Assessment of tumor blood flow and its correlation with histopathologic features in skull base meningiomas and schwannomas by using pseudo-continuous arterial spin labeling images.

OBJECTIVE: We aimed to investigate whether pseudo-continuous arterial spin labeling (pcASL)-MRI can adequately evaluate tumor perfusion even if the tumors are located in the skull base region and evaluate the correlation between tumor blood flow (TBF) and the histopathologic features of skull base meningiomas and schwannomas. MATERIALS AND METHODS: We enrolled 31 patients with skull base meningioma (n=14) and schwannoma (n=17) who underwent surgical resection. TBF was calculated from pcASL. Tissue sections were stained with CD34 to evaluate microvessel area (MVA). TBF and MVA ratio were compared between meningiomas and schwannomas using Mann-Whitney U-test. The correlations between MVA ratio and TBF were evaluated in each tumor by using single linear regression analysis and Spearman's rank correlation coefficients (rs). RESULTS: MVA ratio and TBF were significantly higher in meningioma than in schwannoma (both p<0.01). Correlation analyses revealed significant positive correlations between MVA ratio and both mean and max TBF for meningiomas (rs=0.89, 0.81, both p<0.01). There was a weak positive correlation between MVA ratio and mean TBF for schwannomas (rs=0.43, p=0.04). However, no significant correlation was found between MVA ratio and max TBF for schwannoma. CONCLUSIONS: pcASL-MRI is useful for evaluating tumor perfusion even if the tumors are located in the skull base region. Moreover, pcASL-TBF was significantly higher in most meningiomas compared to schwannomas, which can help in the differential diagnosis of the 2 tumor types even without the use of contrast material.

Decision tree induction in the diagnosis of otoneurological diseases.

Expert systems have been applied in medicine as diagnostic aids and education tools. The construction of a knowledge base for an expert system may be a difficult task; to automate this task several machine learning methods have been developed. These methods can be also used in the refinement of knowledge bases for removing inconsistencies and redundancies, and for simplifying decision rules. In this study, decision tree induction was employed to acquire diagnostic knowledge for otoneurological diseases and to extract relevant parameters from the database of an otoneurological expert system ONE. The records of patients with benign positional vertigo, Meniere's disease, sudden deafness, traumatic vertigo, vestibular neuritis and vestibular schwannoma were retrieved from the database of ONE, and for each disease, decision trees were constructed. The study shows that decision tree induction is a useful technique for acquiring diagnostic knowledge for otoneurological diseases and for extracting relevant parameters from a large set of parameters.

Resection of giant schwannoma by combined surgical exposure.

Contrary to the past experience the giant Schwannoma with symptoms of canalis vertebralis compression has been removed by combined surgical exposure in one sitting. Laminectomy, decompression of the canalis vertebralis and immediately subsequent extracanalicular resection of the tumour by retroperitoneal approach was performed in one sitting by two surgical teams. The advantages of the combined surgical exposure: 1. The affliction of the patients caused by the operation significantly decreased. 2. The expenses of the treatment, nursing and hotel decreased as well. 3. The surgical team of different specialists remove the tumour together in both approaches.