Boundary Distance Research Articles

Axisymmetric oscillatory modes in cylindrical magnetized plasma bounded by a conducting wall

A comparison between analytic theory and numerical simulations of axisymmetric modes in magnetically confined, cylindrical plasma with non-circular cross-section bounded by a conducting wall is presented. If the wall is close to the plasma, modes are oscillatory, with frequency scaling with the Alfvén frequency. The two frequencies differ when the plasma cross-section is elongated, but they become equal in the circular limit. The mechanism for oscillatory behavior is a consequence of the currents induced on the perfectly conducting wall when the plasma is displaced from its equilibrium positions. The induced currents exert a restoring force on the plasma, and the oscillation frequency is a combination of the strength of this force and the plasma mass density. An additional parameter, depending on the distance between the wall and the plasma boundary, also affects the oscillation frequency so that the frequency becomes large when the wall-plasma boundary distance approaches zero.

Methods to minimise effective public dose in the event of an accident at a fusion power plant

An investigation was conducted into minimising effective dose to members of the public in the unlikely event of an accident by optimising design parameters and site locations of future fusion power plants. This is part of the defence in depth approach for tritium safety that also includes significant work on the prevention of accidents. Calculations were performed using Atmospheric Dispersion Modelling System (ADMS 5), a validated software package that models plume dispersion with inputs including buildings, stack height, terrain topography and meteorological data. To decrease the effective dose to on-site workers and the public, stack height, release duration and site boundary should be maximised. The minimum recommended stack height and site boundary distance are 60 m and 250 m, respectively. It was also found that very unstable weather conditions that cause enhanced vertical mixing, Pasquill-Gifford Stability Class A, minimise effective dose to members of the public. The impact of these changes on effective doses are demonstrated quantitatively. Analysis of tritium dispersion and dose rates provides upper bounds on releasable tritium inventories that can be stored on site. It is envisaged that future fusion power plants such as DEMO can use this information when designing and choosing a location for their site.

Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances

Similarity search is a core analytical task, and its performance critically depends on the choice of distance measure. For time-series querying, elastic measures achieve state-of-the-art accuracy but are computationally expensive. Thus, fast lower bounding (LB) measures prune unnecessary comparisons with elastic distances to accelerate similarity search. Despite decades of attention, there has never been a study to assess the progress in this area. In addition, the research has disproportionately focused on one popular elastic measure, while other accurate measures have received little or no attention. Therefore, there is merit in developing a framework to accumulate knowledge from previously developed LBs and eliminate the notoriously challenging task of designing separate LBs for each elastic measure. In this paper, we perform the first comprehensive study of 11 LBs spanning 5 elastic measures using 128 datasets. We identify four properties that constitute the effectiveness of LBs and propose the Generalized Lower Bounding (GLB) framework to satisfy all desirable properties. GLB creates cache-friendly summaries, adaptively exploits summaries of both query and target time series, and captures boundary distances in an unsupervised manner. GLB outperforms all LBs in speedup (e.g., up to 13.5× faster against the strongest LB in terms of pruning power), establishes new state-of-the-art results for the 5 elastic measures, and provides the first LBs for 2 elastic measures with no known LBs. Overall, GLB enables the effective development of LBs to facilitate fast similarity search.

Development and clinical utility analysis of a prostate zonal segmentation model on T2-weighted imaging: a multicenter study

ObjectivesTo automatically segment prostate central gland (CG) and peripheral zone (PZ) on T2-weighted imaging using deep learning and assess the model’s clinical utility by comparing it with a radiologist annotation and analyzing relevant influencing factors, especially the prostate zonal volume.MethodsA 3D U-Net-based model was trained with 223 patients from one institution and tested using one internal testing group (n = 93) and two external testing datasets, including one public dataset (ETDpub, n = 141) and one private dataset from two centers (ETDpri, n = 59). The Dice similarity coefficients (DSCs), 95th Hausdorff distance (95HD), and average boundary distance (ABD) were calculated to evaluate the model’s performance and further compared with a junior radiologist’s performance in ETDpub. To investigate factors influencing the model performance, patients’ clinical characteristics, prostate morphology, and image parameters in ETDpri were collected and analyzed using beta regression.ResultsThe DSCs in the internal testing group, ETDpub, and ETDpri were 0.909, 0.889, and 0.869 for CG, and 0.844, 0.755, and 0.764 for PZ, respectively. The mean 95HD and ABD were less than 7.0 and 1.3 for both zones. The U-Net model outperformed the junior radiologist, having a higher DSC (0.769 vs. 0.706) and higher intraclass correlation coefficient for volume estimation in PZ (0.836 vs. 0.668). CG volume and Magnetic Resonance (MR) vendor were significant influencing factors for CG and PZ segmentation.ConclusionsThe 3D U-Net model showed good performance for CG and PZ auto-segmentation in all the testing groups and outperformed the junior radiologist for PZ segmentation. The model performance was susceptible to prostate morphology and MR scanner parameters.

