From Pixels to Prognosis: Artificial Intelligence and Machine Learning Models in Brain Tumour Mutation Prediction

Area of Science:

• Neuro-oncology
• Medical imaging
• Computational biology

Background:

• Brain tumors are a significant cause of mortality and morbidity.
• Current diagnosis relies on MRI and pathological examination of tissue.
• Limitations exist in current diagnostic methods for precise tumor characterization.

Purpose of the Study:

• To review the role of machine learning in identifying brain tumor mutations.
• To explore the potential of using imaging data alone for diagnosis.
• To highlight advancements in AI for neuro-oncology.

Main Methods:

• Literature review of studies on machine learning and brain tumors.
• Analysis of research utilizing imaging data for tumor mutation prediction.
• Synthesis of findings on AI-driven diagnostic and prognostic capabilities.

Main Results:

• Machine learning models can analyze imaging data to infer tumor characteristics.
• Pattern recognition in MRI scans shows potential for non-invasive mutation detection.
• AI offers new avenues for outcome prediction in brain tumor patients.

Conclusions:

• Machine learning is a powerful tool for advancing brain tumor diagnosis.
• Non-invasive methods using AI and imaging data are emerging.
• Further research is needed to fully integrate ML into clinical practice for brain tumor management.