Long-Term Effectiveness of a Nonsurgical BPH Treatment System in Prostate Radiation Patients with Medication-Refractory Lower Urinary Tract Symptoms

PURPOSE/OBJECTIVE(S)

To evaluate the long-term outcomes and toxicity of a nonsurgical prostatic urethral lift system in patients with prior prostate cancer radiation therapy (RT).

MATERIALS/METHODS

From 2018 to 2020, nineteen patients with a history of prior prostate RT underwent a nonsurgical prostatic urethral lift (PUL) procedure for lower urinary track symptoms (LUTS) refractory to alpha blocker medications. Prostate cancer RT was completed a median of 17.8 months (IQR 13.4-27.4) prior to PUL procedure. All patients were assessed with IPSS/quality of life (QOL) scores and urinary post void residual (PVR) ultrasound before and after PUL. Patients were also assessed for ability to discontinue alpha blockade medications for their LUTS and any post PUL toxicity based on CTCAE v5.0 criteria.

RESULTS

All patients underwent successful placement of the PUL implants. Median pre-PUL IPSS score was 20 (IOR 17.5-22.5) and the median prostate volume was 41cc (IQR 33-49). Median follow up was 22 months (IQR 5-30). The average improvement in IPSS score was 8.5 points (IRQ 5.5-11) at 3 months and 7.5 points (IQR 6-10) at last follow-up. Two patients (10%) initially had improvement in IPSS score at 3 months with subsequent significant increase in IPSS (>5 points) at last follow-up. Overall, there was minimal change in reported QOL scores (median 0, IQR 0-1.75). The average reduction in PVR was 90cc. The percentage of patients able to discontinue alpha blockade at last follow-up was 68%. There was no grade 3 or higher GU toxicity in either group.

CONCLUSION

Use of a nonsurgical prostatic urethral lift system in patients with prior prostate RT refractory to alpha blocker medications results in significant long-term improvement of LUTS after radiation therapy for the majority of patients with minimal toxicity.

Smoothing method for unit quaternion time series in a classification problem: an application to motion data

Smoothing orientation data is a fundamental task in different fields of research. Different methods of smoothing time series in quaternion algebras have been described in the literature, but their application is still an open point. This paper develops a smoothing approach for smoothing quaternion time series to obtain good performance in classification problems. Starting from an existing method which involves an angular velocity transformation of unit quaternion time series, a new method which employ the logarithm function to transform the quaternion time series to a real three-dimensional time series is proposed. Empirical evidences achieved on real data set and artificially noisy data sets confirm the effectiveness of the proposed method compared with the classical approach based on angular velocity transformation. The R functions developed for this paper will be provided in a Github repository.

Semi-supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data

Recent approaches in gait analysis involve the use of wearable motion sensors to extract spatio-temporal parameters that characterize multiple aspects of an individual's gait. In particular, the medical community could largely benefit from this type of devices as they could provide the clinicians with a valuable tool for assessing gait impairment. Motion sensor data are however complex and there is an urgent unmet need to develop sound statistical methods for analyzing such data and extracting clinically relevant information. In this article, we measure gait by following the hip rotation over time and the resulting statistical unit is a time series of unit quaternions. We explore the possibility to form groups of patients with similar walking impairment by taking into account their walking data and their global decease severity with semi-supervised clustering. We generalize a compromise-based method named hclustcompro to unit quaternion time series by combining it with the proper dissimilarity quaternion dynamic time warping. We apply this method on patients diagnosed with multiple sclerosis to form groups of patients with similar walking deficiencies while accounting for the clinical assessment of their overall disability. We also compare the compromise-based clustering approach with the method mergeTrees that falls into a sub-class of ensemble clustering named collaborative clustering. The results provide a first proof of both the interest of using wearable motion sensors for assessing gait impairment and the use of prior knowledge to guide the clustering process. It also demonstrates that compromise-based clustering is a more appropriate approach in this context.

