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Voronina I.A.

Burdenko Neurosurgical Institute, Moscow, Russia

Voronov V.G.

Algorithm identification of differential diagnostic clinical syndromes in patients with neuroepithelial tumors of the III ventricle

Authors:

Voronina I.A., Voronov V.G.

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Journal: S.S. Korsakov Journal of Neurology and Psychiatry. 2011;111(8): 4‑8

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Table of contents

ALGORITHM IDENTIFICATION OF DIFFERENTIAL DIAGNOSTIC CLINICAL SYNDROMES IN PATIENTS WITH NEUROEPITHELIAL TUMORS OF THE III VENTRICLE
ALGORITHM IDENTIFICATION OF DIFFERENTIAL DIAGNOSTIC CLINICAL SYNDROMES IN PATIENTS WITH NEUROEPITHELIAL TUMORS OF THE III VENTRICLE

To cite this article:

Voronina IA, Voronov VG. Algorithm identification of differential diagnostic clinical syndromes in patients with neuroepithelial tumors of the III ventricle. S.S. Korsakov Journal of Neurology and Psychiatry. 2011;111(8):4‑8. (In Russ.)

Подписка 2026 Журнал неврологии и психиатрии им. С.С. Корсакова (S.S. Korsakov Journal of Neurology and Psychiatry)
МСФ на ЯМ
Использование инфографики авторами научных работ
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Abstract:

An algorithm for identifying differential diagnostic clinical syndromes in neurooncologic patients was suggested. This technique involves: creating a database of cases - lists of symptoms, requests for selection of thematic cases with their subsequent division into the main and control groups; generation of single-symptom applicants; selection and verification of clinical differential diagnostic syndromes appropriate to sensitivity and specificity using a 4-dipole table and selection of the best of them. Based on the database of 368 patients with tumors of basal-diencephalic localization, we tried to perform the algorithm of identification of differential diagnostic clinical syndromes in patients with different anatomical and topographical variations of neuroepithelial tumors of the III ventricle. The identified syndromes are reasonable to use for selecting the optimal treatment strategy.

Keywords:

differential-diagnostic clinical syndromes
neuroepithelial tumor of III ventricle
clinical epidemiology
algorithmic methods of detecting syndromes

Authors:

Voronina I.A.

Burdenko Neurosurgical Institute, Moscow, Russia

Voronov V.G.

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