An optimization approach to white matter brain tumor resection

Abstract

Preserving brain functionality while performing maximal tumor resection remains a significant challenge in current clinical practice, as existing approaches do not offer a systematic framework to balance tumor removal with functional outcomes. To address this, we developed a mathematical optimization model for brain tumor resection problem that aims to preserve network efficiency as much as possible by resecting a predefined volume of tumor. Network efficiency is quantified using the Global Efficiency (GE) metric. The model includes spatial contiguity constraints to ensure that the resected area forms a clinically realistic connected region. To improve computational efficiency and scalability, we proposed enhanced formulations and introduced an aggregation heuristic that groups nodes that are physically closer to each other, significantly reducing solution time while maintaining high-quality results. For the experimental analysis, we used real brain network data representing healthy white and gray matter regions and generated synthetic tumor instances and modeled their disruptive impact on the brain network. We first validated the performance of the aggregation heuristic by comparing it against the original model. Results show that the heuristic drastically reduces computational time while achieving comparable GE values. We then investigated the impact of tumor size and found that larger tumors disrupt the network more severely even before resection, and lead to lower GE values post-resection at the same threshold levels. Then, we examined the impact of tumor location and observed that centrally located tumors lead to greater decreases in GE compared to peripheral tumors when the same tumor volume is removed. Finally, using a real tumor instance, we compared the ”best” resection guided by our model with the ”worst” resection, and demonstrated that our approach leads to significantly better preservation of brain network efficiency.