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Purpose <p>To develop a mathematical framework to estimate the in silico A0 threshold based on the technical specifications of a specific ablation confirmation software package for thermal ablation of liver tumors that can then be used to identify the impact of different sources of error.</p> Methods <p>To estimate in silico A0 thresholds, we developed a simulation framework incorporating technical parameters and biological effects. Technical parameters were segmentation error, registration error, and slice thickness, and biological effects were tissue shrinkage and microscopic satellite lesions; these parameters and effects were all modeled using statistical distributions. For each permutation of parameters, a logistic regression was fitted to determine the observed MAM required to achieve ≥ 99% probability of true complete tumor coverage (i.e., the A0 threshold). The mathematical framework was integrated into a web application to estimate the A0 threshold and the reliability of the commonly used 5-mm A0 threshold based on several software performance characteristics.</p> Results <p>A total of 15,000,000 simulations (10,000 simulations × 1500 parameter permutations) were run and summarized. Tumor and ablation zone segmentation most greatly influenced the A0 threshold, with thresholds of 3.4 and 8.4 mm for 1- and 5-mm errors, whereas slice thickness had a relatively small effect, with A0 thresholds of 2.9 and 3.4 mm for thicknesses of 1 and 5 mm, respectively.</p> Conclusion <p>This framework provides a method to determine software-specific in silico A0 thresholds and evaluate the reliability of existing 5-mm criteria based on software performance metrics. The results further show that ablation confirmation software should have registration and segmentation errors of ≤ 3 mm to reliably use a 5-mm A0 threshold.</p>

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The effects of measurement errors on minimum ablative margins after thermal ablation of liver tumors: a simulation study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月14日

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  • Iwan Paolucci,
  • Jessica Albuquerque,
  • Noreen S. Siddiqi,
  • A. Kyle Jones,
  • Kristy K. Brock,
  • Bruno C. Odisio

摘要

Purpose

To develop a mathematical framework to estimate the in silico A0 threshold based on the technical specifications of a specific ablation confirmation software package for thermal ablation of liver tumors that can then be used to identify the impact of different sources of error.

Methods

To estimate in silico A0 thresholds, we developed a simulation framework incorporating technical parameters and biological effects. Technical parameters were segmentation error, registration error, and slice thickness, and biological effects were tissue shrinkage and microscopic satellite lesions; these parameters and effects were all modeled using statistical distributions. For each permutation of parameters, a logistic regression was fitted to determine the observed MAM required to achieve ≥ 99% probability of true complete tumor coverage (i.e., the A0 threshold). The mathematical framework was integrated into a web application to estimate the A0 threshold and the reliability of the commonly used 5-mm A0 threshold based on several software performance characteristics.

Results

A total of 15,000,000 simulations (10,000 simulations × 1500 parameter permutations) were run and summarized. Tumor and ablation zone segmentation most greatly influenced the A0 threshold, with thresholds of 3.4 and 8.4 mm for 1- and 5-mm errors, whereas slice thickness had a relatively small effect, with A0 thresholds of 2.9 and 3.4 mm for thicknesses of 1 and 5 mm, respectively.

Conclusion

This framework provides a method to determine software-specific in silico A0 thresholds and evaluate the reliability of existing 5-mm criteria based on software performance metrics. The results further show that ablation confirmation software should have registration and segmentation errors of ≤ 3 mm to reliably use a 5-mm A0 threshold.