Real-time liver tumor localization via combined surface imaging and a single x-ray projection

Objective. Real-time imaging, a building block of real-time adaptive radiotherapy, provides instantaneous knowledge of anatomical motion to drive delivery adaptation to improve patient safety and treatment efficacy. The temporal constraint of real-time imaging (<500 milliseconds) significantly limits the imaging signals that can be acquired, rendering volumetric imaging and 3D tumor localization extremely challenging. Real-time liver imaging is particularly difficult, compounded by the low soft tissue contrast within the liver. We proposed a deep learning (DL)-based framework (Surf-X-Bio), to track 3D liver tumor motion in real-time from combined optical surface image and a single on-board x-ray projection. Approach. Surf-X-Bio performs mesh-based deformable registration to track/localize liver tumors volumetrically via three steps. First, a DL model was built to estimate liver boundary motion from an optical surface image, using learnt motion correlations between the respiratory-induced external body surface and liver boundary. Second, the residual liver boundary motion estimation error was further corrected by a graph neural network-based DL model, using information extracted from a single x-ray projection. Finally, a biomechanical modeling-driven DL model was applied to solve the intra-liver motion for tumor localization, using the liver boundary motion derived via prior steps. Main results. Surf-X-Bio demonstrated higher accuracy and better robustness in tumor localization, as compared to surface-image-only and x-ray-only models. By Surf-X-Bio, the mean (±s.d.) 95-percentile Hausdorff distance of the liver boundary from the ‘ground-truth’ decreased from 9.8 (±4.5) (before motion estimation) to 2.4 (±1.6) mm. The mean (±s.d.) center-of-mass localization error of the liver tumors decreased from 8.3 (±4.8) to 1.9 (±1.6) mm. Significance. Surf-X-Bio can accurately track liver tumors from combined surface imaging and x-ray imaging. The fast computational speed (<250 milliseconds per inference) allows it to be applied clinically for real-time motion management and adaptive radiotherapy.

DomBpred: Protein Domain Boundary Prediction Based on Domain-Residue Clustering Using Inter-Residue Distance.

Domain boundary prediction is one of the most important problems in the study of protein structure and function, especially for large proteins. At present, most domain boundary prediction methods have low accuracy and limitations in dealing with multi-domain proteins. In this study, we develop a sequence-based protein domain boundary prediction, named DomBpred. In DomBpred, the input sequence is first classified as either a single-domain protein or a multi-domain protein through a designed effective sequence metric based on a constructed single-domain sequence library. For the multi-domain protein, a domain-residue clustering algorithm inspired by Ising model is proposed to cluster the spatially close residues according inter-residue distance. The unclassified residues and the residues at the edge of the cluster are then tuned by the secondary structure to form potential cut points. Finally, a domain boundary scoring function is proposed to recursively evaluate the potential cut points to generate the domain boundary. DomBpred is tested on a large-scale test set of FUpred comprising 2549 proteins. Experimental results show that DomBpred better performs than the state-of-the-art methods in classifying whether protein sequences are composed by single or multiple domains, and the Matthew's correlation coefficient is 0.882. Moreover, on 849 multi-domain proteins, the domain boundary distance and normalised domain overlap scores of DomBpred are 0.523 and 0.824, respectively, which are 5.0% and 4.2% higher than those of the best comparison method, respectively. Comparison with other methods on the given test set shows that DomBpred outperforms most state-of-the-art sequence-based methods and even achieves better results than the top-level template-based method. The executable program is freely available at https://github.com/iobio-zjut/DomBpred and the online server at http://zhanglab-bioinf.com/DomBpred/.

A Novel Model of Thyroid Nodule Segmentation for Ultrasound Images.

