Conventional MRI imaging

 MR, with its excellent soft tissue contrast, is the modality of choice for the characterization of brain lesions. It is the most sensitive imaging modality in detecting a mass, in defining the location and apparent size, and in interrogating the integrity of the blood–brain barrier with an exogenous contrast agent. In most medical centers neurosurgeons and neuro-oncologists make their management and therapy planning decisions based on conventional MR images.
In 1990, it was reported that neuroradiologists successfully predicted tumor grade on a three-tiered grading system about 80% of the time using the gestalt approach. In 36 gliomas, the study found that positive MR predictors were mass effect (P = 0.0000) and cyst formation or necrosis (P = 0.0512) and MR accuracy rate approached that of neuropathologic diagnosis, which is subjected to sampling errors. Perilesional edema, signal heterogeneity, hemorrhage, border definition, and crossing of the midline were among other MR features which were not found to be significant positive predictors. Two major limitations of that study were that the number of patients was very small and that only one subtype of brain tumors (i.e. astrocytomas) was included. In 1995, another study compared diagnostic accuracy of experienced radiologists with two objective and reproducible methods: neural network models and multiple linear regression. Diagnostic accuracy of grading glioma was 61% for the neural network model, 59% for multiple regression, and 57% for radiologist’s experience. All three methods were better than random (50%), but the study showed that the relationship between MRI features and tumor grade is not strong enough to allow sufficiently accurate determination of grade. Other studies have reported an MR imaging accuracy of diagnosing supratentorial neoplasms to vary between 30% and 90%.
At the current state of the art, conventional MR imaging features cannot be used to determine underlying tissue histopathology. The limitations of conventional MRI are well known: it does not allow accurate diagnosis of tumor type; it cannot distinguish infiltrating tumor from vasogenic edema since both appear bright on T2-weighted images; it cannot distinguish infiltrating tumor from functioning brain tissue at tumor borderline; it cannot measure cell density, cell anaplasia, mitotic index, or angiogenesis. As a consequence, it cannot accurately predict individual patient time-to-progression and survival.

What’s New

Inomed ISIS Intraoperative neurophysiological monitoring started to function in all our related surgeries.
Oct /07/2009
The author celebrating 30 years experience in neurosurgery.
Nov /27/2013
Magnetom Skyra 3 tesla with all clinical applications is running and intraoperative MRI monitoring started.
  Copyright [2014] [CNS Clinic-JORDAN]. All rights reserved