Advances and Integrations of Computer-Assisted Planning, Artificial Intelligence, and Predictive Modeling Tools for Laser Interstitial Thermal Therapy in Neurosurgical Oncology.
Authors
Affiliations (2)
Affiliations (2)
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, Maryland, USA.
Abstract
Laser interstitial thermal therapy (LiTT) has emerged as a minimally invasive, MRI-guided treatment of brain tumors that are otherwise considered inoperable because of their location or the patient's poor surgical candidacy. By directing thermal energy at neoplastic lesions while minimizing damage to surrounding healthy tissue, LiTT offers promising therapeutic outcomes for both newly diagnosed and recurrent tumors. However, challenges such as postprocedural edema, unpredictable heat diffusion near blood vessels and ventricles in real time underscore the need for improved planning and monitoring. Incorporating artificial intelligence (AI) presents a viable solution to many of these obstacles. AI has already demonstrated effectiveness in optimizing surgical trajectories, predicting seizure-free outcomes in epilepsy cases, and generating heat distribution maps to guide real-time ablation. This technology could be similarly deployed in neurosurgical oncology to identify patients most likely to benefit from LiTT, refine trajectory planning, and predict tissue-specific heat responses. Despite promising initial studies, further research is needed to establish the robust data sets and clinical trials necessary to develop and validate AI-driven LiTT protocols. Such advancements have the potential to bolster LiTT's efficacy, minimize complications, and ultimately transform the neurosurgical management of primary and metastatic brain tumors.