Imaging-based characterization of convective tissue properties.

Convective transport is an important phenomenon for nanomedicine delivery. We present an imaging-based approach to recover tissue properties that are significant in the accumulation of nanoparticles delivered via systemic methods. The classical pharmacokinetic analysis develops governing equations for the particle transport from a first principle mass balance. Fundamentally, the governing equations for compartmental mass balance represent a spatially invariant mass transport between compartments and do not capture spatially variant convection phenomena. Further, the parameters recovered from this approach do not necessarily have direct meaning with respect to the governing equations for convective transport. In our approach, a framework is presented for directly measuring permeability in the sense of Darcy flow through porous tissue. Measurements from our approach are compared to an extended Tofts model as a control. We demonstrate that a pixel-wise iterative clustering algorithm may be applied to reduce the parameter space of the measurements. We show that measurements obtained from our approach are correlated with measurements obtained from the extended Tofts model control. These correlations demonstrate that the proposed approach contains similar information to an established compartmental model and may be useful in providing an alternative theoretical framework for parameterizing mathematical models for treatment planning and diagnostic studies involving nanomedicine where convection dominated effects are important.

Radiomic mapping model for prediction of Ki-67 expression in adrenocortical carcinoma.

AIM: To determine the value of contrast-enhanced computed tomography (CT)-derived radiomic features in the preoperative prediction of Ki-67 expression in adrenocortical carcinoma (ACC) and to detect significant associations between radiomic features and Ki-67 expression in ACC. MATERIALS AND METHODS: For this retrospective analysis, patients with histopathologically proven ACC were reviewed. Radiomic features were extracted for all patients from the preoperative contrast-enhanced abdominal CT images. Statistical analysis identified the radiomic features predicting the Ki-67 index in ACC and analysed the correlation with the Ki-67 index. RESULTS: Fifty-three cases of ACC that met eligibility criteria were identified and analysed. Of the radiomic features analysed, 10 showed statistically significant differences between the high and low Ki-67 expression subgroups. Multivariate linear regression analysis yielded a predictive model showing a significant association between radiomic signature and Ki-67 expression status in ACC (R2=0.67, adjusted R2=0.462, p=0.002). Further analysis of the independent predictors showed statistically significant correlation between Ki-67 expression and shape flatness, elongation, and grey-level long run emphasis (p=0.002, 0.01, and 0.04, respectively). The area under the curve for identification of high Ki-67 expression status was 0.78 for shape flatness and 0.7 for shape elongation. CONCLUSION: Radiomic features derived from preoperative contrast-enhanced CT images show encouraging results in the prediction of the Ki-67 index in patients with ACC. Morphological features, such as shape flatness and elongation, were superior to other radiomic features in the detection of high Ki-67 expression.

Hepatocellular carcinoma response to transcatheter arterial chemoembolisation using automatically generated pre-therapeutic tumour volumes by a random forest-based segmentation protocol.

AIM: To demonstrate the feasibility of correlating pre-therapeutic volumes and residual liver volume (RLV) with clinical outcomes: time to progression (TTP) and overall survival (OS) in hepatocellular carcinoma (HCC) treated with transcatheter arterial chemoembolisation (TACE). MATERIALS AND METHODS: TTP was calculated from a database of 105 patients, receiving first-line treatment with TACE. TTP cut-off for stratifying patients into responders and non-responders was 28 weeks. Pre-treatment tumour and liver volumes were correlated with the TTP and OS following treatment. Univariate cox-regression model was used to assess whether these volumes could predict TTP and/or OS. Kaplan-Meier analysis with log-rank test was used to compare the TTP between high and low volume groups for viable, necrotic, and total tumour. Kaplan-Meier analysis was performed comparing the OS of 10 patients with the longest TTP (mean=122 weeks) in the responder group and 10 patients with the shortest TTP (mean=7 weeks) in the non-responder group. RESULTS: HCC in high tumour volume groups had a shorter TTP than lesions in low tumour volume groups (p=0.05, p=0.04, p=0.02, for enhancing, non-enhancing, total tumour groups, respectively). A negative (correlation coefficient [CC] 0.3) linear correlation between TTP and tumour volumes, and a positive linear correlation between TTP and residual liver volumes were also demonstrated (CC 0.3). Patients with the longest TTP had a higher OS than with the shortest TTP (p=0.03). CONCLUSION: This demonstrates the feasibility of predicting treatment response of HCC to TACE using volumetric measurements of pre-treatment lesion and the feasibility of correlating RLV with TACE outcome data in HCC patients.

Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT.

AIM: To compare the efficacy of computed tomography (CT) texture analysis and conventional evaluation by radiologists for differentiation between large adrenal adenomas and carcinomas. MATERIALS AND METHODS: Quantitative CT texture analysis was used to evaluate 54 histopathologically proven adrenal masses (mean size=5.9 cm; range=4.1-10 cm) from 54 patients referred to Anderson Cancer Center from January 2002 through April 2014. The patient group included 32 women (mean age at mass evaluation=59 years) and 22 men (mean age at mass evaluation=61 years). Adrenal lesions seen on precontrast and venous-phase CT images were labelled by three different readers, and the labels were used to generate intensity- and geometry-based textural features. The textural features and the attenuation values were considered as input values for a random forest-based classifier. Similarly, the adrenal lesions were classified by two different radiologists based on morphological criteria. Prediction accuracy and interobserver agreement were compared. RESULTS: The textural predictive model achieved a mean accuracy of 82%, whereas the mean accuracy for the radiologists was 68.5% (p<0.0001). The interobserver agreements between the predictive model and radiologists 1 and 2 were 0.44 (p<0.0005; 95% confidence interval [CI]: 0.25-0.62) and 0.47 (p<0.0005; 95% CI: 0.28-0.66), respectively. The Dice similarity coefficient between the readers' image labels was 0.875±0.04. CONCLUSION: CT texture analysis of large adrenal adenomas and carcinomas is likely to improve CT evaluation of adrenal cortical tumours.

Comparison of gene expression profiles between pansensitive and multidrug-resistant strains of Mycobacterium tuberculosis.

Mycobacterium tuberculosis has developed resistance to anti-tuberculosis first-line drugs. Multidrug-resistant strains complicate the control of tuberculosis and have converted it into a worldwide public health problem. Mutational studies of target genes have tried to envisage the resistance in clinical isolates; however, detection of these mutations in some cases is not sufficient to identify drug resistance, suggesting that other mechanisms are involved. Therefore, the identification of new markers of susceptibility or resistance to first-line drugs could contribute (1) to specifically diagnose the type of M. tuberculosis strain and prescribe an appropriate therapy, and (2) to elucidate the mechanisms of resistance in multidrug-resistant strains. In order to identify specific genes related to resistance in M. tuberculosis, we compared the gene expression profiles between the pansensitive H37Rv strain and a clinical CIBIN:UMF:15:99 multidrug-resistant isolate using microarray analysis. Quantitative real-time PCR confirmed that in the clinical multidrug-resistant isolate, the esxG, esxH, rpsA, esxI, and rpmI genes were upregulated, while the lipF, groES, and narG genes were downregulated. The modified genes could be involved in the mechanisms of resistance to first-line drugs in M. tuberculosis and could contribute to increased efficiency in molecular diagnosis approaches of infections with drug-resistant strains.

A comparison of methods to suppress electrocardiographic artifacts in local field potential recordings.

OBJECTIVE: Local field potential (LFP) recordings from deep brain stimulation (DBS) electrodes are often contaminated with electrocardiographic (ECG) artifacts that hinder the detection of disease-specific electrical brain activity. METHODS: Three ECG suppression methods were evaluated: (1) QRS interpolation of the Perceive toolbox, (2) template subtraction, and (3) singular value decomposition (SVD). LFPs were recorded with the Medtronic PerceptTM PC system in nine Parkinson's disease patients with stimulation OFF ("OFF-DBS"; anode disconnected) and ON at 0 mA ("ON-DBS 0 mA"; anode connected). Findings were verified with simulated ECG-contaminated time series. RESULTS: ECG artifacts were present in 10 out of 18 ON-DBS 0 mA recordings. All ECG suppression methods drastically reduced artifact-induced beta band (13-35 Hz) power and at least partly recovered the beta peak and beta burst dynamics. Using external ECG recordings and lengthening artifact epoch length improved the performance of the suppression methods. Increasing epoch length, however, elevated the risk of flattening the beta peak and losing beta burst dynamics. CONCLUSIONS: The SVD method formed the preferred trade-off between artifact cleaning and signal loss, as long as its parameter settings are adequately chosen. SIGNIFICANCE: ECG suppression methods enable analysis of disease-specific neural activity from signals affected by ECG artifacts.