Domain selection and family-wise error rate for functional data: a unified framework

Functional data are smooth, often continuous, random curves, which can be seen as an extreme case of multivariate data with infinite dimensionality. Just as component-wise inference for multivariate data naturally performs feature selection, subset-wise inference for functional data performs domain selection. In this paper, we present a unified testing framework for domain selection on populations of functional data. In detail, p-values of hypothesis tests performed on point-wise evaluations of functional data are suitably adjusted for providing a control of the family-wise error rate (FWER) over a family of subsets of the domain. We show that several state-of-the-art domain selection methods fit within this framework and differ from each other by the choice of the family over which the control of the FWER is provided. In the existing literature, these families are always defined a priori. In this work, we also propose a novel approach, coined threshold-wise testing, in which the family of subsets is instead built in a data-driven fashion. The method seamlessly generalizes to multidimensional domains in contrast to methods based on a-priori defined families. We provide theoretical results with respect to consistency and control of the FWER for the methods within the unified framework. We illustrate the performance of the methods within the unified framework on simulated and real data examples, and compare their performance with other existing methods. This article is protected by copyright. All rights reserved.

Subtype Diagnosis of Sporadic Creutzfeldt-Jakob Disease with Diffusion Magnetic Resonance Imaging

OBJECTIVE

Sporadic Creutzfeldt-Jakob disease (sCJD) comprises several subtypes as defined by genetic and prion protein characteristics, which are associated with distinct clinical and pathological phenotypes. To date, no clinical test can reliably diagnose the subtype. We established two procedures for the antemortem diagnosis of sCJD subtype using diffusion magnetic resonance imaging (MRI).

METHODS

MRI of 1,458 patients referred to the National Prion Disease Pathology Surveillance Center were collected through its consultation service. One neuroradiologist blind to the diagnosis scored 12 brain regions and generated a lesion profile for each MRI scan. We selected 487 patients with autopsy-confirmed diagnosis of "pure" sCJD subtype and at least one positive diffusion MRI examination. We designed and tested two data-driven procedures for subtype diagnosis: the first procedure-prion subtype classification algorithm with MRI (PriSCA_MRI)-uses only MRI examinations; the second-PriSCA_MRI + Gen-includes knowledge of the prion protein codon 129 genotype, a major determinant of sCJD subtypes. Both procedures were tested on the first MRI and the last MRI follow-up.

RESULTS

PriSCA_MRI classified the 3 most prevalent subtypes with 82% accuracy. PriSCA_MRI + Gen raised the accuracy to 89% and identified all subtypes. Individually, the 2 most prevalent sCJD subtypes, MM1 and VV2, were diagnosed with sensitivities up to 95 and 97%, respectively. The performances of both procedures did not change in 168 patients with longitudinal MRI studies when the last examination was used.

INTERPRETATION

This study provides the first practical algorithms for antemortem diagnosis of sCJD subtypes. MRI diagnosis of subtype is likely to be attainable at early disease stages to prognosticate clinical course and design future therapeutic trials. ANN NEUROL 2021;89:560-572.

Structures and internal dynamics of diphenylether and its aggregates with water

We report on a detailed multi-spectroscopic analysis of the structures and internal dynamics of diphenylether and its aggregates with up to three water molecules by employing molecular beam experiments.

Corpus Callosum White Matter Diffusivity Reflects Cumulative Neurological Comorbidity in Tuberous Sclerosis Complex

INTRODUCTION

Neurological manifestations in Tuberous Sclerosis Complex (TSC) are highly variable. Diffusion tensor imaging (DTI) may reflect the neurological disease burden. We analyzed the association of autism spectrum disorder (ASD), intellectual disability (ID) and epilepsy with callosal DTI metrics in subjects with and without TSC.

METHODS

186 children underwent 3T MRI DTI: 51 with TSC (19 with concurrent ASD), 46 with non-syndromic ASD and 89 healthy controls (HC). Subgroups were based on presence of TSC, ASD, ID, and epilepsy. Density-weighted DTI metrics obtained from tractography of the corpus callosum were fitted using a 2-parameter growth model. We estimated distributions using bootstrapping and calculated half-life and asymptote of the fitted curves.

RESULTS

TSC was associated with a lower callosal fractional anisotropy (FA) than ASD, and ASD with a lower FA than HC. ID, epilepsy and ASD diagnosis were each associated with lower FA values, demonstrating additive effects. In TSC, the largest change in FA was related to a comorbid diagnosis of ASD. Mean diffusivity (MD) showed an inverse relationship to FA. Some subgroups were too small for reliable data fitting.

CONCLUSIONS

Using a cross-disorder approach, this study demonstrates cumulative abnormality of callosal white matter diffusion with increasing neurological comorbidity.