Ultrasonography is regarded as an effective technique for the detection, diagnosis and monitoring of thyroid nodules. Segmentation of thyroid nodules on ultrasound images is important in clinical practice. However, because in ultrasound images there is an unclear boundary between thyroid nodules and surrounding tissues, the accuracy of segmentation remains a challenge. Although the deep learning model provides an accurate and convenient method for thyroid nodule segmentation, it is unsatisfactory of the existing model in segmenting the margin of thyroid nodules. In this study, we developed boundary attention transformer net (BTNet), a novel segmentation network with a boundary attention mechanism combining the advantages of a convolutional neural network and transformer, which could fuse the features of both long and short ranges. Boundary attention is improved to focus on learning the boundary information, and this module enhances the segmentation ability of the network boundary. For features of different scales, we also incorporate a deep supervision mechanism to blend the outputs of different levels to enhance the segmentation effect. As the BTNet model incorporates the long range-short range connectivity effect and the boundary-regional cooperation capability, our model has excellent segmentation performance in thyroid nodule segmentation. The development of BTNet was based on the data set from Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital and the public data set. BTNet achieved good performance in the segmentation of thyroid nodules with an intersection-over-union of 0.810 and Dice coefficient of 0.892 Moreover, our work revealed great improvement in the boundary metrics; for example, the boundary distance was 7.308, the boundary overlap 0.201 and the boundary Dice 0.194, all with p values <0.05.

Demarcating Z-line and Gastric Folds Boundary Based on the Segmentation of the Lower Esophageal Sphincter Images.

The endoscopic diagnosis of pathological changes in the gastroesophageal junction including esophagitis and Barrett's mucosa is based on the visual detection of two boundaries: mucosal color change between esophagus and stomach, and top endpoint of gastric folds. The presence and pattern of mucosal breaks in the gastroesophageal mucosal junction (Z line) classify esophagitis in patients and the distance between the two boundaries points to the possible columnar lined epithelium. Since visual detection may suffer from intra- and interobserver variability, our objective was to define the boundaries automatically based on image processing algorithms, which may enable us to measure the detentions of changes in future studies. To demarcate the Z-line, first the artifacts of endoscopy images are eliminated. In the second step, using SUSAN edge detector, Mahalanobis distance criteria, and Gabor filter bank, an initial contour is estimated for the Z-line. Using region-based active contours, this initial contour converges to the Z-line. Finally, by applying morphological operators and Gabor Filter Bank to the region inside of the Z-line, gastric folds are segmented. To evaluate the results, a database consisting of 50 images and their ground truths were collected. The average dice coefficient and mean square error of Z-line segmentation were 0.93 and 3.3, respectively. Furthermore, the average boundary distance criteria are 12.3 pixels. In addition, two other criteria that compare the segmentation of folds with several ground truths, i.e., Sweet-Spot Coverage and Jaccard Index for Golden Standard, are 0.90 and 0.84, respectively. Considering the results, automatic segmentation of Z-line and gastric folds are matched to the ground truths with appropriate accuracy.

Theoretical study of the dynamic response of a functionally graded hollow cylinder in a half space subjected to plane SH waves

An analytical solution is derived for the dynamic response of a functionally graded hollow cylinder in a half space under incident plane SH waves. The shear modulus and shear wave velocity of the functionally graded hollow cylinder are assumed to have a power-law variation with respect to radius. Based on the wave function expansion method and the image technique, the wave fields with unknown coefficients in the hollow cylinder and the half space are constructed in corresponding polar coordinates. After expressing the scattered wave fields induced by the hollow cylinder and its mirror in one coordinate system by the Graf’s addition formula, the coefficients of the wave fields for both regions are obtained by taking advantage of the displacement and traction boundary conditions. Through a systemic parametric analysis, the effect of the inhomogeneity exponent and the boundary distance on the dynamic stress concentration factor of the functionally graded hollow cylinder is illustrated.

Synchronization and balancing around simple closed polar curves with bounded trajectories

The problem of synchronization and balancing around simple closed polar curves is addressed for unicycle-type multi-agent systems. Leveraging the concept of barrier Lyapunov function in conjunction with bounded Lyapunov-like curve-phase potential functions, we propose distributed feedback control laws and show that the agents asymptotically stabilize to the desired closed curve in synchronized and balanced curve-phase patterns, and their trajectories remain bounded within a compact set. Our control design methodology is based on the proposition of two models, namely the parametric-phase control model and the curve-phase control model. We also characterize the trajectory-constraining set based on the magnitude of the safe distance of the exterior boundary from the desired curve. We further establish a connection between the perimeters and areas of the trajectory-constraining set with the perimeter and area of the desired curve. We obtain bounds on different quantities of interest in the post-design analysis and provide simulation results to illustrate the theoretical findings.

