title: New Software for Preoperative Diagnostics of Meningeal Tumor Histologic Types.
abstract: OBJECTIVE
  Meningeal tumors are neoplasms with different histologic manifestations of both benign and malignant types that determine the prognosis of tumor recurrence and its consistency. The risk of surgical treatment depends on the location, size, and consistency of the tumor. Magnetic resonance imaging (MRI) sequences can be used to identify the features of tumors, but these MRI characteristics are not well understood. The present study describes an advanced mathematical algorithm to analyze MRI data and distinguish histologic types of meningeal tumors before surgery.
  
  
  METHODS
  Forty-eight patients underwent surgical removal of meningeal brain tumor. All patients had preoperative MRI with a 1.5-T scanner. One radiologist and 2 neurosurgeons evaluated MRI histogram peaks of the whole tumor volume using the advanced computer algorithm.
  
  
  RESULTS
  Three specialists received the following mean value of histogram peaks: 15.99 ± 0.23 (± standard error of the mean [SEM]) for meningoteliomatous meningiomas; 21.24 ± 0.3 (±SEM) for fibroplastic meningiomas; 19.0 ± 0.28 (±SEM) for transitional meningiomas; 10.7 ± 0.27 (±SEM) for anatypical, anaplastic meningiomas, 11.03 ± 0.51 (±SEM) for primary intracranial fibrosarcomas and 25.72 ± 0.29 (±SEM) for meningeal hemangiopericytomas. A one-way analysis of variance test proved the difference between group means: F = 70.138, P < 0.01. The Tukey test and the Games-Howell test indicated that the difference between the tumor groups was significant. Mean deviation in agreement index between specialists was 0.98 ± 0.007 (±SEM).
  
  
  CONCLUSIONS
  The advanced algorithm proved high specificity, sensitivity, and interoperator repeatability.
authors.affiliation: Department of Neurosurgery, Novosibirsk State Medical University, Novosibirsk, Russia. Electronic address: alkr01@yandex.ru.
authors.forename: Alexey L
authors.initials: AL
authors.lastname: Krivoshapkin
authors.affiliation: Department of Neurosurgery, European Medical Center, Moscow, Russia.
authors.forename: Gleb S
authors.initials: GS
authors.lastname: Sergeev
authors.affiliation: Department of Physics, Novosibirsk State University, Novosibirsk, Russia.
authors.forename: Leonid E
authors.initials: LE
authors.lastname: Kalneus
authors.affiliation: Department of Neurosurgery, Novosibirsk State Medical University, Novosibirsk, Russia.
authors.forename: Alekey S
authors.initials: AS
authors.lastname: Gaytan
authors.affiliation: Department of Neurosurgery, Academician E.N. Meshalkin Novosibirsk State Research Institute of Circulation Pathology, Novosibirsk, Russia.
authors.forename: Vladimir I
authors.initials: VI
authors.lastname: Murtazin
authors.affiliation: Department of Radiological and Functional Diagnostics, Academician E.N. Meshalkin Novosibirsk State Research Institute of Circulation Pathology, Novosibirsk, Russia.
authors.forename: Vladislav P
authors.initials: VP
authors.lastname: Kurbatov
authors.affiliation: Laboratory of Experimental Surgery and Morphology, Center of New Technologies, Academician E.N. Meshalkin Novosibirsk State Research Institute of Circulation Pathology, Novosibirsk, Russia.
authors.forename: Alexander M
authors.initials: AM
authors.lastname: Volkov
authors.affiliation: Laboratory of Experimental Surgery and Morphology, Center of New Technologies, Academician E.N. Meshalkin Novosibirsk State Research Institute of Circulation Pathology, Novosibirsk, Russia.
authors.forename: Diana V
authors.initials: DV
authors.lastname: Kostromskaya
authors.affiliation: Department of Neurosurgery, Railway Clinical Hospital, Novosibirsk, Russia.
authors.forename: Sergey M
authors.initials: SM
authors.lastname: Pyatov
authors.affiliation: Department of Radiology, Federal Center of Neurosurgery, Novosibirsk, Russia.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Amelin
authors.affiliation: Department of Neurosurgery, Railway Clinical Hospital, Novosibirsk, Russia.
authors.forename: Abdyrakman R
authors.initials: AR
authors.lastname: Duishobaev
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology / Q000601:surgery; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008577:Meningeal Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology* / Q000601:surgery; D008579:Meningioma / Q000000981:diagnostic imaging* / Q000473:pathology* / Q000601:surgery; D008875:Middle Aged; D015588:Observer Variation; D011300:Preoperative Care / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software
keywords: Histologic type; Meningeal tumors; Software for MRI