Integrating Biophysical Modeling and Radiomics: Computational Strategies for Brain Tumor Growth and Therapy Planning

Central nervous system (CNS) tumors, such as glioblastomas, are characterized by significant histologic, molecular, and imaging heterogeneity, complicating precise diagnosis and therapeutic planning. Integrating biophysical growth models with multiparametric magnetic resonance imaging (mpMRI) and radiomic feature analysis offers a promising computational framework for personalized neuro-oncology. Biophysical models simulate tumor growth and invasion based on physical principles, providing insights into spatial patterns of tumor proliferation and invasion. Calibrating these models using mpMRI data, which includes sequences like T1-weighted, T2-weighted, FLAIR, and diffusion-weighted imaging, allows for the extraction of quantitative features that reflect tumor characteristics. The integration of biophysical models, mpMRI, and radiomics has shown promise in differentiating tumor types, assessing treatment response, and predicting patient outcomes. For instance, studies have demonstrated the utility of radiomics in distinguishing glioblastomas from primary CNS lymphomas and in evaluating treatment responses. However, challenges remain, including standardization of imaging protocols, reproducibility across different institutions, and the need for large-scale clinical validation. Advancements in machine learning and deep learning are driving the development of more robust and accurate models. The integration of these technologies with biophysical modeling and radiomics holds the potential to revolutionize CNS tumor classification and grading, aligning with the World Health Organization's evolving classification systems. Ultimately, these integrated strategies aim to improve patient outcomes through personalized treatment approaches. In summary, the fusion of biophysical modeling, mpMRI, and radiomics represents a significant step toward precision neuro-oncology, offering a comprehensive and personalized approach to the diagnosis and treatment of CNS tumors.