Theoretical model for laser ablation outcome predictions in brain: calibration and validation on clinical MR thermometry images.

ABSTRACT: PURPOSE:Neurosurgical laser ablation is experiencing a renaissance. Computational tools for ablation planning aim to further improve the intervention. Here, global optimisation and inverse problems are demonstrated to train a model that predicts maximum laser ablation extent. METHODS:A closed-form steady state model is trained on and then subsequently compared to N?=?20 retrospective clinical MR thermometry datasets. Dice similarity coefficient (DSC) is calculated to provide a measure of region overlap between the 57?°C isotherms of the thermometry data and the model-predicted ablation regions; 57?°C is a tissue death surrogate at thermal steady state. A global optimisation scheme samples the dominant model parameter sensitivities, blood perfusion (?) and optical parameter (?eff) values, throughout a parameter space totalling 11?440 value-pairs. This represents a lookup table of ?eff-? pairs with the corresponding DSC value for each patient dataset. The ?eff-? pair with the maximum DSC calibrates the model parameters, maximising predictive value for each patient. Finally, leave-one-out cross-validation with global optimisation information trains the model on the entire clinical dataset, and compares against the model naïvely using literature values for ? and ?eff. RESULTS:When using naïve literature values, the model's mean DSC is 0.67 whereas the calibrated model produces 0.82 during cross-validation, an improvement of 0.15 in overlap with the patient data. The 95% confidence interval of the mean difference is 0.083-0.23 (p?

SUBMITTER: Fahrenholtz SJ 

PROVIDER: S-EPMC6295207 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature