Ablation Planning Software for Optimizing Treatment: Challenges, Techniques, and Applications

Lyons, Gray R.; Pua, Bradley B.

doi:10.1053/j.tvir.2018.10.005

Cited by 19 publications

(15 citation statements)

References 25 publications

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“…Although the computational prediction of ice ball formation is complex, and mathematical models for prediction are continuously being improved, several planning tools for clinical use have been developed [ 9 ]. Boas et al developed and validated a planning tool for multiple-probe cryoablation [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

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

Oostenbrugge

Heidkamp

Moche

et al. 2020

Cardiovasc Intervent Radiol

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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.

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Section: Discussionmentioning

confidence: 99%

“…Currently, there is limited software available for interventional radiologists to plan the procedure and to predict procedural outcomes [ 9 ]. Obtaining full tumor coverage with enough margin is imperative and depends on the type, configuration, and the number of needles used.…”

Section: Introductionmentioning

confidence: 99%

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

Oostenbrugge

Heidkamp

Moche

et al. 2020

Cardiovasc Intervent Radiol

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“…[45][46][47] Moreover, ablation simulation tools have gradually offered better predictive models of ablation zones, thereby improving preprocedural planning of adequate ablation and avoidance of procedural complications. 48 For example, radiofrequency ablation of liver tumors using preprocedural 3D renderings of patients' livers resulted in accurate prediction of the size and shape of ablation zones. 49…”

Section: Procedural Planningmentioning

confidence: 99%

The Value of Simulation in Interventional Radiology

Barnett

Pua

2020

Dig Dis Interv

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Medicine has increasingly incorporated simulation training amidst improving simulation technologies and evolving educational perspectives. This article examines the broad concept of simulation in medicine to focus, in particular, on its multifaceted value for providers and patients within the field of interventional radiology. Simulation technology ranges in degrees of fidelity, with new advancements notably in high-fidelity endovascular simulation, as well as in augmented and virtual reality. Current evidence on the validity of simulation within interventional radiology is promising, spanning training, credentialing, preprocedural planning, and team-building applications.

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“…[6][7][8][9][10] Recently, there has been growing interest in the development of computational models for guiding planning of clinical ablation procedures, and for assessing treatment outcome. 11 Experimental validation of MWA computational models is important to provide confidence in model predictions [12][13][14][15][16] and may contribute to increased use of modeling tools in the clinical setting. Although the ultimate application of MWA technology is in pathologic tissue in the in vivo setting, extensive validation in the controlled ex vivo tissue environment represents an important first step for establishing model credibility.…”

Section: Introductionmentioning

confidence: 99%

“…Models are also used for comparative assessment of energy delivery strategies 5 and for assessing the impact of various sources of uncertainty on ablation outcome 6–10 . Recently, there has been growing interest in the development of computational models for guiding planning of clinical ablation procedures, and for assessing treatment outcome 11 . Experimental validation of MWA computational models is important to provide confidence in model predictions 12–16 and may contribute to increased use of modeling tools in the clinical setting.…”

Section: Introductionmentioning

confidence: 99%

Experimental assessment of microwave ablation computational modeling with MR thermometry

et al. 2020

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Computational models are widely used during the design and characterization of microwave ablation (MWA) devices, and have been proposed for pretreatment planning. Our objective was to assess three-dimensional (3D) transient temperature and ablation profiles predicted by MWA computational models with temperature profiles measured experimentally using magnetic resonance (MR) thermometry in ex vivo bovine liver. Materials and methods: We performed MWA in ex vivo tissue under MR guidance using a custom, 2.45 GHz water-cooled applicator. MR thermometry data were acquired for 2 min prior to heating, during 5-10 min microwave exposures, and for 3 min following heating. Fiber-optic temperature sensors were used to validate the accuracy of MR temperature measurements. A total of 13 ablation experiments were conducted using 30-50 W applied power at the applicator input. MWA computational models were implemented using the finite element method, and incorporated temperature-dependent changes in tissue physical properties. Model-predicted ablation zone extents were compared against MRI-derived Arrhenius thermal damage maps using the Dice similarity coefficient (DSC). Results: Prior to heating, the observed standard deviation of MR temperature data was in the range of 0.3-0.7°C. Mean absolute error between MR temperature measurements and fiber-optic temperature probes during heating was in the range of 0.5-2.8°C. The mean DSC between model-predicted ablation zones and MRI-derived Arrhenius thermal damage maps for 13 experimental setups was 0.95. When comparing simulated and experimentally (i.e. using MRI) measured temperatures, the mean absolute error (MAE %) relative to maximum temperature change was in the range 5%-8.5%. Conclusion: We developed a system for characterizing 3D transient temperature and ablation profiles with MR thermometry during MWA in ex vivo liver tissue, and applied the system for experimental validation of MWA computational models.

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Ablation Planning Software for Optimizing Treatment: Challenges, Techniques, and Applications

Cited by 19 publications

References 25 publications

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

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

The Value of Simulation in Interventional Radiology

Experimental assessment of microwave ablation computational modeling with MR thermometry

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