Presence of SARS-CoV-2 on food surfaces and public space surfaces in three districts of Lima, Peru.

The aim of this study was to determine the presence of SARS-CoV-2 on food surfaces and surfaces in public spaces in 3 districts of Lima, Peru. A cross-sectional descriptive study was carried out in three districts of the Lima metropolitan area. Surfaces that were most exposed to users were selected. Samples were swabbed for 4 weeks and transported to the laboratory to determine the presence of the virus. One thousand ninety-five inert surface samples and 960 food surface samples were evaluated for the identification of SARS-CoV-2 by the real time-PCR molecular test, whereby only one sample from an automated teller machine was positive. Most of the inert and food surfaces evaluated did not show the presence of SARS-CoV-2 during the time of sample collection. Despite the negative results, the frequency of disinfection and hygiene measures on high-contact surfaces should be maintained and increased to prevent other highly contagious infectious diseases.

Cellular Density in Adult Glioma, Estimated with MR Imaging Data and a Machine Learning Algorithm, Has Prognostic Power Approaching World Health Organization Histologic Grading in a Cohort of 1181 Patients.

BACKGROUND AND PURPOSE: Recent advances in machine learning have enabled image-based prediction of local tissue pathology in gliomas, but the clinical usefulness of these predictions is unknown. We aimed to evaluate the prognostic ability of imaging-based estimates of cellular density for patients with gliomas, with comparison to the gold standard reference of World Health Organization grading. MATERIALS AND METHODS: Data from 1181 (207 grade II, 246 grade III, 728 grade IV) previously untreated patients with gliomas from a single institution were analyzed. A pretrained random forest model estimated voxelwise tumor cellularity using MR imaging data. Maximum cellular density was correlated with the World Health Organization grade and actual survival, correcting for covariates of age and performance status. RESULTS: A maximum estimated cellular density of >7681 nuclei/mm2 was associated with a worse prognosis and a univariate hazard ratio of 4.21 (P < .001); the multivariate hazard ratio after adjusting for covariates of age and performance status was 2.91 (P < .001). The concordance index between maximum cellular density (adjusted for covariates) and survival was 0.734. The hazard ratio for a high World Health Organization grade (IV) was 7.57 univariate (P < .001) and 5.25 multivariate (P < .001). The concordance index for World Health Organization grading (adjusted for covariates) was 0.761. The maximum cellular density was an independent predictor of overall survival, and a Cox model using World Health Organization grade, maximum cellular density, age, and Karnofsky performance status had a higher concordance (C = 0.764; range 0.748-0.781) than the component predictors. CONCLUSIONS: Image-based estimation of glioma cellularity is a promising biomarker for predicting survival, approaching the prognostic power of World Health Organization grading, with added values of early availability, low risk, and low cost.

Thalamic local field potentials recorded using the deep brain stimulation pulse generator.

Background: Essential tremor (ET) is one of the most common movement disorders, and continuous deep brain stimulation (DBS) is an established treatment for medication-refractory cases. However, the need for increasing stimulation intensities, with unpleasant side effects, and DBS tolerance over time can be problematic. The advent of novel DBS devices now provides the opportunity to longitudinally record LFPs using the implanted pulse generator, which opens up possibilities to implement adaptive DBS algorithms in a real-life setting. Methods: Here we report a case of thalamic LFP activity recorded using a commercially available sensing-enabled DBS pulse generator (Medtronic Percept PC). Results: In the OFF-stimulation condition, a peak tremor frequency of 3.8 Hz was identified during tremor evoking movements as assessed by video and accelerometers. Activity at the same and supraharmonic frequency was seen in the frequency spectrum of the LFP data from the left vim nucleus during motor tasks. Coherence analysis showed that peripherally recorded tremor was coherent with the LFP signal at the tremor frequency and supraharmonic frequency. Conclusion: This is the first report of recorded tremor-related thalamic activity using the electrodes and pulse generator of an implanted DBS system. Larger studies are needed to evaluate the clinical potential of these fully implantable systems, and ultimately pulse generators with sensing-coupled algorithms driving stimulation, to really close the loop.

