Real-time monitoring radiofrequency ablation using tree-based ensemble learning models

Predicting spatio-temporal radiofrequency ablation temperature using deep neural networks

Radiofrequency ablation (RFA) of the medial branch nerve is a widely used therapeutic intervention for facet joint pain. However, denervation of the multifidus muscle is an inevitable consequence of RFA. New ablation techniques with the potential to prevent muscle denervation can be designed using computational simulations. However, depending on the complexity of the model, they could be computationally expensive. As an alternative approach, deep neural networks (DNNs) can be used to predict tissue temperature during RFA procedure. The objective of this paper is to predict the tissue spatial and temporal temperature distributions during RFA using DNNs. First, finite element (FE) models with a range of distances between the probes were run to obtain the temperature readings. The measured temperatures were then used to train the DNNs that predict the spatio-temporal temperature distribution within the tissue. Finally, a separate data obtained from FE simulations were used to test the efficacy of the network. The results presented in this paper demonstrate that the network can achieve an error rate as low as 0.05%, accompanied by a 92% reduction in time compared to FE simulations. The approach proposed in this study will play a major role in the design of new RFA treatments for facet joint pain.

A Novel Local Tumor Progression Prediction Method for Multimode Ablation Treatment

OBJECTIVE

The multimode ablation of liver cancer, which uses radio-frequency heating after a pre-freezing process to treat the tumor, has shown significantly improved therapeutic effects and enhanced anti-tumor immune response. Unlike open surgery, the ablated lesions remain in the body after treatment, so it is critical to assess the immediate outcome and to monitor disease status over time. Here we propose a novel tumor progression prediction method for simultaneous postoperative evaluation and prognosis analysis.

METHODS

We propose to leverage the intraoperative therapeutic information extracted from thermal dose distribution. For tumors with specific sensitivity reflected in medical images, different thermal doses implicitly indicate the degree of instant damage and long-term inhibition excited under specific ablation energy. We further propose a survival analysis framework for the multimode ablation treatment. It extracts carefully designed features from clinical, preoperative, intraoperative, and postoperative data, then uses random survival forest for feature selection and deep neural networks for survival prediction.

RESULTS

We evaluated the proposed methods using clinical data. The results show that our method outperforms the state-of-the-art survival analysis methods with a C-index of 0.8550.090. The thermal dose information contributes significantly to the prediction accuracy by taking up 21.7% of the overall feature importance.

CONCLUSION

The proposed methods have been demonstrated to be a powerful tool in tumor progression prediction of multimode ablation therapy.

SIGNIFICANCE

This kind of data-driven prognosis analysis may benefit personalized medicine and simplify the follow-up process.

Domain Heterogeneity in Radiofrequency Therapies for Pain Relief: A Computational Study with Coupled Models

The objective of the current research work is to study the differences between the predicted ablation volume in homogeneous and heterogeneous models of typical radiofrequency (RF) procedures for pain relief. A three-dimensional computational domain comprising of the realistic anatomy of the target tissue was considered in the present study. A comparative analysis was conducted for three different scenarios: (a) a completely homogeneous domain comprising of only muscle tissue, (b) a heterogeneous domain comprising of nerve and muscle tissues, and (c) a heterogeneous domain comprising of bone, nerve and muscle tissues. Finite-element-based simulations were performed to compute the temperature and electrical field distribution during conventional RF procedures for treating pain, and exemplified here for the continuous case. The predicted results reveal that the consideration of heterogeneity within the computational domain results in distorted electric field distribution and leads to a significant reduction in the attained ablation volume during the continuous RF application for pain relief. The findings of this study could provide first-hand quantitative information to clinical practitioners about the impact of such heterogeneities on the efficacy of RF procedures, thereby assisting them in developing standardized optimal protocols for different cases of interest.

Thermal ablation of biological tissues in disease treatment: A review of computational models and future directions

Percutaneous thermal ablation has proven to be an effective modality for treating both benign and malignant tumours in various tissues. Among these modalities, radiofrequency ablation (RFA) is the most promising and widely adopted approach that has been extensively studied in the past decades. Microwave ablation (MWA) is a newly emerging modality that is gaining rapid momentum due to its capability of inducing rapid heating and attaining larger ablation volumes, and its lesser susceptibility to the heat sink effects as compared to RFA. Although the goal of both these therapies is to attain cell death in the target tissue by virtue of heating above 50°C, their underlying mechanism of action and principles greatly differs. Computational modelling is a powerful tool for studying the effect of electromagnetic interactions within the biological tissues and predicting the treatment outcomes during thermal ablative therapies. Such a priori estimation can assist the clinical practitioners during treatment planning with the goal of attaining successful tumour destruction and preservation of the surrounding healthy tissue and critical structures. This review provides current state-of-the-art developments and associated challenges in the computational modelling of thermal ablative techniques, viz., RFA and MWA, as well as touch upon several promising avenues in the modelling of laser ablation, nanoparticles assisted magnetic hyperthermia and non-invasive RFA. The application of RFA in pain relief has been extensively reviewed from modelling point of view. Additionally, future directions have also been provided to improve these models for their successful translation and integration into the hospital work flow.

Early and Late Fusion Machine Learning on Multi-Frequency Electrical Impedance Data to Improve Radiofrequency Ablation Monitoring

Radiofrequency ablation (RFA) is a popular modality for tumor treatment. However, inexpensive real-time monitoring of RFA within multiple tissue types is still an ongoing research topic. The objective of this study is to utilize multi-frequency electrical impedance data within real-time RFA depth estimation through data fusion schemes that include non-linear machine learning (ML) models. Multi-frequency tissue complex electrical impedance measurements are used to provide input data to the data fusion schemes. Our results show that the fusion schemes significantly decrease both the spread of residuals and the mean of the residuals for depth estimation. Thus, data fusion can be a significant tool for use in improving the performance of ML-based monitoring for RFA.

Real-Time Radiofrequency Ablation Lesion Depth Estimation Using Multi-frequency Impedance With a Deep Neural Network and Tree-Based Ensembles

OBJECTIVE

Design and optimization of statistical models for use in methods for estimating radiofrequency ablation (RFA) lesion depths in soft real-time performance.

METHODS

Using tissue multi-frequency complex electrical impedance data collected from a low-cost embedded system, a deep neural network (NN) and tree-based ensembles (TEs) were trained for estimating the RFA lesion depth via regression.

RESULTS

Addition of frequency sweep data, previous depth data, and previous RF power state data boosted accuracy of the statistical models. The root mean square errors were 2 mm for NN and 0.5 mm for TEs for previous statistical models and the root mean square errors were 0.4 mm for NN and 0.04 mm for TEs for the statistical models presented in this paper. Simulation ablation performance showed a mean difference against physical measurements of 0.5 ±0.2 mm for the NN-based depth estimation method and 0.7 ±0.4 mm for the TE-based depth estimation method.

CONCLUSION

The results show that multi-frequency data significantly improves the depth estimation performance of the statistical models.

SIGNIFICANCE

The RFA lesion depth estimation methods presented in this work achieve millimeter-resolution accuracy with soft real-time performance on an ARMv7-based embedded system for potential translation to clinical RFA technologies.