Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland.
Siemens SRL, Brasov, Romania.
Int J Comput Assist Radiol Surg. 2022 Aug;17(8):1489-1496. doi: 10.1007/s11548-022-02689-x. Epub 2022 Jul 1.
Thermal ablation of liver tumors has emerged as a first-line curative treatment for single small tumors (diameter < 2.5 cm) due to similar overall survival rates as surgical resection. Moreover, it is far less invasive, has lower complication rates, a superior cost-effectiveness, and an extremely low treatment-associated mortality. However, in many cases, complete tumor coverage cannot be achieved only with a single electrode and several electrodes are used to create overlapping ablations. Multi-electrode planning is a challenging 3D task with many contradictive constraints to consider, a dimensionality difficult to assess even for experts. It requires extremely long planning time since it is mostly performed mentally by clinicians looking at 2D CT views. An accurate and reliable prediction of the ablation zone would help to turn thermal ablation into a first-line curative treatment also for large liver tumors treated with multiple electrodes. In order to determine the level of model simplification that can be acceptable, we compared three computational models, a simple spherical model, a biophysics-based model and an Eikonal model.
RF ablation electrodes were virtually placed at a desired position in the patient pre-operative CT image and the models predicted the ablation zone generated by multiple electrodes. The last two models are patient-specific. In these cases, hepatic structures were automatically segmented from the pre-operative CT images to predict a patient-specific ablation zone.
The three models were used to simulate multiple electrode ablations on 12 large tumors from 11 patients for which the procedure information was available. Biophysics-based simulations approximate better the post-operative ablation zone in term of Hausdorff distance, Dice Similarity Coefficient, radius, and volume compared to two other methods. It also predicts better the coverage percentage and thus the tumor ablation margin.
The results obtained with the biophysics-based model indicate that it could improve ablation planning by accurately predicting the ablation zone, avoiding over or under-treatment. This is particularly beneficial for multi-electrode radiofrequency ablation of larger liver tumors where the planning phase is particularly challenging.
由于与手术切除相比，肝肿瘤热消融术具有相似的总生存率，因此已成为单个小肿瘤（直径<2.5cm）的一线治疗方法。此外，它的侵袭性更小、并发症发生率更低、成本效益更高，且治疗相关死亡率极低。然而，在许多情况下，仅使用单个电极无法实现完全的肿瘤覆盖，因此需要使用多个电极来创建重叠消融。多电极规划是一项具有挑战性的 3D 任务，需要考虑许多相互矛盾的约束条件，即使对于专家来说也很难评估其维度。由于它主要是由临床医生通过查看 2D CT 视图在头脑中进行的，因此需要非常长的规划时间。对于通过多个电极治疗的大肝肿瘤，准确可靠的消融区域预测有助于将热消融术转变为一线治疗方法。为了确定可以接受的模型简化程度，我们比较了三种计算模型，即简单的球形模型、基于生物物理的模型和 Eikonal 模型。
在患者术前 CT 图像中，将虚拟 RF 消融电极放置在所需位置，然后使用模型预测多个电极产生的消融区。后两种模型是针对患者的。在这些情况下，从术前 CT 图像中自动分割肝结构，以预测患者特定的消融区。
使用这三种模型模拟了 11 名患者的 12 个大肿瘤的多个电极消融，这些患者的手术信息可用。与另外两种方法相比，基于生物物理的模拟在 Hausdorff 距离、Dice 相似系数、半径和体积方面更能近似术后消融区。它还能更好地预测覆盖百分比，从而预测肿瘤消融边界。
基于生物物理的模型的结果表明，它可以通过准确预测消融区来改善消融计划，避免过度或不足的治疗。这对于较大肝肿瘤的多电极射频消融尤其有益，因为在规划阶段具有很大的挑战性。
