Computational modeling of microwave ablation of lung tumors: Assessment of model-predictions against post-treatment imaging

Background

Percutaneous microwave ablation is a clinically established method for treatment of unresectable lung nodules. When planning the intervention, the size of ablation zone, which should encompass the nodule as well as a surrounding margin of normal tissue, is predicted via manufacturer-provided geometric models, which do not consider patient-specific characteristics. However, the size and shape of ablation is dependent on tissue composition and properties and can vary between patients.

Purpose

To comparatively assess performance of a computational model-based approach and manufacturer geometric model for predicting extent of ablation zones following microwave lung ablation procedures on a retrospectively collected clinical imaging dataset.

Methods

A retrospective computed-tomography (CT) imaging dataset was assembled of 50 patients treated with microwave ablation of lung tumors at a single institution. For each case, the dataset consisted of a pre-procedure CT acquired without the ablation applicator, a peri-procedure CT scan with the ablation applicator in position, and post-procedure CT scan to assess the ablation zone extent acquired on the first follow-up visit. A physics-based computational model of microwave absorption and bioheat transfer was implemented using the finite element method, with the model geometry incorporating key tissue types within 2 cm of the applicator as informed by imaging data. The model-predicted extent of the ablation zone was estimated using the Arrhenius thermal damage model. The ablation zone predicted by the manufacturer geometric model consisted of an ellipsoid registered with the applicator position and dimensions provided by instructions for use documentation. Both ablation estimates were compared to ground truth ablation zone segmented from post-procedure CT via Dice similarity coefficient (DSC) and average absolute error (AAE). The statistically significant difference at level 0.05 in performance between both ablation prediction methods was tested with permutation test on DSC as well as AAE datasets with applied Bonferroni multiple-comparison correction.

Results

Receiver operating characteristic analysis of the predictive power of the volume of insufficient coverage (i.e. tumor volume which did not receive an ablative thermal dose) as an indicator of local tumor recurrence yielded an area under the curve of 0.84, illustrating the clinical significance of accurate prediction of ablation zone extents. Across all cases, AAEs were 3.65 ± 1.12 mm, and 5.11 ± 1.93 mm for patient-specific computational and manufacturer geometric models respectively. Similarly, average DSCs were 0.55 ± 0.14, and 0.46 ± 0.19 for computational and manufacturer geometric models respectively. The manufacturer geometric model overpredicted volume of the ablation zone compared to ground truth by 141% on average, whereas the patient-specific computational model overpredicted ablation zone volumes by 31.5% on average.

Conclusions

Patient-specific physics-based computational models of lung microwave ablation yield improved prediction of microwave ablation extent compared to the manufacturer geometric model.