.CrossRefPubMed

25.

Illán IA, Górriz JM, Ramírez J, Segovia F, Jiménez-Hoyuela JM, Lozano SJO. Automatic assistance to Parkinson’s disease diagnosis in DaTSCAN SPECT imaging. Med Phys. 2012;39:5971–80. https://doi.org/10.1118/1.4742055.CrossRefPubMed

26.

Oliveira FPM, Castelo-Branco M. Computer-aided diagnosis of Parkinson’s disease based on [¹²³I]FP-CIT SPECT binding potential images, using the voxels-as-features approach and support vector machines. J Neural Eng. 2015;12:026008. https://doi.org/10.1088/1741-2560/12/2/026008.CrossRefPubMed

Title: Extraction, selection and comparison of features for an effective automated computer-aided diagnosis of Parkinson’s disease based on [123I]FP-CIT SPECT images
Authors: Francisco P. M. Oliveira
Diogo Borges Faria
Durval C. Costa
Miguel Castelo-Branco
João Manuel R. S. Tavares
Publication date: 01-06-2018
Publisher: Springer Berlin Heidelberg
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 6/2018
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-017-3918-7

At a glance: The STEP trials

Springer Medicine

Extraction, selection and comparison of features for an effective automated computer-aided diagnosis of Parkinson’s disease based on [¹²³I]FP-CIT SPECT images

Abstract

Purpose

Methods

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 6/2018

11C–acetate PET/CT: a potential alternative of transcranial biopsy for grading cerebral gliomas

Ultra-early response assessment in lymphoma treatment: [18F]FDG PET/MR captures changes in glucose metabolism and cell density within the first 72 hours of treatment

Correction to: Biological tumour volumes of gliomas in early and standard 20–40 min 18F-FET PET images differ according to IDH mutation status

Cancer metastasizes to the bone marrow and not to the bone: time for a paradigm shift!

Is it time to change our vision of tumor metabolism prior to immunotherapy?

The roles of 11C-acetate PET/CT in predicting tumor differentiation and survival in patients with cerebral glioma