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Optimizing Trajectories for Cranial Laser Interstitial Thermal Therapy Using Computer-Assisted Planning: A Machine

Kuo Li1,2, Vejay N Vakharia3,4, Rachel Sparks5,6

  • 1The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.

Neurotherapeutics : the Journal of the American Society for Experimental Neurotherapeutics
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Machine learning optimizes laser interstitial thermal therapy (LITT) trajectories for drug-resistant epilepsy. This approach identifies optimal entry and target points to maximize ablation of the amygdalohippocampal complex while sparing the parahippocampal gyrus, potentially improving outcomes.

Keywords:
LITTMTLEMachine learningepilepsylaser ablation.

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Area of Science:

  • Neurosurgery
  • Medical Technology
  • Computational Neuroscience

Background:

  • Laser interstitial thermal therapy (LITT) offers an alternative to open surgery for drug-resistant mesial temporal lobe epilepsy (MTLE).
  • Maximizing ablation of the amygdalohippocampal complex (AHC) correlates with seizure freedom, while sparing the parahippocampal gyrus (PHG) is linked to better neuropsychological outcomes.
  • Computer-assisted planning (CAP) enhances trajectory optimization, but specific entry and target zones for maximal AHC ablation and PHG sparing remain undetermined.

Purpose of the Study:

  • To apply a machine learning (ML) approach for predicting optimal entry and target parameters in CAP for LITT in MTLE.
  • To identify entry and target zones that maximize AHC ablation while minimizing PHG involvement.
  • To generate clinically feasible LITT trajectories using ML-predicted parameters for improved safety and efficacy.

Main Methods:

  • A retrospective analysis of ten MTLE patients with hippocampal sclerosis undergoing CAP LITT.
  • Generation of 7600 LITT trajectories by varying entry regions (occipital and temporal gyri) and target points within the AHC.
  • Utilized random forest and linear regression ML models to predict ablation volumes and identify optimal parameter combinations.

Main Results:

  • ML models (random forest and linear regression) showed high correlation (ρ=0.7) with calculated ablation volumes.
  • Optimal entry points were identified near the junction of the inferior occipital, middle occipital, and middle temporal gyri.
  • The anteromedial amygdala was determined as the optimal target point for maximizing AHC ablation and sparing PHG.

Conclusions:

  • Machine learning accurately predicts composite ablation scores for LITT trajectory planning in MTLE.
  • Identified optimal entry and target parameters can guide CAP to maximize therapeutic effect while minimizing collateral damage.
  • Prospective studies are needed to validate if this ML-guided approach improves seizure control and reduces neuropsychological deficits in MTLE patients.