K.O. Tutsenko1, A.N. Narkevich2, V.G. Abramov3

1,2 FSBEI HE Prof. V.F. Voino-Yasenetsky KrasSMU MOH (Krasnoyarsk, Russia)

3 Center for Clinical Research at the Federal Medical-Biological Agence (Krasnoyarsk, Russia)

Parkinson's disease is a progressive neurodegenerative disease. Symptoms appear when approximately 70% of the dopamine-producing neurons have died. In this case, therapy becomes ineffective, treatment is symptomatic, and patients with Parkinson's disease need early diagnosis. An important task is the differential diagnosis of Parkinson's disease and essential tremor. In essential tremor, there is no dopaminergic deficit. Difficulties in diagnosis are associated with similar symptoms of these two pathologies. The degree of damage to the dopaminergic system in this pathology can be assessed using positron emission tomography with the radiopharmaceutical 18F-DOPA. This method makes it possible to determine the activity of a radiopharmaceutical in various parts of the brain, which is used both for the diagnosis of Parkinson's disease and for the differential diagnosis of Parkinson's disease and essential tremor.

Having a large number of parameters, there is a difficulty in interpreting the results and making a diagnosis. In this case, it is necessary to reduce the dimension of the data to exclude non-informative indicators. There are many methods used for this purpose. In this study, the most diagnostically valuable indicators were selected using ROC analysis and the calculation of information content using the cumulative frequency method and the Shannon method.

The purpose of the study was to reduce the number of studied indicators of the activity of the radiopharmaceutical to increase the accuracy of diagnosing Parkinson's disease using positron emission tomography with the radiopharmaceutical 18F-DOPA.

In this study, positron emission tomography was performed on healthy individuals, patients with Parkinson's disease, and patients with essential tremor. The diagnostic value of 40 indicators of 18F-DOPA activity in various parts of the brain was analyzed. The quality of the binary classification was assessed using ROC analysis, the information content of the indicators was calculated by the cumulative frequency method and the Shannon method.

Relative indicators showed greater diagnostic value compared to absolute ones, the most informative are the relative indicators of SOR and SCR at a significance level of p < 0.001.

The results of this work will help to increase the accuracy of the diagnosis of Parkinson's disease by excluding non-informative signs from the analysis.

Tutsenko K.O., Narkevich A.N., Abramov V.G. Determination of diagnostically valuable indicators of 18F-DOPA activity to increase the accuracy of diagnostics of Parkinson's disease. Technologies of Living Systems. 2022. V. 19. № 3. Р. 37–44. DOI: https://doi.org/ 10.18127/j20700997-202203-04 (In Russian)

1. Bykova V.V., Katayeva A.V. Metody i sredstva analiza informativnosti priznakov pri obrabotke meditsinskikh dannykh. Programmnyye produkty i sistemy. 2016. T. 2(114). S. 172–178. (in Russian).
2. Emelianova Yu.A. Otsenka informativnosti mnogomernykh dannykh. Molodezh i sovremennyye informatsionnyye tekhnologii. Sb. trudov Mezhdunarodnoy nauchno-prakticheskoy konferentsii studentov. aspirantov i molodykh uchenykh. 2018. № 1. S. 356–357. (in Russian).
3. Selikhova M.V., Katunina E.A., Voun A. Pozitronnaya emissionnaya i odnofotonnaya emissionnaya kompyuternaya tomografiya v otsenke sostoyaniya monoaminergicheskikh sistem mozga pri ekstrapiramidnykh rasstroystvakh. Annaly klinicheskoy i eksperimentalnoy nevrologii. 2019. T. 13(2). S. 69–78. (in Russian).
4. Sushkova O.S., Morozov A.A., Gabova A.V. Razrabotka metoda ranney i differentsialnoy diagnostiki bolezni Parkinsona i essentsialnogo tremora s pomoshchyu analiza v spleskoobraznoy aktivnosti myshts. Informatsionnyye tekhnologii i nanotekhnologii. 2020. № 1. S. 170–178. (in Russian).
5. Titova N.V., Chauduri K.R. Nemotornyye simptomy bolezni Parkinsona: podvodnaya chast aysberga. Annaly klinicheskoy i eksperimentalnoy nevrologii. 2017. T. 11(4). S. 5–18. (in Russian).
6. Ball N., Teo W.P., Chandra S. Parkinson's disease and the environment. Frontiers in neurology. 2019. V. 10. P. 218–225.
7. Jakubowski J.L., Labrie V. Epigenetic biomarkers for Parkinson’s disease: from diagnostics to therapeutics. Journal of Parkinson's disease. 2017. V. 7(1). P. 1–12.
8. Pringsheim T., Jette N., Frolkis A. The prevalence of Parkinson's disease: a systematic review and meta‐analysis. Movement disorders. 2014. V. 29(13). P. 1583–1590.
9. Scorza F.A, do Carmo A.C., Fiorini A.C. Sudden unexpected death in Parkinson's disease (SUDPAR): a review of publications since the decade of the brain. Clinics. 2017. V. 72. P. 649–651.
10. Willis A.W., Evanoff B.A., Lian M. Geographic and ethnic variation in Parkinson disease: a population-based study of US Medicare beneficiaries. Neuroepidemiology. 2010. V. 34(3). P. 143–151.
11. Yang F., Johansson A.L., Pedersen N.L. Socioeconomic status in relation to Parkinson's disease risk and mortality: A population-based prospective study. Medicine. 2016. V. 95(30). P. 37–43.