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Updated: Jul 16, 2026

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Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery
Published on: July 5, 2021
Clinico-pathological study in large posterior fossa midline tumors
S Sarkar1, M A Hossain, U Mazumder
1Department of Neurosurgery, Mymensingh Medical College, Mymensingh, Bangladesh. saumitra_s2001@yahoo.com
Mymensingh Medical Journal : MMJ
|March 9, 2007
Summary
This study on posterior fossa tumors found medulloblastoma to be the most common type. Magnetic Resonance Imaging (MRI) showed higher accuracy than non-contrast CT (NECT) for diagnosing these brain tumors.
Area of Science:
- Neurosurgery
- Radiology
- Oncology
Background:
- Posterior fossa tumors are a significant cause of morbidity and mortality in neurosurgery patients.
- Accurate preoperative diagnosis is crucial for effective treatment planning and patient outcomes.
Purpose of the Study:
- To analyze the clinical and radiological features of posterior fossa tumors.
- To evaluate the diagnostic performance of non-contrast computed tomography (NECT) and magnetic resonance imaging (MRI) in identifying these tumors.
Main Methods:
- A cross-sectional study was conducted on 50 admitted neurosurgery patients (age 2.5-70 years) between July 2002 and December 2004.
- Tumor characteristics including size, density, enhancement patterns, and calcification were assessed using NECT and MRI.
- Histopathological analysis confirmed the tumor types.
Main Results:
- The mean tumor size was 4.38 cm. Medulloblastoma was the most frequent histopathological diagnosis (32%).
- NECT showed 62% of midline tumors as mixed density. Calcification was observed in 14% of cases, predominantly in ependymomas.
- MRI demonstrated superior diagnostic accuracy (91.30%) compared to NECT (84.78%). Both modalities achieved 100% sensitivity.
Conclusions:
- Medulloblastoma is a common posterior fossa tumor, and imaging features like calcification can suggest ependymoma.
- MRI is a more accurate diagnostic tool than NECT for posterior fossa tumors, offering better diagnostic accuracy and positive predictive value.