Assessment of psychosocial adjustment in patients with temporal lobe epilepsy using a standard measure.

Despite the growing evidence of poor psychosocial adjustment, at present there is no formal method of assessment of social adjustment in patients with temporal lobe epilepsy (TLE). First, we assessed social adjustment in patients with TLE using a self-report questionnaire and compared the results with those from quality-of-life (QOL) scales. Second, we verified the influence of cognitive performance and clinical variables of epilepsy on social adjustment and QOL. We evaluated 35 people with TLE and 38 healthy controls. Patients had worse social adjustment, and it was correlated with worse perception of cognitive function. Attention and verbal memory dysfunctions were negatively correlated with social adjustment. However, there was no significant correlation between cognitive performance and QOL. Regarding clinical variables, persons with left TLE showed worse social adjustment and patients with frequent seizures showed worse QOL. These findings indicate the relevance of evaluating social adjustment and emphasize the importance of cognitive rehabilitation to improved social adjustment.

Magnetic resonance temperature imaging validation of a bioheat transfer model for laser-induced thermal therapy.

PURPOSE: Magnetic resonance-guided laser-induced thermal therapy (MRgLITT) is currently undergoing initial safety and feasibility clinical studies for the treatment of intracranial lesions in humans. As studies progress towards evaluation of treatment efficacy, predictive computational models may play an important role for prospective 3D treatment planning. The current work critically evaluates a computational model of laser induced bioheat transfer against retrospective multiplanar MR thermal imaging (MRTI) in a canine model of the MRgLITT procedure in the brain. METHODS: A 3D finite element model of the bioheat transfer that couples Pennes equation to a diffusion theory approximation of light transport in tissue is used. The laser source is modelled conformal with the applicator geometry. Dirichlet boundary conditions are used to model the temperature of the actively cooled catheter. The MRgLITT procedure was performed on n = 4 canines using a 1-cm diffusing tip 15-W diode laser (980 nm). A weighted L2norm is used as the metric of comparison between the spatiotemporal MR-derived temperature estimates and model prediction. RESULTS: The normalised error history between the computational models and MRTI was within 1-4 standard deviations of MRTI noise. Active cooling models indicate that the applicator temperature has a strong effect on the maximum temperature reached, but does not significantly decrease the tissue temperature away from the active tip. CONCLUSIONS: Results demonstrate the computational model of the bioheat transfer may provide a reasonable approximation of the laser-tissue interaction, which could be useful for treatment planning, but cannot readily replace MR temperature imaging in a complex environment such as the brain.

Estimating Local Cellular Density in Glioma Using MR Imaging Data.

BACKGROUND AND PURPOSE: Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, but the degree to which cellular density can be quantitatively estimated from imaging is unknown. The purpose of this study was to discover the best MR imaging and processing techniques to make quantitative and spatially specific estimates of cellular density. MATERIALS AND METHODS: We collected stereotactic biopsies in a prospective imaging clinical trial targeting untreated patients with gliomas at our institution undergoing their first resection. The data included preoperative MR imaging with conventional anatomic, diffusion, perfusion, and permeability sequences and quantitative histopathology on biopsy samples. We then used multiple machine learning methodologies to estimate cellular density using local intensity information from the MR images and quantitative cellular density measurements at the biopsy coordinates as the criterion standard. RESULTS: The random forest methodology estimated cellular density with R 2 = 0.59 between predicted and observed values using 4 input imaging sequences chosen from our full set of imaging data (T2, fractional anisotropy, CBF, and area under the curve from permeability imaging). Limiting input to conventional MR images (T1 pre- and postcontrast, T2, and FLAIR) yielded slightly degraded performance (R2 = 0.52). Outputs were also reported as graphic maps. CONCLUSIONS: Cellular density can be estimated with moderate-to-strong correlations using MR imaging inputs. The random forest machine learning model provided the best estimates. These spatially specific estimates of cellular density will likely be useful in guiding both diagnosis and treatment.

Combined therapeutic effect of a monoclonal anti-idiotype tumor vaccine against NeuGc-containing gangliosides with chemotherapy in a breast carcinoma model.

