Validation of a Web-Based Planning Tool for Percutaneous Cryoablation of Renal Tumors

Tim J. van Oostenbrugge*, Jan Heidkamp, Michael Moche, Phil Weir, Panchatcharam Mariappan, Ronan Flanigan, Mika Pollari, Stephen Payne, Marina Kolesnik, Sjoerd F.M. Jenniskens, Jurgen J. Fütterer

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Purpose: To validate a simulation environment for virtual planning of percutaneous cryoablation of renal tumors. Materials and Methods: Prospectively collected data from 19 MR-guided procedures were used for validation of the simulation model. Volumetric overlap of the simulated ablation zone volume (Σ) and the segmented ablation zone volume (S; assessed on 1-month follow-up scan) was quantified. Validation metrics were DICE Similarity Coefficient (DSC; the ratio between twice the overlapping volume of both ablation zones divided by the sum of both ablation zone volumes), target overlap (the ratio between the overlapping volume of both ablation zones to the volume of S; low ratio means S is underestimated), and positive predictive value (the ratio between the overlapping volume of both ablation zones to the volume of Σ; low ratio means S is overestimated). Values were between 0 (no alignment) and 1 (perfect alignment), a value > 0.7 is considered good. Results: Mean volumes of S and Σ were 14.8 cm3 (± 9.9) and 26.7 cm3 (± 15.0), respectively. Mean DSC value was 0.63 (± 0.2), and ≥ 0.7 in 9 cases (47%). Mean target overlap and positive predictive value were 0.88 (± 0.11) and 0.53 (± 0.24), respectively. In 17 cases (89%), target overlap was ≥ 0.7; positive predictive value was ≥ 0.7 in 4 cases (21%) and < 0.6 in 13 cases (68%). This indicates S is overestimated in the majority of cases. Conclusion: The validation results showed a tendency of the simulation model to overestimate the ablation effect. Model adjustments are necessary to make it suitable for clinical use.

Original languageEnglish
Pages (from-to)1661-1670
Number of pages10
JournalCardioVascular and Interventional Radiology
Volume43
Issue number11
DOIs
Publication statusPublished - 1 Nov 2020

Keywords

  • Computer-assisted image processing
  • Cryosurgery
  • Intraoperative monitoring
  • Kidney neoplasms
  • Preoperative care

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