Object The precise natural history of incidentally discovered meningiomas (IDMs) remains unknown. It has been reported that for symptomatic meningiomas, tumor location can be used to predict growth. As to whether the same is true for IDMs has not been reported. This study aims to answer this question and provide biological evidence for this assumption by extending the study to involve symptomatic cases. Methods A total of 113 IDMs were analyzed by fine volumetry. A comparison of growth rates and patterns between skull base and non–skull base IDMs was made. Subsequently, materials obtained from 210 patients with symptomatic meningiomas who were treated in the authors' hospital during the same period were included for a biological comparison between skull base and non–skull base tumors using the MIB-1 index. Results The 110 patients with IDMs included 93 females and 17 males, with a mean follow-up period of 46.9 months. There were 38 skull base (34%) and 75 non–skull base (66%) meningiomas. Forty-two (37%) did not exhibit growth of more than 15% of the volume, whereas 71 (63%) showed growth. Only 15 (39.5%) of 38 skull base meningiomas showed growth, whereas 56 (74.7%) of 75 non–skull base meningiomas showed growth (p = 0.0004). In the 71 IDMs (15 skull base and 56 non–skull base), there was no statistical difference between the 2 groups in terms of mean age, sex, follow-up period, or initial tumor volume. However, the percentage of growth (p = 0.002) was significantly lower and the doubling time (p = 0.008) was significantly higher in the skull base than in the non–skull base tumor group. In subsequently analyzed materials from 94 skull base and 116 non–skull base symptomatic meningiomas, the mean MIB-1 index for skull base tumors was markedly low (2.09%), compared with that for non–skull base tumors (2.74%; p = 0.013). Conclusions Skull base IDMs tend not to grow, which is different from non–skull base tumors. Even when IDMs grow, the rate of growth is significantly lower than that of non–skull base tumors. The same conclusion with regard to biological behavior was confirmed in symptomatic cases based on MIB-1 index analyses. The authors' findings may impact the understanding of the natural history of IDMs, as well as strategies for management and treatment of IDMs and symptomatic meningiomas.

Molecular biological characterization of tumors has become a pivotal procedure for glioma patient care. The aim of this study is to build conventional MRI-based radiomics model to predict genetic alterations within grade II/III gliomas attempting to implement lesion location information in the model to improve diagnostic accuracy. One-hundred and ninety-nine grade II/III gliomas patients were enrolled. Three molecular subtypes were identified: IDH1/2-mutant, IDH1/2-mutant with TERT promoter mutation, and IDH-wild type. A total of 109 radiomics features from 169 MRI datasets and location information from 199 datasets were extracted. Prediction modeling for genetic alteration was trained via LASSO regression for 111 datasets and validated by the remaining 58 datasets. IDH mutation was detected with an accuracy of 0.82 for the training set and 0.83 for the validation set without lesion location information. Diagnostic accuracy improved to 0.85 for the training set and 0.87 for the validation set when lesion location information was implemented. Diagnostic accuracy for predicting 3 molecular subtypes of grade II/III gliomas was 0.74 for the training set and 0.56 for the validation set with lesion location information implemented. Conventional MRI-based radiomics is one of the most promising strategies that may lead to a non-invasive diagnostic technique for molecular characterization of grade II/III gliomas.

Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a diagnosis based on conventional radiomic features and patient age. Multisite preoperative MR images of 164 patients with grade II/III glioma were grouped by IDH and TERT promoter (pTERT) mutations as follows: (1) IDH wild type, (2) IDH and pTERT co-mutations, (3) IDH mutant and pTERT wild type. We applied a CNN (AlexNet) to four types of MR sequence and obtained the CNN texture features to classify the groups with a linear support vector machine. The classification was also performed using conventional radiomic features and/or patient age. Using all features, we succeeded in classifying patients with an accuracy of 63.1%, which was significantly higher than the accuracy obtained from using either the radiomic features or patient age alone. In particular, prediction of the pTERT mutation was significantly improved by the CNN texture features. In conclusion, the pretrained CNN texture features capture the information of IDH and TERT genotypes in grade II/III gliomas better than the conventional radiomic features.

There has been renewed interest in decompressive craniectomy as a surgical treatment for elevated intracranial pressure (ICP), although evidence-based clinical data are still lacking and some experimental results are conflicting. Ongoing clinical trials on the use of this operation after traumatic brain injury (TBI) may clarify the clinical application of this technique, however, some pathophysiological issues, such as the timing of this operation, its effect on brain edema formation, and its role for secondary brain damage, are still controversial. This review addresses recent clinical data on the influence of decompressive craniectomy on the brain pathophysiology in TBI. Decompressive craniectomy with dural augmentation enlarges intracranial space so that the swollen cerebral hemisphere could expand out of normal cranial limits, avoiding progression of brain herniation. The gain in intracranial volume results in both the improvement of cerebral compliance and a decrease in ICP; the latter favors a rise in both cerebral blood flow and cerebral microvascular perfusion, which can be accompanied by elevation in brain tissue oxygen tension (PbtO2) as well as the return of abnormal metabolic parameters to normal values in cases of cerebral ischemia. Enhancement of edema formation, impairment of cerebrovascular pressure reactivity, and non-restoration of brain aerobic metabolism due to metabolic crisis may occur after craniectomy and require further investigations. This review suggests that decompressive craniectomy as the sole treatment is likely to be insufficient; efforts must be made to maintain adequate brain hemodynamics, preferably coupled with brain metabolism, in addition to treating brain metabolic abnormalities, during postoperative stages.

Genomic microarray systems can simultaneously provide substantial genetic and chromosomal information in a relatively short time. We have analyzed genomic DNA from frozen sections of 30 cases of primary glioblastomas by GenoSensor Array 300 in order to characterize gene amplifications, gene deletions, and chromosomal information in the whole genome. Genes that were frequently amplified included RFC2/CYLN2 (63.3%), EGFR (53.3%), IL6 (53.3%), ABCB1 (MDR1) (36.7%), and PDGFRA (26.7%). Genes that were frequently deleted included (56.7%), FGFR2 (66.7%), MTAP (60.0%), DMBT1 CDKN2A (p16)/MTAP (50.0%), PIK3CA (43.3%), and EGR2 (43.3%), but deletion of RB1 or TP53 was rarely detected. Chromosomal gains were observed frequently for 7q (33.3%), 7p (20.0%), and 17q (13.3%). Loss of the 10q was frequently detected in 13 of 30 cases (46.7%). Loss of the entire chromosome 10 was seen in 9 of 30 cases (30.0%), and was often accompanied by EGFR amplification (7 cases, 77.8%). The GenoSensor Array 300 proved to be useful for identification of genome-wide molecular changes in glioblastomas. The obtained microarray profile can also yield valuable insight into the molecular events underlying carcinogenesis of brain tumors and may provide clues about clinical correlations, including response to treatment.