To assess the degree of malignancy in cerebral gliomas at the time of diagnosis, we compared the metabolic ratio using 18F-fluorodeoxyglucose(FDG)-Positron Emission Tomography(PET) with histologic grading and proliferative index(Ki-67) of cerebral gliomas. Materials for this study were histologically-examined 21 gliomas and they were divided into glioblastomas as group 1, anaplastic gliomas as group 2, and low-grade gliomas as group 3. The visual analysis of FDG-PET images showed hypermetabolic lesions in 14(87.5%) out of 16 high-grade gliomas (glioblastomas and anaplastic gliomas), and hypometabolic lesions in 4(80%) out of 5 low-grade gliomas. Tumor to cerebellum ratio(T/Cbll) in FDG-PET was used as metabolic ratio and the values of T/Cbll in each group were 1.30+/-0.10, 0.73+/-0.07, 0.70+/-0.07, respectively. In comparision of T/Cbll between group 1 with remaining two groups, differences were statistically significant(p=0.0002, p=0.0002, respectively), however, there was no statistical difference between group 2 and group 3. The values of Ki-67 were 24.16+/-5.66 in group 1, 8.10+/-2.70 in group 2, 5.46+/-1.23 in group 3, and differences were statistically significant between group 1 and group 2, 3(p=0.015, p=0.015, respectively), but there was no statistical difference between group 2 and group 3. The correlation between T/Cbll and Ki-67 was good and statistically significant(p=0.0047). In conclusion, the visual and semiquantitative analysis of FDG-PET would be helpful in determining the degree of malignancy in cerebral gliomas.
Cerebellum ; Diagnosis ; Glioblastoma ; Glioma*

For assessment of the feasibility of histopathological diagnosis on the basis of CT findings in suratentorial gliomas, 60 consecutive histologically proven cases were analysed. Benign astrocytomas(Kernohan's grade I, I-II, II) were 25, Oligodendrogliomas 6, malignant gliomas(Kernohan's grade III, III-IV, GM) 29 in number. Plain CT findings and degree of peritumoral edema were less significant than the patterns of contrast enhancement in predicting the histological malignancy. Calcification, if present, excluded the diagnosis of malignant gliomas. Combining the CT criteria of pattern of contrast enhancement, degree of peritumoral edema with angiographic signs of malignancy in addition to the clinical feature, a more confident histological diagnosis seemed allowable.
Diagnosis* ; Edema ; Glioma* ; Oligodendroglioma

Histopathological diagnosis of brain stem glioma should be performed for the purpose of the determination of its management and clinical course, but its surgical biopsy has been followed by high mortality and morbidity. We performed the tissue sampling for histological examination with BRW stereotaxic system under local anesthesia successfully.
Anesthesia, Local ; Biopsy* ; Brain Stem* ; Brain* ; Diagnosis ; Glioma* ; Mortality

In the treatment of the intracranial gliomas surgical intervention is recommended as the standard procedure which should be performed in all cases when the tumor is accessible. While surgery will not bring about a cure and clearly, radiation therapy and chemotherapy have made a significant impact of long-term survival in the treatment of the malignant gliomas, nevertheless surgery still remains the single most effective method for achieving a rapid reduction of tumor burden reducing increased intracranial pressure and provides a tissue diagnosis. Following surgery, the other antitumor programs have the best chance of achieving a significant increment of tumor cell kill, therefore, surgery has a distinct role to play in the multidisciplinable approach to the treatment of these highly aggressive malignant tumors. It is very unlikely that future advances will obviate the necessity for conventional surgery in the treatment of benign gliomas. The surgical management of gliomas with major emphasis on malignant ones is presented including the pathophysiology, radiological diagnosis, aim of surgery, surgical procedure and some different possibility of surgical treatment. Prospective future development of surgical treatment of brain tumor is also considered.
Brain Neoplasms ; Diagnosis ; Drug Therapy ; Glioma* ; Intracranial Pressure ; Tumor Burden

