ArticlePloS one2018
Ablation dynamics during laser interstitial thermal therapy for mesiotemporal epilepsy.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Imaging-based techniques for ablation zone definition and volumetry after laser interstitial thermal therapy (LITT) for intracranial lesions: a systematic review.Acta neurochirurgica · 2025Pooled it
- Evaluating safety and feasibility of same-day discharge after laser interstitial thermal therapy: a pilot study with a matched control group.Journal of neuro-oncology · 2025Article
- Epilepsy Surgery in the Last 10 Years: Advancements and Controversies.Epilepsy currents · 2025Review
- Laser ablation of a sphenoid wing meningioma: A case report and review of the literature.Surgical neurology international · 2023Article
- Stereotactic Laser Ablation for Mesial Temporal Lobe Epilepsy: A prospective, multicenter, single-arm study.Epilepsia · 2020Article
- Multicenter validation of automated trajectories for selective laser amygdalohippocampectomy.Epilepsia · 2019Article
- Predictive modeling of brain tumor laser ablation dynamics.Journal of neuro-oncology · 2019Article
- Clinically Significant Visual Deficits after Laser Interstitial Thermal Therapy for Mesiotemporal Epilepsy.Stereotactic and functional neurosurgery · 2019Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
introductionThe recent emergence of laser interstitial thermal therapy (LITT) as a frontline surgical tool in the management of brain tumors and epilepsy is a result of advances in MRI thermal imaging. A limitation to further improving LITT is the diversity of brain tissue thermoablative properties, which hinders our ability to predict LITT treatment-related effects. Utilizing the mesiotemporal lobe as a consistent anatomic model system, the goal of this study was to use intraoperative thermal damage estimate (TDE) maps to study short- and long-term effects of LITT and to identify preoperative variables that could be helpful in predicting tissue responses to thermal energy.
methodsFor 30 patients with mesiotemporal epilepsy treated with LITT at a single institution, intraoperative TDE maps and pre-, intra- and post-operative MRIs were co-registered in a common reference space using a deformable atlas. The spatial overlap of TDE maps with manually-traced immediate (post-ablation) and delayed (6-month) ablation zones was measured using the dice similarity coefficient (DSC). Then, motivated by simple heat-transfer models, ablation dynamics were quantified at amygdala and hippocampal head from TDE pixel time series fit by first order linear dynamics, permitting analysis of the thermal time constant (τ). The relationships of these measures to 16 independent variables derived from patient demographics, mesiotemporal anatomy, preoperative imaging characteristics and the surgical procedure were examined.
resultsTDE maps closely overlapped immediate ablation borders but were significantly larger than the ablation cavities seen on delayed imaging, particularly at the amygdala and hippocampal head. The TDEs more accurately predicted delayed LITT effects in patients with smaller perihippocampal CSF spaces. Analyses of ablation dynamics from intraoperative TDE videos showed variable patterns of lesion progression after laser activation. Ablations tended to be slower for targets with increased preoperative T2 MRI signal and in close proximity to large, surrounding CSF spaces. In addition, greater laser energy was required to ablate mesial versus lateral mesiotemporal structures, an effect associated with laser trajectory and target contrast-enhanced T1 MRI signal.
conclusionsPatient-specific variations in mesiotemporal anatomy and pathology may influence the thermal coagulation of these tissues. We speculate that by incorporating demographic and imaging data into predictive models we may eventually enhance the accuracy and precision with which LITT is delivered, improving outcomes and accelerating adoption of this novel tool.
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