Anti-idiotypic monoclonal antibodies (mAb) have been evaluated for actively induced immunotherapy with encouraging results. However, rational combination of cancer vaccines with chemotherapy may improve the therapeutic efficacy of these two approaches used separately. The main objective of this study was to evaluate the antitumor effect of the co-administration of 1E10 (Racotumomab), a monoclonal anti-idiotype tumor vaccine against an IgM mAb, named P3 that reacts specifically with NeuGc-containing gangliosides and low-dose Cyclophosphamide in a mammary carcinoma model. F3II tumor-bearing mice were immunized subcutaneously with 100 microg of 1E10 mAb in Alum or with 150 mg/m(2) of Cyclophosphamide intravenously 7 days after the tumor inoculation. While a limited antitumor effect was induced by a single 1E10 mAb immunization; its co-administration with low-dose Cyclophosphamide reduced significantly the F3II mammary carcinoma growth. That response was comparable with the co-administration of the standard high-dose chemotherapy for breast cancer based on 60 mg/m(2) of Doxorubicin and 600 mg/m(2) of Cyclophosphamide, without toxicity signs. Combinatorial chemo-immunotherapy promoted the CD8(+) lymphocytes tumor infiltration and enhanced tumor apoptosis. Furthermore, 1E10 mAb immunization potentiated the antiangiogenic effect of low-dose Cyclophosphamide. Additionally, splenic myeloid cells Gr1(+)/CD11b(+) associated with a suppressor phenotype were significantly reduced in F3II tumor-bearing mice immunized with 1E10 mAb alone or in combination with low-dose Cyclophosphamide. This data may provide a rational for chemo-immunotherapy combinations with potential medical implications in breast cancer.

Imaging-Based Algorithm for the Local Grading of Glioma.

BACKGROUND AND PURPOSE: Gliomas are highly heterogeneous tumors, and optimal treatment depends on identifying and locating the highest grade disease present. Imaging techniques for doing so are generally not validated against the histopathologic criterion standard. The purpose of this work was to estimate the local glioma grade using a machine learning model trained on preoperative image data and spatially specific tumor samples. The value of imaging in patients with brain tumor can be enhanced if pathologic data can be estimated from imaging input using predictive models. MATERIALS AND METHODS: Patients with gliomas were enrolled in a prospective clinical imaging trial between 2013 and 2016. MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, followed by image-guided stereotactic biopsy before resection. An imaging description was developed for each biopsy, and multiclass machine learning models were built to predict the World Health Organization grade. Models were assessed on classification accuracy, Cohen κ, precision, and recall. RESULTS: Twenty-three patients (with 7/9/7 grade II/III/IV gliomas) had analyzable imaging-pathologic pairs, yielding 52 biopsy sites. The random forest method was the best algorithm tested. Tumor grade was predicted at 96% accuracy (κ = 0.93) using 4 inputs (T2, ADC, CBV, and transfer constant from dynamic contrast-enhanced imaging). By means of the conventional imaging only, the overall accuracy decreased (89% overall, κ = 0.79) and 43% of high-grade samples were misclassified as lower-grade disease. CONCLUSIONS: We found that local pathologic grade can be predicted with a high accuracy using clinical imaging data. Advanced imaging data improved this accuracy, adding value to conventional imaging. Confirmatory imaging trials are justified.

Low-frequency oscillation suppression in dystonia: Implications for adaptive deep brain stimulation.