Fractionation dose and number have been known as radiation factor affecting the radiation complication and the effectiveness in radiotherapy for brain tumors. In this study hyperfractionation technique with 115cGy/fractioin 2 fractions daily 5days/wk, upto 5750-6900cGy to partial brain volume was compared with conventional fractionation technique with daily 200cGy/fraction 5 fraction/wk, upto 5400-6000cGy, in regarding to the effectiveness of hyperfractionated radiotherapy and eraly and later radiation reavtion. The survival period was longer in hyperfractionated irradiated group particularly if the tumors were located in the posterior portion of brain, however there was no singificant statistics due to small number of patients. Mean survival period for glioblastoma multiforme was 11.8 months in hyperfractionated group vs 8.7 months in conventional fractionated group and for high grade astrocytoma 36month in hyperfractionated group, but in conventional fractionated group all was died in 18 months. Acute radiation reaction occurred less frequently in hyperfractionated group, 15.8% vs 47.8% in conventional fractionated group(p<0.024). Alopeci was developed in 31.6% of the hyperfractionated group vs 82.6% of the conventional fractionated group(p<0.0031). One case of later radiation necrosis in cancer region was suspected in the hyperfractionated group but we has been in a dilemma for confirmatory diagnosis in present available diagnostic technique. The hyperfractionated irradiation technique was proven to be superior to conventional fractionated technique regarding the radiation reaction and the effectiveness of the treatment.
Astrocytoma ; Brain ; Brain Neoplasms ; Diagnosis ; Glioblastoma ; Glioma* ; Humans ; Necrosis ; Radiotherapy*

Computerized tomography as a diagnostic procedure is characterized by its high sensitivity but limited specificity. This lack of specificity may result in erroneous diagnosis because many different pathological processes can have similar enhancement patterns. We prosent a patient with a nonspecific inflammatory lesion which was erroneously diagnosed as having an rapidly growing malignant glioma on sequential computerized tomography. We confirmed this lesion as a nonspecific inflammatory process by brain biopsy and the extensive lesion was disappeared after steroid therapy.
Biopsy ; Brain ; Diagnosis ; Glioma* ; Humans ; Pathologic Processes ; Sensitivity and Specificity

Adult ; Diagnostic Errors ; Female ; Glioma ; diagnosis ; Humans ; Male ; Middle Aged

Adult ; Cerebral Ventricle Neoplasms ; diagnosis ; Female ; Glioma ; diagnosis ; Humans ; Magnetic Resonance Imaging ; Prognosis ; Visual Fields

In the field of computational histopathology, computer-assisted diagnosis systems are important in obtaining patient-specific diagnosis for various diseases and help precision medicine. Therefore, many studies on automatic analysis methods for digital pathology images have been reported. In this work, we discuss an automatic feature extraction and disease stage classification method for glioblastoma multiforme (GBM) histopathological images. In this paper, we use deep convolutional neural networks (Deep CNNs) to acquire feature descriptors and a classification scheme simultaneously. Further, comparisons with other popular CNNs objectively as well as quantitatively in this challenging classification problem is undertaken. The experiments using Glioma images from The Cancer Genome Atlas shows that we obtain 96:5% average classification accuracy for our network and for higher cross validation folds other networks perform similarly with a higher accuracy of 98:0%. Deep CNNs could extract significant features from the GBM histopathology images with high accuracy. Overall, the disease stage classification of GBM from histopathological images with deep CNNs is very promising and with the availability of large scale histopathological image data the deep CNNs are well suited in tackling this challenging problem.
Classification* ; Diagnosis ; Diagnosis, Computer-Assisted ; Genome ; Glioblastoma* ; Glioma ; Methods ; Pathology ; Precision Medicine ; Subject Headings

In order to correctly predict the malignant degree of brain glioma, three data mining algorithms: multi-layer perceptron network(MLP), decision tree, and rule induction are adopted to acquire diagnostic knowledge from patients with brain glioma cases. Totally 280 cases are collected, and some of them contain missing values. Preprocessing is taken to make them applicable to all three algorithms. Performance comparisons are carried out with a 10-fold cross validation test. Although the result of MLP is hard to be understood and cannot be applied directly, its reliability and accuracy are the highest when only a few hidden nodes are involved. Unlike MLP, both decision tree and rule induction use attribute-value pairs to represent diagnostic knowledge derived from treated cases. These could improve both the understandability and applicability of their results. When compared with rule induction, the inherent restriction in structure makes decision tree more efficient in decision-making but meanwhile hurts its simplicity, accuracy, and reliability. For testing samples, results of all these algorithms can achieve accuracy rate over 80%, which satisfies the basic requirement of neuroradiologists. If diagnostic accuracy rate is the main factor to be considered, MLP with only a few hidden nodes is the best. If the result is expected to be further checked or evaluated, rule induction will be the best algorithm. This work proves that data mining techniques can be used to obtain valid diagnostic knowledge from brain glioma cases and make computer aided diagnosis system in this field feasible.
Algorithms ; Brain Neoplasms ; diagnosis ; Decision Trees ; Diagnosis, Computer-Assisted ; methods ; Glioma ; diagnosis ; Humans ; Neural Networks (Computer) ; Pattern Recognition, Automated