Determination of a compact Finsler manifold from its boundary distance map and an inverse problem in elasticity

Determination of a compact Finsler manifold from its boundary distance map and an inverse problem in elasticity

Analytical model of hydraulic fracturing horizontal well gas production capacity of a water-bearing tight sandstone reservoir considering planar heterogeneity

Previous models of the gas-production rate of hydraulic fracturing horizontal wells were by assuming permeability heterogeneity, which is unrealistic in water-bearing tight gas reservoirs because of sandstone–mudstone crossover. Therefore, we develop an analytical model to describe the gas-production dynamics of hydraulic fracturing horizontal wells that consider permeability heterogeneity. In addition, threshold pressure gradient, stress sensitivity, and slippage are incorporated into the model. To solve this model analytically, the elliptical flow is transformed to radial flow by conformal transformation. The gas-production rate, reservoir pressure distribution, and average formation pressure are obtained by superposition principle, boundary pressures are calculated by material balance method, and the dynamic supply boundary propagation is modeled by steady-state sequential replacement. Actual field production data from Ordos Basin, China, is used to verify the new model, which increases the accuracy by 11.3% over previous models (98.6% vs 87.3%). The propagation distance of the dynamic supply boundary is predicted (in the fracture direction, it is 109.3 m, and in the vertical fracture direction, it is 44.2 m). We analyze how stress sensitivity, the dynamic threshold pressure gradient, matrix permeability, pressure difference, and initial water saturation affect gas production rate and dynamic supply boundary. Based on orthogonal experimentation, the factors affecting the gas production rate and dynamic supply boundary of tight gas reservoirs can be ranked in the following order: pressure difference &amp;gt; permeability &amp;gt; initial water saturation. This analytical model can accurately characterize gas production and pressure response, and it is easy to use and rapid to calculate.

Convergence in determining enzyme functional descriptors across Kemp eliminase variants

Molecular simulations have been extensively employed to accelerate biocatalytic discoveries. Enzyme functional descriptors derived from molecular simulations have been leveraged to guide the search for beneficial enzyme mutants. However, the ideal active-site region size for computing the descriptors over multiple enzyme variants remains untested. Here, we conducted convergence tests for dynamics-derived and electrostatic descriptors on 18 Kemp eliminase variants across six active-site regions with various boundary distances to the substrate. The tested descriptors include the root-mean-square deviation of the active-site region, the solvent accessible surface area ratio between the substrate and active site, and the projection of the electric field (EF) on the breaking C–H bond. All descriptors were evaluated using molecular mechanics methods. To understand the effects of electronic structure, the EF was also evaluated using quantum mechanics/molecular mechanics methods. The descriptor values were computed for 18 Kemp eliminase variants. Spearman correlation matrices were used to determine the region size condition under which further expansion of the region boundary does not substantially change the ranking of descriptor values. We observed that protein dynamics-derived descriptors, including RMSDactive_site and SASAratio, converge at a distance cutoff of 5 Å from the substrate. The electrostatic descriptor, EFC–H, converges at 6 Å using molecular mechanics methods with truncated enzyme models and 4 Å using quantum mechanics/molecular mechanics methods with whole enzyme model. This study serves as a future reference to determine descriptors for predictive modeling of enzyme engineering.

Automatic Plane of Minimal Hiatal Dimensions Extraction From 3D Female Pelvic Floor Ultrasound.

There is an increasing interest in the applications of 3D ultrasound imaging of the pelvic floor to improve the diagnosis, treatment, and surgical planning of female pelvic floor dysfunction (PFD). Pelvic floor biometrics are obtained on an oblique image plane known as the plane of minimal hiatal dimensions (PMHD). Identifying this plane requires the detection of two anatomical landmarks, the pubic symphysis and anorectal angle. The manual detection of the anatomical landmarks and the PMHD in 3D pelvic ultrasound requires expert knowledge of the pelvic floor anatomy, and is challenging, time-consuming, and subject to human error. These challenges have hindered the adoption of such quantitative analysis in the clinic. This work presents an automatic approach to identify the anatomical landmarks and extract the PMHD from 3D pelvic ultrasound volumes. To demonstrate clinical utility and a complete automated clinical task, an automatic segmentation of the levator-ani muscle on the extracted PMHD images was also performed. Experiments using 73 test images of patients during a pelvic muscle resting state showed that this algorithm has the capability to accurately identify the PMHD with an average Dice of 0.89 and an average mean boundary distance of 2.25mm. Further evaluation of the PMHD detection algorithm using 35 images of patients performing pelvic muscle contraction resulted in an average Dice of 0.88 and an average mean boundary distance of 2.75mm. This work had the potential to pave the way towards the adoption of ultrasound in the clinic and development of personalized treatment for PFD.

