Uncertainty-aware registration for probabilistic margin assessment in renal cell carcinoma ablation.

Quantitative assessment of renal cell carcinoma (RCC) ablation margins requires reliable registration of pre- and post-ablation images. However, ablation-related tissue changes weaken local anatomical correspondence, making deformation estimates potentially non-unique. We developed an uncertainty-aware registration framework for more reliable margin estimation.&#xD;Approach: An ensemble of plausible registrations was generated by stochastically sampling key registration settings, including the registration mask, similarity metric, B-spline grid spacing, and initial alignment, with Latin hypercube sampling used to improve parameter-space coverage. Registration uncertainty was characterized from this ensemble and propagated into probabilistic margin estimation. Evaluation used 25 biomechanically simulated cases with known deformation fields and 47 clinical RCC cases.&#xD;Main results: In simulation, 98 ± 2% of kidney voxels had a mean displacement error below 1 mm, and uncertainty was strongly associated with registration error (median case-wise Spearman correlation, 0.99). In the clinical study, our method achieved anatomical alignment consistent with the deterministic baselines, with a median kidney Dice similarity coefficient of 0.944 and a median local mean absolute surface distance of 0.5 mm. In general, our method maintained a sensitivity of 1.00 while achieving higher positive predictive value (PPV) than the deterministic methods. Under the global criterion, PPV was 0.70 versus 0.54-0.58, although the pairwise differences between our method and each deterministic method were not statistically significant. Under the focal criterion, PPV was 0.78 versus 0.29-0.50. The pairwise differences were significant between our method and the standard and penalty-constrained B-spline methods (both p<0.001), but not between our method and the diffeomorphic method (p=0.063). &#xD;Significance: The framework extends registration uncertainty estimation to RCC ablation-margin assessment by converting variability in plausible correspondences into probabilistic margin maps. It highlights negative-margin regions consistently identified across registrations, reduces registration-sensitive false-positive local tumor progression predictions, and may support more reliable postoperative evaluation.&#xD.