Glioblastoma multiforme (GBM) is recognized as the most common and lethal form of central nervous system cancer. Currently used surgical techniques, chemotherapeutic agents, and radiotherapy strategies have done very little in extending the life expectancies of patients diagnosed with GBM. The difficulty in treating this malignant disease lies both in its inherent complexity and numerous mechanisms of drug resistance. In this review, we summarize several of the primary mechanisms of drug resistance. We reviewed available published literature in the English language regarding drug resistance in glioblastoma. The reasons for drug resistance in glioblastoma include drug efflux, hypoxic areas of tumor cells, cancer stem cells, DNA damage repair, and miRNAs. Many potential therapies target these mechanisms, including a series of investigated alternative and plant-derived agents. Future research and clinical trials in glioblastoma patients should pursue combination of therapies to help combat drug resistance. The emerging new data on the potential of plant-derived therapeutics should also be closely considered and further investigated.

Intraoperative CSF leaks can occur during endoscopic sellar surgery, especially in larger tumors or craniopharyngiomas. Once an intraoperative leak occurs, risk factors for postoperative leaks include craniopharyngiomas and higher BMI. Use of septal flaps decreases this risk.

Objective: Medial temporal lobe epilepsy (TLE) is the most common form of medication-resistant focal epilepsy in adults. Despite removal of medial temporal structures, more than one-third of patients continue to have disabling seizures postoperatively. Seizure refractoriness implies that extramedial regions are capable of influencing the brain network and generating seizures. We tested whether abnormalities of structural network integration could be associated with surgical outcomes. Methods: Presurgical magnetic resonance images from 121 patients with drug-resistant TLE across 3 independent epilepsy centers were used to train feed-forward neural network models based on tissue volume or graph-theory measures from whole-brain diffusion tensor imaging structural connectomes. An independent dataset of 47 patients with TLE from 3 other epilepsy centers was used to assess the predictive values of each model and regional anatomical contributions toward surgical treatment results. Results: The receiver operating characteristic area under the curve based on regional betweenness centrality was 0.88, significantly higher than a random model or models based on gray matter volumes, degree, strength, and clustering coefficient. Nodes most strongly contributing to the predictive models involved the bilateral parahippocampal gyri, as well as the superior temporal gyri. Interpretation: Network integration in the medial and lateral temporal regions was related to surgical outcomes. Patients with abnormally integrated structural network nodes were less likely to achieve seizure freedom. These findings are in line with previous observations related to network abnormalities in TLE and expand on the notion of underlying aberrant plasticity. Our findings provide additional information on the mechanisms of surgical refractoriness.

Deep learning demonstrated to be a powerful statistical approach capable of isolating abnormal individualized patterns from complex datasets to provide a highly accurate prediction of seizure outcomes after surgery. Features involved in this predictive model were both ipsilateral and contralateral to the clinical foci and spanned across limbic and extralimbic networks.

Postoperative endoscopic pituitary adenoma surgery complications are associated with tumors with intraventricular extension, preoperative radiation, as well as common patient comorbidities. Identification of these factors may permit implementation of strategies to reduce postoperative complications.