Region-wise loss for biomedical image segmentation

We propose Region-wise (RW) loss for biomedical image segmentation. Region-wise loss is versatile, can simultaneously account for class imbalance and pixel importance, and it can be easily implemented as the pixel-wise multiplication between the softmax output and a RW map. We show that, under the proposed RW loss framework, certain loss functions, such as Active Contour and Boundary loss, can be reformulated similarly with appropriate RW maps, thus revealing their underlying similarities and a new perspective to understand these loss functions. We investigate the observed optimization instability caused by certain RW maps, such as Boundary loss distance maps, and we introduce a mathematically-grounded principle to avoid such instability. This principle provides excellent adaptability to any dataset and practically ensures convergence without extra regularization terms or optimization tricks. Following this principle, we propose a simple version of boundary distance maps called rectified Region-wise (RRW) maps that, as we demonstrate in our experiments, achieve state-of-the-art performance with similar or better Dice coefficients and Hausdorff distances than Dice, Focal, weighted Cross entropy, and Boundary losses in three distinct segmentation tasks. We quantify the optimization instability provided by Boundary loss distance maps, and we empirically show that our RRW maps are stable to optimize. The code to run all our experiments is publicly available at: https://github.com/jmlipman/RegionWiseLoss.

Nasal-Skull Base Fast Path Extraction and Automatic Navigation

In the nasal-skull base virtual endoscope, the existing navigation path extraction algorithms have low operation efficiency, and automatic navigation algorithm is easy to cause violent rotation of image. Therefore, a center path extraction algorithm based on navigation area pre-planning and an automatic navigation algorithm based on adaptive speed and rotation are proposed. The former improves the distance transformation method. The navigation area is pre-planned based on the fast expanding random tree and calculate the boundary distance field map, and construct the maximum cost tree to extract the center path. The latter improves the DoTween method. The camera moving speed is adjusted according to the length of the shortest line segment in the path, and the predicted steering distance is adjusted according to the cavity diameter at the vertex in the path. Experiments are conducted with nasal models from DICOM. The results show that the path extraction speed of the center path extraction algorithm based on navigation area pre-planning method is about 49 times faster than the distance transformation method, and the automatic navigation algorithm of adaptive speed and rotation is more natural at the vertices comparing with methods such as DoTween.

ERGCN: Data enhancement-based robust graph convolutional network against adversarial attacks

With recent advancements, graph neural networks (GNNs) have shown considerable potential for various graph-related tasks, and their applications have gained considerable attention. However, adversarial attacks can significantly degrade the performance of GNNs, hindering their deployment in critical real-world tasks. GNNs must be robust against adversarial attacks, in which imperceptible adversarial perturbations are introduced to induce serious security issues. To achieve this goal, we propose a robust graph convolutional network, ERGCN, for node classification via data enhancement. ERGCN simultaneously utilizes properties from the “data domain” and “model space” as guidance. Based on the feature smoothness assumption, a graph structure enhancement (GSE) mechanism is proposed to improve the structural reliability of input graphs. Moreover, inspired by self-training methods that assign pseudo-labels to unlabeled training samples and use them to optimize the target model iteratively, a reliable node selection metric, model boundary distance (MBD), is defined based on the distance from training samples to model decision boundary. Finally, a self-training-based robust graph convolutional network is proposed for node classification. Extensive experiments on three public datasets demonstrate the superiority of our model over existing state-of-the-art methods. Our study provides a solution for trustworthy graph machine learning systems in adversarial environments. The code is available at https://github.com/star4455/ERGCN.