Reproducibility of Brain MRI Segmentation Algorithms: Empirical Comparison of Local MAP PSTAPLE, FreeSurfer, and FSL-FIRST

BACKGROUND AND PURPOSE

Segmentation of human brain structures is crucial for the volumetric quantification of brain disease. Advances in algorithmic approaches have led to automated techniques that save time compared to interactive methods. Recently, the utility and accuracy of template library fusion algorithms, such as Local MAP PSTAPLE (PSTAPLE), have been demonstrated but there is little guidance regarding its reproducibility compared to single template-based algorithms such as FreeSurfer and FSL-FIRST.

METHODS

Eight repeated magnetic resonance imagings of 20 subjects were segmented using FreeSurfer, FSL-FIRST, and PSTAPLE. We reported the reproducibility of segmentation-derived volume measurements for brain structures and calculated sample size estimates for detecting hypothetical rates of tissue atrophy given the observed variances.

RESULTS

PSTAPLE had the most reproducible volume measurements for hippocampus, putamen, thalamus, caudate, pallidum, amygdala, Accumbens area, and cortical regions. FreeSurfer was most reproducible for brainstem. PSTAPLE was the most accurate algorithm in terms of several metrics include Dice's coefficient. The sample size estimates showed that a study utilizing PSTAPLE would require tens to hundreds less subjects than the other algorithms for detecting atrophy rates typically observed in brain disease.

CONCLUSIONS

PSTAPLE is a useful tool for automatic human brain segmentation due to its precision and accuracy, which enable the detection of the size of the effect typically reported for neurological disorders with a substantially reduced sample size, in comparison to the other tools we assessed. This enables randomized controlled trials to be executed with reduced cost and duration, in turn, facilitating the assessment of new therapeutic interventions.

Structural analyses of isolated cyclic tetrapeptides with varying amino acid residues

Structural analyses of isolated cyclic tetrapeptides with varying amino acid residues were performed by applying combined IR/UV spectroscopy in the molecular beam and DFT calculations. The intrinsic structural properties especially with regard to the influence of different amino acid residues are fundamental for optimizing their binding ability.

Structural investigations on a linear isolated depsipeptide: the importance of dispersion interactions

The first molecular beam investigations of an isolated linear depsipeptide are presented. By applying IR/UV spectroscopic methods and DFT calculations three structural arrangements are identified with the most stable structure being only stable by including dispersion interactions.

Tubers are neither static nor discrete: Evidence from serial diffusion tensor imaging

OBJECTIVE

To assess the extent and evolution of tissue abnormality of tubers, perituber tissue, and normal-appearing white matter (NAWM) in patients with tuberous sclerosis complex using serial diffusion tensor imaging.

METHODS

We applied automatic segmentation based on a combined global-local intensity mixture model of 3T structural and 35 direction diffusion tensor MRIs (diffusion tensor imaging) to define 3 regions: tuber tissue, an equal volume perituber rim, and the remaining NAWM. For each patient, scan, lobe, and tissue type, we analyzed the averages of mean diffusivity (MD) and fractional anisotropy (FA) in a generalized additive mixed model.

RESULTS

Twenty-five patients (mean age 5.9 years; range 0.5-24.5 years) underwent 2 to 6 scans each, totaling 70 scans. Average time between scans was 1.2 years (range 0.4-2.9). Patient scans were compared with those of 73 healthy controls. FA values were lowest, and MD values were highest in tubers, next in perituber tissue, then in NAWM. Longitudinal analysis showed a positive (FA) and negative (MD) correlation with age in tubers, perituber tissue, and NAWM. All 3 tissue types followed a biexponential developmental trajectory, similar to the white matter of controls. An additional qualitative analysis showed a gradual transition of diffusion values across the tissue type boundaries.

CONCLUSIONS

Similar to NAWM, tuber and perituber tissues in tuberous sclerosis complex undergo microstructural evolution with age. The extent of diffusion abnormality decreases with distance to the tuber, in line with known extension of histologic, immunohistochemical, and molecular abnormalities beyond tuber pathology.

The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery

BACKGROUND AND PURPOSE

Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography-derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop.

METHODS

Eight international teams from leading institutions reconstructed the pyramidal tract in four neurosurgical cases presenting with a glioma near the motor cortex. Tractography methods included deterministic, probabilistic, filtered, and global approaches. Standardized evaluation of the tracts consisted in the qualitative review of the pyramidal pathways by a panel of neurosurgeons and DTI experts and the quantitative evaluation of the degree of agreement among methods.

RESULTS

The evaluation of tractography reconstructions showed a great interalgorithm variability. Although most methods found projections of the pyramidal tract from the medial portion of the motor strip, only a few algorithms could trace the lateral projections from the hand, face, and tongue area. In addition, the structure of disagreement among methods was similar across hemispheres despite the anatomical distortions caused by pathological tissues.

