Learning Curve Analysis and Adverse Events After Implementation of Neurosurgical Laser Ablation Treatment

Jensdottir, Margret; Sandvik, Ulrika; Jakola, Asgeir Store; Fagerlund, Michael; Kits, Annika; Guðmundsdóttir, Klara; Tabari, Sara; Majing, Tomas; Fletcher‐Sandersjöö, Alexander; Chen, Clark C.; Bartek, Jiri

doi:10.1016/j.nec.2022.12.003

Cited by 1 publication

(6 citation statements)

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“…Even with MRgLITT to assist the placement of the laser probe and monitor tissue temperature, outcomes depend on operator skill and experience to avoid off-target thermal damage while ensuring effective ablation of the target tissue [ 5 ]. Complexity in stereotactic laser placement and lack of treatment planning further increases the time necessary for an operator to achieve proficiency [ 6 , 7 ]. LITT treatment requires integrated trajectory planning for placement of the probe and bioheat transfer-based simulations for thermal dose planning.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, there is no LITT treatment planning framework to aid neurosurgeons when they plan thermal dose delivery with LITT. This leads to highly variable, and user-dependent lesion coverage with diminished predictability of outcomes for patients [ 6 , 7 ]. A LITT treatment planning framework that enables neurosurgeons to create robust, reliable, and reproducible LITT plans quickly using mobile computing will reduce the learning curve, improve the lesion coverage and quality control [ 6 , 7 , 9 , 10 , 11 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…This leads to highly variable, and user-dependent lesion coverage with diminished predictability of outcomes for patients [ 6 , 7 ]. A LITT treatment planning framework that enables neurosurgeons to create robust, reliable, and reproducible LITT plans quickly using mobile computing will reduce the learning curve, improve the lesion coverage and quality control [ 6 , 7 , 9 , 10 , 11 , 17 , 18 ]. Ultimately, we can expect that widespread adoption of such tools will manifest with improved outcomes for GBM patients.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Margret et al demonstrated that poor laser positioning can result in inadequate ablation or adverse events such as hemorrhage or unintended hyperthermia in normal brain tissue [ 6 , 7 ]. They noted that improvements correlated with experience and use of a trajectory planning tool for accurate placement of the probe [ 6 , 7 ]. This has motivated development of an accessible and precise treatment planning tool that combines stereotactic placement of the laser catheter to enhance successful LITT treatment.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Treatment Planning Framework for Laser Interstitial Thermal Therapy (LITT)

Lad,

Jangam,

Carlton

et al. 2023

Cancers

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Purpose: Develop a treatment planning framework for neurosurgeons treating high-grade gliomas with LITT to minimize the learning curve and improve tumor thermal dose coverage. Methods: Deidentified patient images were segmented using the image segmentation software Materialize MIMICS©. Segmented images were imported into the commercial finite element analysis (FEA) software COMSOL Multiphysics© to perform bioheat transfer simulations. The laser probe was modeled as a cylindrical object with radius 0.7 mm and length 100 mm, with a constant beam diameter. A modeled laser probe was placed in the tumor in accordance with patient specific patient magnetic resonance temperature imaging (MRTi) data. The laser energy was modeled as a deposited beam heat source in the FEA software. Penne’s bioheat equation was used to model heat transfer in brain tissue. The cerebrospinal fluid (CSF) was modeled as a solid with convectively enhanced conductivity to capture heat sink effects. In this study, thermal damage-dependent blood perfusion was assessed. Pulsed laser heating was modeled based on patient treatment logs. The stationary heat source and pullback heat source techniques were modeled to compare the calculated tissue damage. The developed bioheat transfer model was compared to MRTi data obtained from a laser log during LITT procedures. The application builder module in COMSOL Multiphysics© was utilized to create a Graphical User Interface (GUI) for the treatment planning framework. Results: Simulations predicted increased thermal damage (10–15%) in the tumor for the pullback heat source approach compared with the stationary heat source. The model-predicted temperature profiles followed trends similar to those of the MRTi data. Simulations predicted partial tissue ablation in tumors proximal to the CSF ventricle. Conclusion: A mobile platform-based GUI for bioheat transfer simulation was developed to aid neurosurgeons in conveniently varying the simulation parameters according to a patient-specific treatment plan. The convective effects of the CSF should be modeled with heat sink effects for accurate LITT treatment planning.

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Section: Introductionmentioning

confidence: 99%