BACKGROUND: Low-frequency oscillations (LFO) detected in the internal globus pallidus of dystonia patients have been identified as a physiomarker for adaptive Deep Brain Stimulation (aDBS), since LFO correlate with dystonic symptoms and are rapidly suppressed by continuous DBS (cDBS). However, it is as yet unclear how LFO should be incorporated as feedback for aDBS. OBJECTIVES: to test the acute effects of aDBS, using the amplitude of short-lived LFO-bursts to titrate stimulation, to explore the immediate effects of cDBS on LFO-modulation and dystonic symptoms, and to investigate whether a difference in the resting-state LFO is present between DBS-naïve patients and patients with chronic DBS. METHODS: seven patients were assessed during either DBS-implantation (n = 2) or battery replacement surgery (n = 5), and pseudorandomized in three conditions: no stimulation, cDBS, and aDBS. Additionally, resting-state LFP-recordings from patients undergoing battery replacement were compared to those obtained during DBS-implantation; LFP-recordings from a previous cohort of six dystonia patients undergoing DBS-implantation were incorporated into this analysis (total n = 8 newly implanted patients). RESULTS: we corroborated that a mild LFO-suppression rapidly occurs during cDBS. However, no acute changes in clinical symptoms were observed after cDBS or aDBS. Remarkably, we observed that resting-state LFO were significantly lower in patients who had been effectively treated with chronic cDBS compared to those of newly implanted patients, even when stimulation was suspended. CONCLUSIONS: our results indicate that LFO-suppression in dystonia, similar to symptom response to cDBS, might be gradual, and remain after stimulation is suspended. Therefore, tracking gradual changes in LFO may be required for aDBS implementation.

Mapping Cell Membrane Organization and Dynamics Using Soft Nanoimprint Lithography.

Membrane shape is a key feature of many cellular processes, including cell differentiation, division, migration, and trafficking. The development of nanostructured surfaces allowing for the in situ manipulation of membranes in living cells is crucial to understand these processes, but this requires complicated and limited-access technologies. Here, we investigate the self-organization of cellular membranes by using a customizable and benchtop method allowing one to engineer 1D SiO2 nanopillar arrays of defined sizes and shapes on high-performance glass compatible with advanced microscopies. As a result of this original combination, we provide a mapping of the morphology-induced modulation of the cell membrane mechanics, dynamics and steady-state organization of key protein complexes implicated in cellular trafficking and signal transduction.

A compressed sensing approach to immobilized nanoparticle localization for superparamagnetic relaxometry.

Superparamagnetic relaxometry (SPMR) exploits the unique magnetic properties of targeted superparamagnetic iron oxide nanoparticles (SPIOs) to detect small numbers of cancer cells. Reconstruction of the spatial distribution of cancer-bound nanoparticles requires solving an ill-posed inverse problem. The current method, multiple source analysis (MSA), uses a least-squares fit to determine the strength and location of a pre-determined number of magnetic dipoles. In this proof-of-concept study, we propose the application of a sparsity averaged reweighting algorithm (SARA) for volumetric reconstruction of immobilized nanoparticle distributions. We first calibrate the parameters that define the location of the sensors in the forward model of measurement physics. Using this optimized model, we evaluated the performance of the algorithms on various configurations of single and multiple point-source phantoms. We investigated the effect of the data fidelity parameter, voxel size, and iterative reweighting on the reconstruction produced by SARA. We found that the calibrated physics model can predict the detected field values within 5% of the measured data. When only a single source was present, both algorithms were able to detect as little as 0.5 µg of immobilized particles. However, when two sources were measured simultaneously, MSA failed to detect sources containing as much as 10 µg of particles, while SARA detected all of the sources containing at least 5 µg of particles. We show that a suitable data fidelity parameter can be selected objectively, and the total magnitude and location of a point source reconstructed by SARA is not sensitive to voxel size. Detection and localization of multiple small clusters of nanoparticles is a crucial step in SPMR-based diagnostic applications. Our algorithm overcomes the need to know the number of dipoles before reconstruction and improves the sensitivity of the reconstruction when multiple sources are present.

Dynamic Contrast-Enhanced MRI in Patients with Brain Metastases Undergoing Laser Interstitial Thermal Therapy: A Pilot Study.