Numerical study on harmful boundary of above-ground section leakage of natural gas pipeline

Natural gas has incomparable advantages compared with other energy, but the accidents caused by natural gas pipeline transportation cannot be ignored. In order to reduce the accident damage, it is necessary to master the concentration distribution of leaked gas in the atmosphere scientifically. The main contribution of this research is to predict the harmful boundary of gas diffusion. Numerical simulation was used to establish the leakage diffusion model of natural gas pipeline exposed to atmosphere. The influences of leakage rate, leakage diameter and wind speed on the harmful boundary of gas were studied. The results show that harmful boundary reaches a stable state after a period of leakage. Leakage rate and leakage diameter contribute to vertical height of harmful boundary, and have little effect in the horizontal direction. Wind speed is the main factor affecting gas diffusion path. Increasing wind speed results in decrease of vertical height and increase of horizontal distance of harmful boundary, gas diffusion develops near the ground in the downwind direction. The quantitative formula between harmful boundary of gas diffusion and influencing factors is proposed. This work has a great guiding significance for emergency rescue work in engineering.

Adaptively unsupervised seepage detection in tunnels from 3D point clouds

To address the low efficiency and subjectiveness in manual visual inspection in operational tunnels, a hybrid approach that integrates the 3D Otsu method and K-nearest neighbors are developed for seepage detection from point clouds. Besides, the Delaunay boundary distance threshold method is used to adaptively extract the tunnel’s inner surface. The 3D Otsu method preliminarily segments the metro tunnel point cloud between seepage and non-seepage parts according to the RGB value. The KNN method is followed to denoise the misidentified seepage points by the 3D Otsu method. A real tunnel case in the Wuhan metro system, China, is used to demonstrate the applicability and efficiency of the developed approach. Results indicate that the proposed approach is effective and stable for noise reduction, where 33.26% of the point clouds are identified as noise data and are later eliminated. Compared to the 3D Otsu method and the KNN method alone, the integrated 3D Otsu-KNN method can achieve a higher detection accuracy. The proposed approach can also achieve a comparable detection accuracy to the supervised learning method (e.g. support vector machine). It can be used as a decision tool to automate seepage detection and quantify the severity of seepage in underground tunnels.

Commentary: The Reliability of a 2-mm Minimum Margin as an Adequacy Endpoint for Colorectal Pulmonary Metastasis Ablation Success

The investigators Hasegawa et al ( 1 Hasegawa T. Takaki H. Kodama H. et al. Impact of ablative margin on local tumor progression after radiofrequency ablation for lung metastases from colorectal carcinoma: supplementary analysis of a phase II trial (MLCSG-0802). J Vasc Interv Radiol. 2023; 34: 31-37 Abstract Full Text Full Text PDF Google Scholar ) are to be congratulated for identifying the ablation margin as a key technical element for thermal ablation success in tumor control in their article “Impact of Ablative Margin on Local Tumor Progression after Radiofrequency Ablation for Lung Metastases from Colorectal Carcinoma: Supplementary Analysis of a Phase II Trial.” The study evaluated 95 colorectal cancer (CRC) pulmonary metastases treated with radiofrequency (RF) ablation followed by sequential computed tomography (CT) examinations for at least 12 months after RF ablation and aimed to assess the minimum ablation margin (MM) required for better local tumor control ( 1 Hasegawa T. Takaki H. Kodama H. et al. Impact of ablative margin on local tumor progression after radiofrequency ablation for lung metastases from colorectal carcinoma: supplementary analysis of a phase II trial (MLCSG-0802). J Vasc Interv Radiol. 2023; 34: 31-37 Abstract Full Text Full Text PDF Google Scholar ). The MM was estimated as the shortest distance of the outer tumor boundary to the ground-glass opacity representing the ablation zone (AZ) immediately after RF ablation. Impact of the Ablative Margin on Local Tumor Progression after Radiofrequency Ablation for Lung Metastases from Colorectal Carcinoma: Supplementary Analysis of a Phase II Trial (MLCSG-0802)Journal of Vascular and Interventional RadiologyVol. 34Issue 1PreviewPulmonary oligometastatic disease is considered to be an indication for lung radiofrequency (RF) ablation when patients cannot undergo surgical treatment or external radiation therapy because of poor performance status, comorbid disease, or advanced age (1–3). In particular, the treatment outcomes of lung RF ablation for colorectal cancer (CRC) lung metastases have been well investigated, yielding satisfactory results including a 5-year disease-specific survival rate of 47%–69% and overall survival rate of 33%–68% (2–7). Full-Text PDF