Cost-effectiveness of navigated radiofrequency ablation for hepatocellular carcinoma in China.

OBJECTIVES: Real-time virtual sonography (RVS) is a promising navigation technique for percutaneous radiofrequency ablation (RFA) treatment, especially in ablating nodules poorly visualized on conventional ultrasonography (US). However, its cost-effectiveness has not been established. The purpose of this study is to evaluate the cost-effectiveness of RVS navigated RFA (RVS-RFA) relative to US guided RFA (US-RFA) in patients with small hepatocellular carcinoma (HCC) in China, from the modified societal perspective. METHODS: A state-transition Markov model was created using TreeAge Pro™ 2012. The parameters used in the model, including natural history of HCC patients, procedure efficacy and related costs, were obtained from a systematic search of literature through PubMed, EMBASE, and Science Citation Index databases. The simulated cohort was patients with solitary, small HCC (<3 cm in diameter) and Child-Pugh class A or B, whose tumors are poorly visualized in B-mode US but clearly detectable by CT or MRI. RESULTS: In this cohort of difficult cases, RVS-RFA was a preferred strategy saving 2,467 CNY ($392) throughout the patient's life while gaining additional 1.4 QALYs compared with conventional US guidance. The results were sensitive to the efficacy of US-RFA and RVS-RFA including complete ablation rate and local recurrence rate, the median survival for patients with progressive HCC, the probability of performing RFA for recurrent HCC, and the cost of RVS navigation, disposable needle or hospitalization. CONCLUSIONS: RVS-RFA is a dominant strategy for patients with small HCC unidentifiable in B-mode US, in terms of cost savings and QALYs gained, relative to the conventional US-guided method.

Economic assessment of home-based COPD management programs.

Home-based exacerbation management programs have been proposed as an approach to reducing the clinical and financial burden of COPD. We demonstrate a framework to evaluate such programs in order to guide program design and performance decisions towards optimizing cost and clinical outcomes. This study models the impact of hypothetical exacerbation management programs through probabilistic Markov simulations. Patients were stratified by risk using exacerbation rates from the ECLIPSE study and expert opinion. Three scenarios were modeled, using base, worst and best case parameters to suggest potential telehealth program performance. In these scenarios, acute exacerbations could be detected early, with sensitivity and specificity ranging from 60-90%. Detected acute exacerbations could be diverted to either a sub-acute pathway (12.5-50% probability), thus entirely avoiding hospitalization, or a lower cost pathway through length-of-stay reduction (14-28% reduction). For a cohort of patients without prior hospitalization, the base case telehealth scenario results in a cumulative per-patient lifetime savings of $2.9 K over ≈ 12 years. For a higher risk cohort of patients with a prior admission and 1 to 2 acute exacerbations per year, a cumulative $16K per patient was saved during the remaining ≈ 3 life-years. Acceptable prices for home-based exacerbation detection testing were highly dependent on patient risk and scenario, but ranged from $290-$1263 per month for the highest risk groups. These results suggest the economic viability of exacerbation management programs and highlight the importance of risk stratification in such programs. The presented model can further be adapted to model specific programs as trial data becomes available.

Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature.

Although clinicians have long sought to integrate computer-aided diagnostic (CAD) systems into routine clinical practice, it has proven to be extremely difficult to perform fully automated algorithmic analyses on lesions, based solely on the information contained in images. To increase the utility of computerized tools, it would be intuitive to incorporate anatomical and pathological knowledge and heuristics to help the system draw diagnostic inferences. In neuro-imaging applications, for example, one way to perform this knowledge integration is to uncover symmetry/asymmetry information from the corresponding regions of the head and to explore its implication to positive clinical findings. To correctly quantify asymmetric patterns in brain images, however, the symmetry axis, or the symmetry plane, needs to be appropriately oriented in space; i.e., the symmetry plane needs to be correctly identified either manually or using computerized methods. This review will provide an overview of the current state of knowledge of both symmetry axis/plane detection, and asymmetry quantification in neuro-images.

Asymmetry analysis in rodent cerebral ischemia models.

