Developing advanced AI and computational models to understand and predict tumor growth and patient survival holds promise for improving outcomes in brain and central nervous system cancers. Despite substantial advancements, mortality rates remain unchanged over the past decades, particularly for aggressive forms like glioblastomas. Using high-resolution MR imaging, alongside molecular and patient clinical data, researchers aim to create non-invasive AI models that address tumor recurrence and radiation-induced challenges. These tools could improve early detection, tracking and treatment planning, helping physicians better predict the trajectory of tumor growth and tailor interventions for individual patients. Additionally, studying inherent biases in these AI models ensures that they are representative of different patient populations, bolstering their robustness and efficacy in healthcare settings.
The project will harness multimodal imaging and patient data to build detailed, physics-informed AI models and digital twins of tumor growth, simulating how tumors evolve and respond to treatments.
These AI models serve as virtual replicas, allowing healthcare professionals to explore different scenarios and optimize clinical strategies without invasive procedures.
By examining trustworthy and ethical AI modeling, the research aims to create reliable clinical tools that support better cancer care.
The efforts are bolstered by the expertise of ±¬ÁϹÏ’s Vision Lab, with significant experience in leading NIH-sponsored research projects on brain tumors. With a goal to broaden research into tumor recurrence and progression, collaborations with multiple academic medical institutions are underway. These initiatives are not only positioned to leverage funding opportunities from institutions like the NIH, but also to establish themselves as leaders in AI-driven medical research. Commitment to trustworthy AI, guided by the FAIR data principles, is integral to the project, ensuring that advances in this field are shared widely through publications and various media, fostering continued innovation and application in cancer treatment.
Khan Iftekharuddin, Ph.D.
Director, ±¬ÁϹÏ’s Vision Lab; Inaugural Director, Institute of Data Science; Professor, Electrical and Computer Engineering; ±¬ÁϹÏ