Meningioma recurrence: Time for an online prediction tool?

Area of Science:

• Neuro-oncology
• Genetics
• Medical Informatics

Background:

• Meningiomas, the most common brain tumors, have a high recurrence risk despite traditional grading systems.
• Current grading criteria (Simpson's, WHO) may be insufficient due to emerging clinical, radiological, and molecular variables.
• Advancements in web-based tools necessitate integrating new meningioma markers for improved patient care.

Purpose of the Study:

• To review the latest meningioma literature regarding recurrence and aggressive tumor biology.
• To identify the need for improved decision-making tools for clinicians managing meningiomas.
• To highlight the potential of novel markers and machine learning for predicting meningioma recurrence.

Main Methods:

• A scoping review of MEDLINE and Embase databases was conducted.
• Original studies and review articles from September 2022 to December 2023 were analyzed.
• Data on demographic, clinical, radiographic, histopathological, and genetic factors were synthesized.

Main Results:

• Older age, female sex, TERT promoter mutation, CDKN2A deletion, subtotal resection, and higher grade are associated with meningioma recurrence.
• A significant lack of clinical decision-making tools for meningioma management was identified.
• The need for a dynamic, machine-learning-based model to predict meningioma recurrence risk was established.

Conclusions:

• A recurrence prediction tool for meningioma is crucial for informed patient and clinician decision-making.
• Such a tool would aid in long-term surveillance and management strategies.
• Integrating diverse variables into a predictive model is essential for advancing meningioma care.