Dataset Information

Volumetric Quantitative Ablation Margins for Assessment of Ablation Completeness in Thermal Ablation of Liver Tumors.

ABSTRACT:

Background

In thermal ablation of liver tumors, complete coverage of the tumor volume by the ablation volume with a sufficient ablation margin is the most important factor for treatment success. Evaluation of ablation completeness is commonly performed by visual inspection in 2D and is prone to inter-reader variability. This work aimed to introduce a standardized approach for evaluation of ablation completeness after CT-guided thermal ablation of liver tumors, using volumetric quantitative ablation margins (QAM).

Methods

A QAM computation metric based on volumetric segmentations of tumor and ablation areas and signed Euclidean surface distance maps was developed, including a novel algorithm to address QAM computation in subcapsular tumors. The code for QAM computation was verified in artificial examples of tumor and ablation spheres simulating varying scenarios of ablation margins. The applicability of the QAM metric was investigated in representative cases extracted from a prospective database of colorectal liver metastases (CRLM) treated with stereotactic microwave ablation (SMWA).

Results

Applicability of the proposed QAM metric was confirmed in artificial and clinical example cases. Numerical and visual options of data presentation displaying substrata of QAM distributions were proposed. For subcapsular tumors, the underestimation of tumor coverage by the ablation volume when applying an unadjusted QAM method was confirmed, supporting the benefits of using the proposed algorithm for QAM computation in these cases. The computational code for developed QAM was made publicly available, encouraging the use of a standard and objective metric in reporting ablation completeness and margins.

Conclusion

The proposed volumetric approach for QAM computation including a novel algorithm to address subcapsular liver tumors enables precision and reproducibility in the assessment of ablation margins. The quantitative feedback on ablation completeness opens possibilities for intra-operative decision making and for refined analyses on predictability and consistency of local tumor control after thermal ablation of liver tumors.

SUBMITTER: Sandu RM

PROVIDER: S-EPMC7988092 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PURPOSE:The goal of this study was to develop and evaluate a volumetric three-dimensional (3D) approach to improve the accuracy of ablation margin assessment following thermal ablation of hepatic tumors. METHODS:The 3D margin assessment technique was developed to generate the new 3D assessment metrics: volumes of insufficient coverage (VICs) measuring volume of tissue at risk post-ablation. VICs were computed for the tumor and tumor plus theoretical 5- and 10-mm margins. The diagnostic accuracy of the 3D assessment to predict 2-year local tumor progression (LTP) was compared to that of manual 2D assessment using retrospective analysis of a patient cohort that has previously been reported as a part of an outcome-centered study. Eighty-six consecutive patients with 108 colorectal cancer liver metastases treated with radiofrequency ablation (2002-2012) were used for evaluation. The 2-year LTP discrimination power was assessed using receiver operating characteristic area under the curve (AUC) analysis. RESULTS:A 3D assessment of margins was successfully completed for 93 out of 108 tumors. The minimum margin size measured using the 3D method had higher discrimination power compared with the 2D method, with an AUC value of 0.893 vs. 0.790 (p?=?0.01). The new 5-mm VIC metric had the highest 2-year LTP discrimination power with an AUC value of 0.923 (p?=?0.004). CONCLUSIONS:Volumetric semi-automated 3D assessment of the ablation zone in the liver is feasible and can improve accuracy of 2-year LTP prediction following thermal ablation of hepatic tumors. KEY POINTS:• More accurate prediction of local tumor progression risk using volumetric 3D ablation zone assessment can help improve the efficacy of image-guided percutaneous thermal ablation of hepatic tumors. • The accuracy of evaluation of ablation zone margins after thermal ablation of colorectal liver metastases can be improved using a volumetric 3D semi-automated assessment approach and the volume of insufficient coverage assessment metric. • The new 5-mm volume-of-insufficient-coverage metric, indicating the volume of tumor plus 5-mm margin that remained untreated, had the highest 2-year local tumor progression discrimination power.

Project description:BackgroundThree-dimensional (3D) volumetric ablation margin assessment after thermal ablation of liver tumors using software has been described, but its predictive value on treatment efficacy when accounting for other factors known to correlate ablation site recurrence (ASR) remains unknown.PurposeTo investigate 3D quantitative ablation margins (3D-QAMs) as an algorithm to predict ASR within 1 year after stereotactic microwave ablation (SMWA) for colorectal liver metastases (CRLM).Materials and methodsSixty-five tumors in 47 patients from a prospective multicenter study of patients undergoing SMWA for CRLM were included in this retrospective 3D-QAM analysis. Using a previously developed algorithm, 3D-QAM defined as the distribution of tumor to ablation surface distances was assessed in co-registered pre- and post-ablation CT scans. The discriminatory power and optimal cutoff values for 3D-QAM were assessed using receiver operating characteristic (ROC) curves. Multivariable logistic regression analysis using generalized estimating equations was applied to investigate the impact of various 3D-QAM outputs on 1-year ASR while accounting for other known influencing factors.ResultsTen of the 65 (15.4%) tumors included for 3D-QAM analysis developed ASR. ROC analyses identified i) 3D-QAM <1 mm for >23% of the tumor surface, ii) 3D-QAM <5 mm for >45%, and iii) the minimal ablation margin (MAM) as the 3D-QAM outputs with optimal discriminatory qualities. The multivariable regression model without 3D-QAM yielded tumor diameter and KRAS mutation as 1-year ASR predictors. When adding 3D-QAM, this factor became the main predictor of 1-year ASR [odds ratio (OR) 21.67 (CI 2.48, 165.21) if defined as >23% <1 mm; OR 0.52 (CI 0.29, 0.95) if defined as MAM].Conclusions3D-QAM allows objectifiable and standardized assessment of tumor coverage by the ablation zone after SMWA. Our data shows that 3D-QAM represents the most important factor predicting ASR within 1 year after SMWA of CRLM.

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