Real-time monitoring radiofrequency ablation using tree-based ensemble learning models
Real-time monitoring radiofrequency ablation using tree-based ensemble learning models
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DOI：
10.1080/02656736.2019.1587008
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发表时间：
2019-01
影响因子：
3.1
通讯作者：
E. Besler;Y. Curtis Wang;Terence C Chan;Alan V Sahakian
中科院分区：
文献类型：
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作者：
E. Besler;Y. Curtis Wang;Terence C Chan;Alan V Sahakian
摘要背景：射频消融是一种微创治疗方法，旨在通过将不需要的组织暴露于100 kHz-800 kHz频率范围内的交流电并对其进行加热，直到通过凝固性坏死将其破坏。消融治疗正在获得动力，特别是在癌症研究中，其中不需要的组织是恶性肿瘤。虽然用电极或导管消融肿瘤是一项简单的任务，但为了保持治疗的可靠性，必须实时监测消融过程。用于该监测任务的常用方法已被证明是准确的，然而，它们都是耗时的或需要昂贵的设备，由于临床程序的时间依赖性，这使得临床消融过程更加繁琐和昂贵。方法：提出了一种机器学习（ML）方法，旨在减少监测时间，同时保持传统方法的准确性。使用两种不同的硬件设置来执行消融并同时收集阻抗数据，并测试不同的ML算法，以基于收集的数据预测三维消融深度。结果如下：随机森林和自适应增强（adaboost）模型在使用基于嵌入式系统的硬件仪器设置收集的数据上的R2均超过98%，优于基于神经网络的模型。结论：结果表明，在×86-64工作站上，最佳硬件设置和ML算法（Adaboost）能够通过估计损伤深度（测试平均值为0.3mm）控制消融，同时将估计时间保持在10 ms以内。
Abstract Background: Radiofrequency ablation is a minimally-invasive treatment method that aims to destroy undesired tissue by exposing it to alternating current in the 100 kHz–800 kHz frequency range and heating it until it is destroyed via coagulative necrosis. Ablation treatment is gaining momentum especially in cancer research, where the undesired tissue is a malignant tumor. While ablating the tumor with an electrode or catheter is an easy task, real-time monitoring the ablation process is a must in order to maintain the reliability of the treatment. Common methods for this monitoring task have proven to be accurate, however, they are all time-consuming or require expensive equipment, which makes the clinical ablation process more cumbersome and expensive due to the time-dependent nature of the clinical procedure. Methods: A machine learning (ML) approach is presented that aims to reduce the monitoring time while keeping the accuracy of the conventional methods. Two different hardware setups are used to perform the ablation and collect impedance data at the same time and different ML algorithms are tested to predict the ablation depth in 3 dimensions, based on the collected data. Results: Both the random forest and adaptive boosting (adaboost) models had over 98% R2 on the data collected with the embedded system-based hardware instrumentation setup, outperforming Neural Network-based models. Conclusions: It is shown that an optimal pair of hardware setup and ML algorithm (Adaboost) is able to control the ablation by estimating the lesion depth within a test average of 0.3mm while keeping the estimation time within 10ms on a ×86–64 workstation.