The invasive capabilities of glioblastoma (GBM) define the cancer’s aggressiveness, treatment resistance, and overall mortality. The tumor microenvironment influences the molecular behavior of cells, both epigenetically and genetically. Current forces being studied include properties of the extracellular matrix (ECM), such as stiffness and “sensing” capabilities. There is currently limited data on the physical forces in GBM—both relating to how they influence their environment and how their environment influences them. This review outlines the advances that have been made in the field. It is our hope that further investigation of the physical forces involved in GBM will highlight new therapeutic options and increase patient survival. A search of the PubMed database was conducted through to 23 March 2022 with the following search terms: (glioblastoma) AND (physical forces OR pressure OR shear forces OR compression OR tension OR torsion) AND (migration OR invasion). Our review yielded 11 external/applied/mechanical forces and 2 tumor microenvironment (TME) forces that affect the ability of GBM to locally migrate and invade. Both external forces and forces within the tumor microenvironment have been implicated in GBM migration, invasion, and treatment resistance. We endorse further research in this area to target the physical forces affecting the migration and invasion of GBM.

The response assessment in neuro-oncology (RANO) criteria have been the gold standard for monitoring treatment response in glioblastoma (GBM) and differentiating tumor progression from pseudoprogression. While the RANO criteria have played a key role in detecting early tumor progression, their ability to identify pseudoprogression is limited by post-treatment damage to the blood-brain barrier (BBB), which often leads to contrast enhancement on MRI and correlates poorly to tumor status. Amino acid positron emission tomography (AA PET) is a rapidly growing imaging modality in neuro-oncology. While contrast-enhanced MRI relies on leaky vascularity or a compromised BBB for delivery of contrast agents, amino acid tracers can cross the BBB, making AA PET particularly well-suited for monitoring treatment response and diagnosing pseudoprogression. The authors performed a systematic review of PubMed, MEDLINE, and Embase through December 2021 with the search terms “temozolomide” OR “Temodar,” “glioma” OR “glioblastoma,” “PET,” and “amino acid.” There were 19 studies meeting inclusion criteria. Thirteen studies utilized [¹⁸F]FET, five utilized [¹¹C]MET, and one utilized both. All studies used static AA PET parameters to evaluate TMZ treatment in glioma patients, with nine using dynamic tracer parameters in addition. Throughout these studies, AA PET demonstrated utility in TMZ treatment monitoring and predicting patient survival.

The quality of radiation therapy (RT) treatment plans directly affects the outcomes of clinical trials. KBP solutions have been utilized in RT plan quality assurance (QA). In this study, we evaluated the quality of RT plans for brain and head/neck cancers enrolled in multi-institutional clinical trials utilizing a KBP approach. The evaluation was conducted on 203 glioblastoma (GBM) patients enrolled in NRG-BN001 and 70 nasopharyngeal carcinoma (NPC) patients enrolled in NRG-HN001. For each trial, fifty high-quality photon plans were utilized to build a KBP photon model. A KBP proton model was generated using intensity-modulated proton therapy (IMPT) plans generated on 50 patients originally treated with photon RT. These models were then applied to generate KBP plans for the remaining patients, which were compared against the submitted plans for quality evaluation, including in terms of protocol compliance, target coverage, and organ-at-risk (OAR) doses. RT plans generated by the KBP models were demonstrated to have superior quality compared to the submitted plans. KBP IMPT plans can decrease the variation of proton plan quality and could possibly be used as a tool for developing improved plans in the future. Additionally, the KBP tool proved to be an effective instrument for RT plan QA in multi-center clinical trials.