Development of a Searchable Database of Cryoablation Simulations for Use in Treatment Planning

Boas, F. Edward; Srimathveeravalli, Govindarajan; Durack, Jeremy C.; Kaye, Elena; Erinjeri, Joseph P.; Ziv, Etay; Maybody, Majid; Yarmohammadi, Hooman; Solomon, Stephen B.

doi:10.1007/s00270-016-1562-4

Cited by 14 publications

(2 citation statements)

References 19 publications

(20 reference statements)

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“…Model-based thermal ablation planning is a promising approach to assist the radiologist with the procedure, providing essential information on treatment settings. This has been shown in several studies for cryoablation, radiofrequency (RF), microwave (MW) and laser ablation in (pre-)clinical scenarios [11][12][13][14][15][16][17][18] . In this study, a biophysical model of thermal ablation is integrated in a prototype software which processes magnetic resonance imaging (MRI), or computed tomography (CT) imaging acquired during the regular clinical workflow.…”

Section: Introductionmentioning

confidence: 78%

Model-based hepatic percutaneous microwaveablation planning. First validation on a clinical dataset

Frackowiak

Bosch

Tokoutsi

et al. 2023

Preprint

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A model-based planning tool, integrated in an imaging system, is envisioned for CT-guided percutaneous microwave ablation. This study aims to evaluate the biophysical model performance, by comparing its prediction retrospectively with the actualablation ground truth from a clinical data set in liver. The biophysical model uses a simplified formulation of heat depositionon the applicator and a heat sink related to vasculature to solve the bioheat equation. A performance metric is defined toassess how the planned ablation overlaps the actual ground truth. Results demonstrate superiority of this model predictioncompared to manufacturer tabulated data and a significant influence of the vasculature cooling effect. Nevertheless, vasculatureshortage due to branches occlusion and applicator misalignment due to registration error between scans affects the thermalprediction. With a more accurate vasculature segmentation, occlusion risk can be estimated, whereas branches can be usedas liver landmarks to improve the registration accuracy. Overall, this study emphasizes the benefit of a model-based thermalablation solution in better planning the ablation procedures. Contrast and registration protocols must be adapted to facilitate itsintegration into the clinical workflow.

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Section: Introductionmentioning

confidence: 78%

Model-based hepatic percutaneous microwaveablation planning. First validation on a clinical dataset

Frackowiak

Bosch

Tokoutsi

et al. 2023

Preprint

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“…However, the preoperative estimates of the iceball size during the freezing cycle remain dependent on the physician's experience and hardly attain accurate predictions. The instructions for a certain cryoprobe, typically the reference for doctors, can only offer a rough size of the iceball generated from the single probe at a speci c timepoint [30] .…”

Section: Discussionmentioning

confidence: 99%

Iceball Growth 3D Simulation Model Based on Finite Element Method for Hepatic Cryoablation Planning

Li,

Zhang,

Zhou

et al. 2024

Preprint

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Background: Cryoablation simulation based on finite element method (FEM) can facilitate preoperative planning for liver tumors. However, it has limited application in clinical practice due to its time-consuming process and improvable accuracy. We aimed to propose a FEM-based simulation model for rapid and accurate prediction of the iceball size during the hepatic cryofreezing cycle. Methods: A 3D simulation model was presented to predict the iceball size (frozen isotherm boundaries) in biological liver tissues undergoing cryofreezing based on the Pennes bioheat equation. The simulated results for three cryoprobe types were evaluated in the ex vivo porcine livers and clinical data. In ex vivo experiments, CT-based measurements of iceball size were fitted as growth curves and compared to the simulated results. Eight patient cases of CT-guided percutaneous hepatic cryoablation procedures were retrospectively collected for clinical validation. The Dice Score Coefficient (DSC) and Hausdorff distance (HD) were used to measure the similarity between simulation and ground truth segmentation. Results: The measurements in the ex vivo experiments showed a close similarity between the simulated and experimental iceball growth curves for three cryoprobe models, with all mean absolute error＜2.9mm and coefficient of determination＞0.85.In the clinical validation, the simulation model achieved high accuracy with a DSC of 0.87±0.03 and a HD of 2.0±0.4mm. The average computational time was 23.2 seconds for all simulations. Conclusion Our simulation model achieves accurate iceball size predictions within a short time during hepatic cryoablation and potentially allows for the implementation of the preoperative cryoablation planning system.

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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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Development of a Searchable Database of Cryoablation Simulations for Use in Treatment Planning

Cited by 14 publications

References 19 publications

Model-based hepatic percutaneous microwaveablation planning. First validation on a clinical dataset

Model-based hepatic percutaneous microwaveablation planning. First validation on a clinical dataset

Iceball Growth 3D Simulation Model Based on Finite Element Method for Hepatic Cryoablation Planning

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

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