Anatomical Modeling to Improve the Precision of Image Guided Liver Ablation

原发性和继发性肝癌的发病率正在增加，它们共同导致了 全球每年有100万人死亡。在可用于治疗肝癌的治疗方法中，手术切除 被认为是护理的标准。不幸的是，只有不到20%的患者有资格在 诊断的时间。图像引导经皮热消融(PTA)已成为一种广泛使用的选择 对于不符合手术条件的患者，局部控制成功率从55%到85%(4-6)。 为了达到PTA后的最佳效果，肿瘤残留或复发率应为 最小化(6，8)，这可以通过在肿瘤周围提供足够的最小消融边缘来实现。至 为了达到这一目标，拥有高质量的过程内成像是至关重要的，它提供了关于精确的信息 确定靶肿瘤的范围，确认消融探头在靶肿瘤的放置(S)，以及 准确的消融余量评估。目前，还没有商业上可用的工具来支持 考虑生物力学的肿瘤标测和消融评估的精确方法 与消融治疗相关的构象变化。 根据我们的初步工作，我们假设肝癌消融后局部肿瘤控制。 将通过应用专用解剖线弹性生物力学模型进行治疗而得到改善 通过实现对肿瘤的准确识别和靶向并提供 分别进行术中消融评估。这一假设将通过三个具体的 目标。首先，我们将在RayStation平台上对解剖建模肝脏消融导引进行优化， 肝线弹性生物力学模型在肝图应用中的准确性验证 将介入前图像上定义的肿瘤转移到消融前获得的术中图像上； 其次，我们将评估该模型对肝脏消融后局部肿瘤控制的影响。 第二阶段随机临床试验；最后，我们将优化生物力学模型，以便能够对 消融后肿瘤和周围正常组织的局部变化。 我们相信，集成准确、精确和高效的生物力学建模工具可以 在消融时确定肿瘤的位置并监测消融边缘将改善局部肿瘤 控制肝癌患者的死亡率，潜在地提高总体存活率。执行任务的能力 可变形图像配准以映射在介入前成像上识别的肿瘤 消融探头产生的伪影和肝脏内几乎没有对比度的伪影对 大多数基于强度的算法。在这一应用中使用基于生物力学的模型有望使 这是一个显著的影响，有可能使这种治疗失败的20%的患者能够进行局部控制。整合 将这项技术引入RayStation平台确保了这项技术在患者中的广泛应用。

Primary and secondary liver cancers are increasing in incidence and are collectively responsible for over 1 million deaths per year worldwide. Among the curative treatments available for liver cancers, surgical resection is considered the standard of care. Unfortunately, less than 20% of patients are eligible for such resection at the time of the diagnosis. Image-guided percutaneous thermal ablation (PTA) has become a widely utilized option for patients not eligible for surgery with local control success rates ranging from 55% to 85% (4-6). In order to achieve optimal results following PTA, rates of residual tumor or recurrence should be minimized (6, 8), which can be achieved by providing adequate minimal ablation margins around the tumor. To meet this goal, it is critical to have high-quality intra-procedurally imaging that offers information in respect precise definition of extent of the target tumor, confirmation of ablation probe placement at the target tumor(s), and accurate ablation margins assessment. Currently, there are no commercially available tools that enable an accurate method for tumor mapping and ablation assessment while taking in consideration biomechanical conformational changes associated with the ablation therapy. Based in our preliminary work, we hypothesize that local tumor control following ablation of liver cancers will be improved with the application of a dedicated anatomical linear elastic biomechanical model for treatment guidance and efficacy assessment by enabling accurate identification and targeting of the tumor and providing intra-procedural assessment of the ablation, respectively. This hypothesis will be tested through three specific aims. Firstly, we will optimize the anatomical modeling liver ablation guidance in the RayStation Platform by validating the accuracy of the linear elastic biomechanical models of the liver for the application of mapping the tumor defined on the pre-interventional images onto the intra-procedural images obtained just prior to ablation; Secondly, we will evaluate the impact of this model on local tumor control following liver ablation by conducting a phase II randomized clinical trial; Finally, we will optimize the biomechanical model to enable modeling of the local changes in the tumor and surrounding normal tissue resulting from the ablation. We believe that the integration of accurate, precise, and efficient biomechanical modeling tools to determine the tumor location at the time of ablation and to monitor the ablation margin will improve local tumor control rates in patients with liver cancers, potentially improving overall survival rates. The ability to perform deformable image registration to map the tumor, identified on pre-intervention imaging, in the presence of artifacts from the ablation probe and with little to no contrast within the liver presents a significant challenge to most intensity-based algorithms. The use of a biomechanical-based model in this application is poised to make a significant impact, potentially enabling local control for the 20% of patients who fail this therapy. The integration of this technology into the RayStation platform ensures that this technology is widely available to patients.