Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning.

Area of Science:

• Neuro-oncology
• Medical Imaging Analysis
• Computational Pathology

Background:

• Brain tumor heterogeneity presents challenges in accurate tissue characterization.
• Quantitative imaging analysis is crucial for understanding tumor microenvironments.
• Artificial intelligence (AI) and radiomics are emerging as key technologies in this field.

Purpose of the Study:

• To explore the fundamentals of multiparametric brain tumor characterization.
• To understand the strengths, limitations, and applications of AI and radiomics in neuro-oncology.
• To guide the development and evaluation of models for improved diagnostic and prognostic value.

Main Methods:

• Utilizing artificial intelligence and radiomics for quantitative feature extraction from medical images.
• Employing machine learning algorithms for image preprocessing and tumor segmentation.
• Applying computational tools for feature extraction, classification, and prognostic stratification.

Main Results:

• AI and radiomics enable detailed analysis of complex brain tumor microenvironments.
• Computational tools facilitate various stages of image analysis, from preprocessing to prognosis.
• Understanding these tools is essential for enhancing diagnostic accuracy and prognostic capabilities.

Conclusions:

• AI and radiomics are invaluable for multiparametric brain tumor characterization.
• Effective application of these technologies can significantly improve patient outcomes.
• Further development and evaluation of AI-driven models are critical in neuro-oncology.