Conclusions
During cross-validation, the calibrated model is superior to the naïve model as measured by DSC, with +22% mean prediction accuracy. Calibration empowers a relatively simple model to become more predictive.
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 (ω) and optical parameter (μeff) values, throughout a parameter space totalling 11 440 value-pairs. This represents a lookup table of μeff-ω pairs with the corresponding DSC value for each patient dataset. The μeff-ω 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 ω and μeff.
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.
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 < 0.001). Conclusions: During cross-validation, the calibrated model is superior to the naïve model as measured by DSC, with +22% mean prediction accuracy. Calibration empowers a relatively simple model to become more predictive.