A heterogeneous tissue model for treatment planning for magnetic resonance-guided laser interstitial thermal therapy

Abstract

We evaluated a physics-based model for planning for magnetic resonance-guided laser interstitial thermal therapy for focal brain lesions. Linear superposition of analytical point source solutions to the steady-state Pennes bioheat transfer equation simulates laser-induced heating in brain tissue. The line integral of the photon attenuation from the laser source enables computation of the laser interaction with heterogeneous tissue. Magnetic resonance thermometry data sets (n = 31) were used to calibrate and retrospectively validate the model's thermal ablation prediction accuracy, which was quantified by the Dice similarity coefficient (DSC) between model-predicted and measured ablation regions (T > 57 °C). A Gaussian mixture model was used to identify independent tissue labels on pre-treatment anatomical magnetic resonance images. The tissue-dependent optical attenuation coefficients within these labels were calibrated using an interior point method that maximises DSC agreement with thermometry. The distribution of calibrated tissue properties formed a population model for our patient cohort. Model prediction accuracy was cross-validated using the population mean of the calibrated tissue properties. A homogeneous tissue model was used as a reference control. The median DSC values in cross-validation were 0.829 for the homogeneous model and 0.840 for the heterogeneous model. In cross-validation, the heterogeneous model produced a DSC higher than that produced by the homogeneous model in 23 of the 31 brain lesion ablations. Results of a paired, two-tailed Wilcoxon signed-rank test indicated that the performance improvement of the heterogeneous model over that of the homogeneous model was statistically significant (p < 0.01).

Thermal ablation of biological tissues in disease treatment: A review of computational models and future directions

“…With regards to the application of laser therapy in neurosurgery, (Fahrenholtz et al, 2018) recently reported a computational study for predicting and optimizing the maximum extent of ablation during the surgical planning of magnetic resonance-guided laser ablation in brain. (Mitchell et al, 2018) reported a study to model magnetic resonance-guided laser therapy in heterogeneous tissue, considering four different tissue types with independent optical properties, for predicting the treatment outcomes in brain.…”

“…Although, RFA (and recently MWA) applications in clinical practices is more widespread compared to laser ablation for treating a tumor in soft tissues, recently there has been a surge in the application of laser therapy for treating different types of neurological disorders, e.g., brain tumors, epilepsy. Importantly, the application of magnetic resonance (MR)-guided laser therapy in treating brain disorders results in higher efficacy, improved real-time intraoperative monitoring of the ablation zone, low risk of complications, shorter hospitalization time and no damage of the tissue beyond the ablation zone as compared to the conventional treatments (Mitchell et al, 2018). Furthermore, the accuracy of the computational models of thermal ablation is significantly dependent on the tissue's biophysical parameters and thus accurate characterization of such parameters.…”

The use of laser interstitial thermal therapy in the treatment of brain metastases: a literature review

“…A central problem in interstitial heating is the difficulty to predict the extent of ablation due to tissue characteristics and proximity to heat sinks, such as large vessels, ventricles and the sulcal interfaces as they contain cerebrospinal fluid in the brain [16,17]. The compatibility of LITT with Magnetic Resonance Imaging (MRI) techniques has allowed the visualization of energy deposition in near real-time.…”

“…Incompletely ablated lesions are among the dominant indicators for recurrent disease. A-priori mathematical model predictions [17] may optimize treatment planning for more complete tumor coverage during delivery. Nonlinear models of bioheat transfer with homogeneous temperature dependent perfusion and optical properties are likely to provide a practical methodology in planning thermal damage.…”

Remote control of CAR T cell therapies by thermal targeting

“…Heat treatments are used clinically to sensitize cancer cells to chemotherapy, ablate isolated metastatic nodules, and enhance diffusion of small molecule drugs into tumors 24 . Both superficial and deepseeded tumors can be targeted for thermal treatment by platforms including high intensity focused ultrasound (HIFU) 25 , laser interstitial thermal therapy (LITT) 26 , and electromagnetic heating 27 . To engineer T cells with the ability to respond to heat, we constructed and screened panels of synthetic thermal gene switches containing combinations of Heat Shock Elements (HSEs) and core promoters to identify an architecture that responds to mild hyperthermia while remaining non-responsive to orthogonal cell stresses.…”