CONCLUSIONS

The DTI Challenge provides a benchmark for the standardized evaluation of tractography methods on neurosurgical data. This study suggests that there are still limitations to the clinical use of tractography for neurosurgical decision making.

Systematic combined noncontrast CT-CT angiography in the management of unexplained nontraumatic coma

BACKGROUND

Nontraumatic impaired consciousness is a common issue in emergency departments with a serious but widely variable prognosis.

STUDY OBJECTIVES

The aim of this prospective study was to evaluate the ability of systematic combined noncontrast computed tomography (NCCT)/computed tomography angiography (CTA) imaging, firstly to provide a neurologic prognosis and secondly to ensure early detection of basilar artery occlusion (BAO), in unexplained nontraumatic impaired consciousness management.

METHODS

Combined NCCT/CTA imaging was performed on 65 patients with impaired consciousness and no history of trauma prospectively over 14 months in a single center. Images were assessed based on visual and quantitative criteria. Clinical outcome was assessed using the modified Rankin Scale at 3 months. Statistical analysis aimed to identify the prognostic value of combined NCCT/CTA imaging and its ability for early BAO detection.

RESULTS

This study shows that combined NCCT/CTA imaging was a significant predictor of poor neurological outcome, with a positive predictive value of 94.6%. The combination was also crucial for early detection of BAO, given that 42.8% of cases were misdiagnosed with NCCT alone. Basilar artery occlusion represented 10.8% of all unexplained nontraumatic impaired consciousness.

CONCLUSIONS

Systematic combined NCCT/CTA imaging is an efficient tool for predicting poor neurologic prognosis in cases of unexplained nontraumatic impaired consciousness and is also essential for detecting BAO.

Functional arterial spin labeling: Optimal sequence duration for motor activation mapping in clinical practice

PURPOSE

To determine the minimal optimal functional arterial spin labeling (fASL) sequence duration allowing steady and reproducible motor activation mapping.

MATERIALS AND METHODS

Three magnetic resonance imaging (MRI) sessions including fASL and blood oxygenation level-dependent (BOLD) functional MRI (fMRI) sequences were performed on 12 healthy subjects at 3T with a 32-channel coil. The raw 7-minute fASL sequence was truncated to obtain six fASL sequences with durations ranging from 1-6 minutes. All the resulting fASL activations were compared between themselves and with both the 7-minute fASL and BOLD activations. Quantitative parameters assessed activation location (activated volume, barycenter, and distance between barycenters), activation quantification (activation-related cerebral blood flow), and intraindividual reproducibility across fMRI sessions. The statistical analysis was based on analysis of variance (ANOVA) and Tukey's multiple comparisons.

RESULTS

Four-minute fASL achieved steady location and quantification of activation with the activated volume corresponding to 81% of the 7-minute fASL volume and a barycenter located 1.2 mm from the 7-minute fASL barycenter and 3.0 mm from the BOLD fMRI barycenter. Four-minute fASL reproducibility was high and statistically equivalent to 7-minute values.

CONCLUSION

A 4-minute fASL sequence is thus a reliable tool for motor activation mapping and suitable for use in clinical practice.

Enhancement of ferroelectric polarization stability by interface engineering

By using theoretical predictions based on first-principle calculations, we explore an interface engineering approach to stabilize polarization states in ferroelectric heterostructures with a thickness of just several nanometers.

Non-local robust detection of DTI white matter differences with small databases

Diffusion imaging, through the study of water diffusion, allows for the characterization of brain white matter, both at the population and individual level. In recent years, it has been employed to detect brain abnormalities in patients suffering from a disease, e.g., from multiple sclerosis (MS). State-of-the-art methods usually utilize a database of matched (age, sex, ...) controls, registered onto a template, to test for differences in the patient white matter. Such approaches however suffer from two main drawbacks. First, registration algorithms are prone to local errors, thereby degrading the comparison results. Second, the database needs to be large enough to obtain reliable results. However, in medical imaging, such large databases are hardly available. In this paper, we propose a new method that addresses these two issues. It relies on the search for samples in a local neighborhood of each pixel to increase the size of the database. Then, we propose a new test based on these samples to perform a voxelwise comparison of a patient image with respect to a population of controls. We demonstrate on simulated and real MS patient data how such a framework allows for an improve detection power and a better robustness and reproducibility, even with a small database.