Journal of Neuro-Oncology

“micro-metastases”–indicative of local spread/invasion or intracerebral seeding of tumoral cells–and the exceedingly rare instances of distant GBM metastasis.“Micro-metastases” typically refer to small, infiltrative clusters of tumoral cells that locally disperse from the primary tumor, usually measuring less than 2 mm in size. In contrast, distant GBM metastasis, which often occur extracranially, is exceptionally uncommon [8]. The rarity is attributed partly to the aggressive nature of GBM, which often results in limited survival time, thereby not allowing sufficient time for distant metastases to develop.

Recent meta-analyses [9, 10] have indicated that time elapsed between the identification of the metastasis and death was significantly increased in patients undergoing surgery [9] than those who only had biopsy [10]. This suggests that early detection and treatment of primary tumor could potentially delay GBM metastasis. The critical need for early detection of GBM underscores the importance of innovative diagnostic approaches, such as imaging biomarkers. It is noteworthy that GBM “micro-metastasis” is the primary contributor to GBM recurrence, presenting significant surgical or treatment challenges and contributing to the poor prognosis of this disease. To date, it remains unclear whether imaging biomarkers alone are sufficient for detecting infiltrating GBM tumoral cells. However, incorporating imaging biomarkers alongside standard imaging techniques could offer surgeons and clinicians with enhanced diagnostic insight. The integration of novel molecular imaging agents that target specific tumor cell markers could significantly enhance the sensitivity of PET scans in identifying distant GBM metastases. Radiomics, derived from 18F-FDG PET/CT, has been utilized to differentiate GBM metastasis and primary GBM from other brain metastasis [11, 12]. Another consideration is the integration of imaging biomarker data with other clinical and molecular data to improve patient stratification and treatment personalization. The burgeoning field of radiogenomics, which links imaging features with genetic information, is beginning to show how imaging biomarkers can be used to predict tumor behavior and response to therapy. As research progresses, the role of artificial intelligence (AI) in interpreting complex imaging data cannot be overstated. AI and machine learning algorithms are being developed to analyze imaging biomarkers, offering the potential to rapidly identify patterns that might indicate metastasis or predict therapeutic response [13]. However, the application of these imaging biomarkers in clinical practice is not without challenges. One significant hurdle is the standardization of imaging protocols and the interpretation of results across different institutions. Furthermore, there is a need for comprehensive validation studies to establish the reliability and clinical relevance of these biomarkers in the context of GBM metastasis [4]. Also,