RATIONALE AND OBJECTIVES: An automated method for identification and segmentation of acute/subacute ischemic stroke, using the inherent bi-fold symmetry in brain images, is presented. An accurate and automated method for localization of acute ischemic stroke could provide physicians with a mechanism for early detection and potentially faster delivery of effective stroke therapy. MATERIALS AND METHODS: Segmentation of ischemic stroke was performed on magnetic resonance (MR) images of subacute rodent cerebral ischemia. Eight adult male Wistar rats weighing 225-300 g were anesthetized with halothane in a mix of 70% nitrous oxide/30% oxygen. Animal core temperature was maintained at 37 degrees C during the entire surgical procedure, including occlusion of the middle cerebral artery (MCA) and the 90-minute post-reperfusion period. To confirm cerebral ischemia, transcranial measurements of cerebral blood flow were performed with laser-Doppler flowmetry, using 15-mm flexible fiberoptic Doppler probes attached to the skull over the MCA territory. Animal MR scans were performed at 1.5 T using a knee coil. Three experts performed manual tracing of the stroke regions for each rat, using the histologic-stained slices to guide delineation of stroke regions. A strict tracing protocol was followed that included multiple (three) tracings of each stroke region. The volumetric MR image data were processed for each rat by computing the axis of symmetry and extracting statistical dissimilarities. A nonparametric Wilcoxon rank sum test operating on paired windows in opposing hemispheres identified seeds in the pixels exhibiting statistically significant bi-fold mirror asymmetry. Two brain reference maps were used for analysis: an absolute difference map (ADM) and a statistical difference map (SDM). Although an ADM simply displays the absolute difference by subtracting one brain hemisphere from its reflection, SDM highlights regions by labeling pixels exhibiting statistically significant asymmetry. RESULTS: To assess the accuracy of the proposed segmentation method, the surrogate ground truth (the stroke tracing data) was compared to the results of our proposed automated segmentation algorithm. Three accuracy segmentation metrics were utilized: true-positive volume fraction (TPVF), false-positive volume fraction (FPVF), and false-negative volume fraction (FNVF). The mean value of the TPVF for our segmentation method was 0.8877; 95% CI 0.7254 to 1.0500; the mean FPVF was 0.3370, 95% CI -0.0893 to 0.7633; the mean FNVF was 0.1122, 95% CI -0.0502 to 0.2747. CONCLUSIONS: Unlike most segmentation methods that require some degree of manual intervention, our segmentation algorithm is fully automated and highly accurate in identifying regions of brain asymmetry. This approach is attractive for numerous neurologic applications where the operator's intervention should be minimal or null.

Economic Modeling of Heart Failure Telehealth Programs: When Do They Become Cost Saving?

Telehealth programs for congestive heart failure have been shown to be clinically effective. This study assesses clinical and economic consequences of providing telehealth programs for CHF patients. A Markov model was developed and presented in the context of a home-based telehealth program on CHF. Incremental life expectancy, hospital admissions, and total healthcare costs were examined at periods ranging up to five years. One-way and two-way sensitivity analyses were also conducted on clinical performance parameters. The base case analysis yielded cost savings ranging from $2832 to $5499 and 0.03 to 0.04 life year gain per patient over a 1-year period. Applying telehealth solution to a low-risk cohort with no prior admission history would result in $2502 cost increase per person over the 1-year time frame with 0.01 life year gain. Sensitivity analyses demonstrated that the cost savings were most sensitive to patient risk, baseline cost of hospital admission, and the length-of-stay reduction ratio affected by the telehealth programs. In sum, telehealth programs can be cost saving for intermediate and high risk patients over a 1- to 5-year window. The results suggested the economic viability of telehealth programs for managing CHF patients and illustrated the importance of risk stratification in such programs.

Meta-analysis and meta-regression of telehealth programmes for patients with chronic heart failure.

To assess the effectiveness of telehealth used for chronic heart failure (CHF) patients, we searched for peer-reviewed, randomized controlled trials published between 2001 and 2012. A total of 33 studies met the inclusion criteria. There were 26 studies (79%) which concerned tele-monitoring and 7 (21%) which concerned case management or nurse administered telephone-based management. There were 7530 patients in all, with an average age of 69 years. A meta-analysis showed that telehealth programmes had significant overall effectiveness in reducing all-cause mortality (Fixed effect model risk ratio 0.76, 95% CI 0.66 to 0.88), CHF-related hospitalization (Random effect model risk ratio 0.72, 95% CI 0.61 to 0.85) and CHF-related length of stay (Random effect model mean difference -1.41 days, 95% CI -2.43 to -0.39). In addition, telehealth programmes showed significantly greater effectiveness in reducing mortality and hospitalizations among patients with higher New York Heart Association (NYHA) categories. With age and NYHA held constant, recording questionnaire (symptoms) data could reduce the mortality risk by 34% and the risk of CHF-related hospitalization by 15%; adding a pulse (heart rate) detector could reduce the mortality risk by 40% and the risk of CHF-related hospitalization by 43%. Finally, telehealth programmes showed a tapering effect on mortality reduction: the longer the follow-up period, the less effective they were on decreasing mortality. In conclusion, telehealth programmes demonstrated clinical effectiveness in patients with CHF compared with usual care.