BACKGROUND AND PURPOSE: Tumor recurrence is difficult to predict in patients receiving laser ablation for intracranial malignancy. We assessed the efficacy of the initial area under the time-to-signal intensity curve at 60 seconds (iAUC60) from dynamic contrast-enhanced MR imaging in predicting progression-free survival in patients with brain metastases following laser interstitial thermal therapy. MATERIALS AND METHODS: The study population was a consecutive series of patients undergoing laser interstitial thermal therapy for brain metastases. Patient demographics including age, sex, tumor histology, and Karnofsky Performance Scale were collected prospectively. Preoperative, postoperative, and 1-month follow-up dynamic contrast-enhanced MRIs were analyzed. Values of iAUC60 were computed using a trapezoidal rule applied to the time history of contrast uptake over the first 60 seconds postenhancement. The change in iAUC60 (ΔiAUC60) was calculated by taking the difference between the values of iAUC60 from 2 time points. Pearson correlation coefficients were calculated between progression-free survival, defined as the time from laser interstitial thermal therapy to tumor recurrence, and iAUC60 or ΔiAUC60 values. RESULTS: Thirty-three cases of laser interstitial thermal therapy for 32 brain metastases in a cohort of 27 patients were prospectively analyzed. A significant relationship was observed between the values of iAUC60 from postoperative dynamic contrast-enhanced MR imaging and progression-free survival with Pearson correlation (P = .03) and Cox univariate analysis (P = .01). The relationship between preoperative and 1-month follow-up dynamic contrast-enhanced MR imaging was not significantly correlated with progression-free survival. Similarly, no statistically significant relationship was observed with ΔiAUC60 and progression-free survival between any time points. CONCLUSIONS: Progression-free survival is difficult to predict in patients undergoing laser interstitial thermal therapy for brain metastases due to confounding with posttreatment change. iAUC60 extracted from postoperative dynamic contrast-enhanced MR imaging shows promise for accurately prognosticating patients following this operative therapy.

Direct comparison of oscillatory activity in the motor system of Parkinson's disease and dystonia: A review of the literature and meta-analysis.

OBJECTIVE: To outline the current knowledge of (sub)cortical oscillations in Parkinson's Disease (PD) and dystonia, and to quantitatively summarize the results of direct comparisons of local oscillatory power between both diseases in the resting state, without medication or stimulation, in both the low-frequency (LF, ±4-12 Hz) and beta (±13 to ∼30 Hz) range. METHODS: Eight relevant studies were included. Recordings from 127 dystonia-, and 144 PD-patient hemispheres were analyzed. Ratios of LF and beta power between diseases were obtained. RESULTS: Beta oscillations in dystonia were lower when compared to beta oscillations in PD, ratio = 0.72, Z = 3.56, p = 0.0004, 95% CI [0.60, 0.86]. Subgroup analyses showed significant differences only in the GPi, whilst conflicting evidence was shown in the STN. LF oscillations in PD were lower when compared to LF oscillations in dystonia, ratio = 0.77, Z = 2.45, p = 0.01, 95% CI [0.63, 0.95]. Subgroup analyses showed significant differences in the GPi and the STN, but not in the M1. CONCLUSIONS: LF and beta oscillations are present in the resting-state motor network of both PD and dystonia patients. However, the power distribution of those oscillations differs between diseases. SIGNIFICANCE: This meta-analysis provides high-level evidence which supports the presence of exaggerated oscillations across the parkinsonian/dystonic motor networks.

Intermuscular coherence as biomarker for pallidal deep brain stimulation efficacy in dystonia.

OBJECTIVE: Finding a non-invasive biomarker for Globus Pallidus interna Deep Brain Stimulation (GPi-DBS) efficacy. Dystonia heterogeneity leads to a wide variety of clinical response to GPi-DBS, making it hard to predict GPi-DBS efficacy for individual patients. METHODS: EEG-EMG recordings of twelve dystonia patients who received bilateral GPi-DBS took place pre- and 1 year post-surgery ON and OFF stimulation, during a rest, pinch, and flexion task. Dystonia severity was assessed using the BFMDRS and TWSTRS (pre- and post-surgery ON stimulation). Intermuscular coherence (IMC) and motorcortex corticomuscular coherence (CMC) were calculated. Low frequency (4-12 Hz) and beta band (13-30 Hz) peak coherences were studied. RESULTS: Dystonia severity improved after 1 year GPi-DBS therapy (BFMDRS: 30%, median 7.8 (IQR 3-10), TWSTRS: 22%, median 6.8 (IQR 4-9)). 86% of IMC were above the 95% confidence limit. The highest IMC peak decreased significantly with GPi-DBS in the low frequency and beta band. Low frequency and beta band IMC correlated partly with dystonia severity and severity improvement. CMC generally were below the 95% confidence limit. CONCLUSIONS: Peak low frequency IMC functioned as biomarker for GPi-DBS efficacy, and partly correlated with dystonia severity. SIGNIFICANCE: IMC can function as biomarker. Confirmation in a larger study is needed for use in clinical practice.