Ultrasound lesion segmentation using clinical knowledge-driven constrained level set.

Ultrasound lesion segmentation is an important and challenging task. Comparing with other methods, region-based level set has many advantages, but still requires considerable improvement to deal with the characteristic of lesions in the ultrasound modality such as shadowing, speckle and heterogeneity. In the clinical workflow, the physician would usually denote long and short axes of a lesion for measurement purpose yielding four markers in an image. Inspired by this workflow, a constrained level set method is proposed to fully utilize these four markers as prior knowledge and global constraint for the segmentation. First, the markers are detected using template-matching algorithm and B-Spline is applied to fit four markers as the initial contour. Then four-marker constrained energy is added to the region-based local level set to make sure that the contour evolves without deviation from the four markers. Finally the algorithm is implemented in a multi-resolution scheme to achieve sufficient computational efficiency. The performance of the proposed segmentation algorithm was evaluated by comparing our results with manually segmented boundaries on 308 ultrasound images with breast lesions. The proposed method achieves Dice similarity coefficient 89.49 ± 4.76% and could be run in real-time.

Quantitative evaluation for accumulative calibration error and video-CT registration errors in electromagnetic-tracked endoscopy.

PURPOSE: Electromagnetic (EM)-guided endoscopy has demonstrated its value in minimally invasive interventions. Accuracy evaluation of the system is of paramount importance to clinical applications. Previously, a number of researchers have reported the results of calibrating the EM-guided endoscope; however, the accumulated errors of an integrated system, which ultimately reflect intra-operative performance, have not been characterized. To fill this vacancy, we propose a novel system to perform this evaluation and use a 3D metric to reflect the intra-operative procedural accuracy. METHODS: This paper first presents a portable design and a method for calibration of an electromagnetic (EM)-tracked endoscopy system. An evaluation scheme is then described that uses the calibration results and EM-CT registration to enable real-time data fusion between CT and endoscopic video images. We present quantitative evaluation results for estimating the accuracy of this system using eight internal fiducials as the targets on an anatomical phantom: the error is obtained by comparing the positions of these targets in the CT space, EM space and endoscopy image space. To obtain 3D error estimation, the 3D locations of the targets in the endoscopy image space are reconstructed from stereo views of the EM-tracked monocular endoscope. Thus, the accumulated errors are evaluated in a controlled environment, where the ground truth information is present and systematic performance (including the calibration error) can be assessed. RESULTS: We obtain the mean in-plane error to be on the order of 2 pixels. To evaluate the data integration performance for virtual navigation, target video-CT registration error (TRE) is measured as the 3D Euclidean distance between the 3D-reconstructed targets of endoscopy video images and the targets identified in CT. The 3D error (TRE) encapsulates EM-CT registration error, EM-tracking error, fiducial localization error, and optical-EM calibration error. CONCLUSION: We present in this paper our calibration method and a virtual navigation evaluation system for quantifying the overall errors of the intra-operative data integration. We believe this phantom not only offers us good insights to understand the systematic errors encountered in all phases of an EM-tracked endoscopy procedure but also can provide quality control of laboratory experiments for endoscopic procedures before the experiments are transferred from the laboratory to human subjects.

Employing symmetry features for automatic misalignment correction in neuroimages.

A novel method to automatically compute the symmetry plane and to correct the 3D orientation of neuro-images is presented. In acquisition of neuroimaging scans, the lack of perfect alignment of a patient's head makes it challenging to evaluate brain images. By deploying a shape-based criterion, the symmetry plane is defined as a plane that best matches external surface points on one side of the head, with their counterparts on the other side. In our method, the head volume is represented as a re-parameterized surface point cloud, where each location is parameterized by its elevation (latitude), azimuth (longitude), and radius. The search for the best matching surfaces is implemented in a multi-resolution paradigm, and the computation time is significantly decreased. The algorithm was quantitatively evaluated using in both simulated data and in real T1, T2, Flair magnetic resonance patient images. This algorithm is found to be fast (<10s per MR volume), robust and accurate (<.6 degree of Mean Angular Error), invariant to the acquisition noise, slice thickness, bias field, and pathological asymmetries.

Automatic misalignment correction in neuroimages using surface symmetry priors.

Many brain imaging procedures require the careful alignment of different sets of images obtained in the same individual. The available automatic methods for brain alignment are susceptible to improvement. This paper discusses briefly a new automatic method to reinstall the tilted orientation of head images, using surface symmetry as a prior.