Multi-approach comparative study of EEG patterns associated with the most common forms of dementia

Electroencephalography's (EEG) sensitivity in discriminating dementia syndromes remains unclear. This study aimed to investigate EEG markers in patients with major cognitive disorders. The studied population included 4 groups of patients: Alzheimer's disease with associated vascular lesions, Alzheimer's disease without vascular lesions (AD-V), Lewy body disease and vascular dementia (VaD); and completed by a control group composed by cognitively unimpaired patients. EEGs were analysed quantitatively using spectral analysis, functional connectivity and micro-states. By comparison to the controls, expected slowing and alterations of functional connectivity were detected in patients with dementia. Among these patients, an overall increase in power in the alpha band was observed in the VaD group, mainly when compared to the 2 AD groups, while the Alzheimer's disease without vascular lesions group exhibited increased power in the beta-2 band and higher functional connectivity in the same frequency band. Micro-state analyses revealed differences in temporal dynamics for the VaD group. A number of EEG modifications reported as markers of some syndromes were found, but others were not reproduced.

The contribution of executive control dysfunction to freezing of gait in Parkinson's disease

OBJECTIVE

An executive dysfunction is supposed to contribute to freezing of gait (FoG) in Parkinson's disease. We aimed to investigate at a behavioral and cortical levels whether an attentional load (particularly, a conflicting situation) can specifically impact preparation and execution phases of step initiation in parkinsonian patients with FoG.

METHODS

Fifteen patients with FoG, 16 without and 15 controls performed an adapted version of the Attention Network Test, with step initiation as response instead of the standard manual keypress. Kinetic and kinematic features of gait initiation as well as high-resolution electroencephalography were recorded during the task.

RESULTS

Patients with FoG presented an impaired executive control. Step execution time was longer in parkinsonian patients. However, the executive control effect on step execution time was not different between all groups. Compared to patients, controls showed a shorter step initiation-locked alpha desynchronization, and an earlier, more intense and shorter beta desynchronization over the sensorimotor cortex. Even though controls were faster, the induced alpha and beta activity associated with the effect of executive control didn't differ between patients and controls.

CONCLUSIONS

Tasks of conflict resolution lead to a comparable alteration of step initiation and its underlying brain activity in all groups. Links between executive control, gait initiation and FoG seem more complex than expected.

SIGNIFICANCE

This study questions the cognitive hypothesis in the pathophysiology of freezing of gait. Executive dysfunction is associated with FoG but is not the main causal mechanism since the interaction between attention and motor preparation didn't provoke FoG.

Texture Features of Magnetic Resonance Images Predict Poststroke Cognitive Impairment: Validation in a Multicenter Study

BACKGROUND:

Imaging features derived from T1-weighted (T1w) images texture analysis were shown to be potential markers of poststroke cognitive impairment, with better sensitivity than atrophy measurement. However, in magnetic resonance images, the signal distribution is subject to variations and can limit transferability of the method between centers. This study examined the reliability of texture features against imaging settings using data from different centers.

METHODS:

Data were collected from 327 patients within the Stroke and Cognition Consortium from centers in France, Germany, Australia, and the United Kingdom. T1w images were preprocessed to normalize the signal intensities and then texture features, including first- and second-order statistics, were measured in the hippocampus and the entorhinal cortex. Differences between the data led to the use of 2 methods of analysis. First, a machine learning modeling, using random forest, was used to build a poststroke cognitive impairment prediction model using one dataset and this was validated on another dataset as external unseen data. Second, the predictive ability of the texture features was examined in the 2 remaining datasets by ANCOVA with false discovery rate correction for multiple comparisons.

RESULTS:

The prediction model had a mean accuracy of 90% for individual classification of patients in the learning base while for the validation base it was ≈ 77%. ANCOVA showed significant differences, in all datasets, for the kurtosis and inverse difference moment texture features when measured in patients with cognitive impairment and those without.

CONCLUSIONS:

These results suggest that texture features obtained from routine clinical MR images are robust early predictors of poststroke cognitive impairment and can be combined with other demographic and clinical predictors to build an accurate prediction model.

Performance Analysis of Optically Pumped 4He Magnetometers vs. Conventional SQUIDs: From Adult to Infant Head Models

Optically pumped magnetometers (OPMs) are new, room-temperature alternatives to superconducting quantum interference devices (SQUIDs) for measuring the brain's magnetic fields. The most used OPM in MagnetoEncephaloGraphy (MEG) are based on alkali atoms operating in the spin-exchange relaxation-free (SERF) regime. These sensors do not require cooling but have to be heated. Another kind of OPM, based on the parametric resonance of ⁴He atoms are operated at room temperature, suppressing the heat dissipation issue. They also have an advantageous bandwidth and dynamic range more suitable for MEG recordings. We quantitatively assessed the improvement (relative to a SQUID magnetometers array) in recording the magnetic field with a wearable ⁴He OPM-MEG system through data simulations. The OPM array and magnetoencephalography forward models were based on anatomical MRI data from an adult, a nine-year-old child, and 10 infants aged between one month and two years. Our simulations showed that a ⁴He OPMs array offers markedly better spatial specificity than a SQUID magnetometers array in various key performance areas (e.g., signal power, information content, and spatial resolution). Our results are also discussed regarding previous simulation results obtained for alkali OPM.

Functional networks underlying freezing of gait: a resting-state electroencephalographic study

INTRODUCTION

The pathophysiology of freezing of gait in people with Parkinson's disease (PD) remains unclear, despite its association with motor, cognitive, limbic and sensory-perceptual impairments. Resting-state electroencephalography (EEG) may provide functional information for a better understanding of freezing of gait by studying spectral power and connectivity between brain regions in different frequency bands.

METHODS

High-resolution EEG was recorded in 36 patients with PD (18 freezers, 18 non-freezers), and 18 healthy controls during a 5-min resting-state protocol with eyes open, followed by a basic spectral analysis in the sensor space and a more advanced analysis of functional connectivity at the source level.

RESULTS

Freezers showed a diffusely higher theta-band relative spectral power than controls. This increased power was correlated with a deficit in executive control. Concerning resting-state functional connectivity, connectivity strength within a left fronto-parietal network appeared to be higher in freezers than in controls in the theta band, and to be correlated with freezing severity and a history of falls.

CONCLUSION

We have shown that spectral power and connectivity analyses of resting-state EEG provide useful and complementary information to better understand freezing of gait in PD. The higher connectivity strength seen within the left ventral attention network in freezers is in keeping with an excessive guidance of behavior by external cues, due to executive dysfunction, and spectral analysis also found changes in freezers that was closely correlated with executive control deficits. This exaggerated influence of the external environment might result in behavioral consequences that contribute to freezing of gait episodes. These findings should be further investigated with a longitudinal study.

Anxiety in Parkinson's disease: A resting-state high density EEG study

OBJECTIVE

To identify markers of Parkinson's disease (PD) related anxiety, using high density electroencephalography (hd-EEG).

METHODS

108 patients participated in the study. They were divided into two groups: with and without clinically relevant anxiety, according to their score on the Parkinson Anxiety Scale. Resting-state hd-EEG was recorded. Spectral and functional connectivity characteristics were compared between the two groups.

RESULTS

Thirty-three patients (31%) had significant anxiety symptoms. In the spectral analysis, relative power in the alpha1 frequency band in the right prefrontal cortex was lower in patients with anxiety than without. Functional connectivity analysis showed a stronger connectivity between the left insula and several regions of the right prefrontal cortex in patients with anxiety than in those without.

CONCLUSION

This study shows the pivotal role of the insula and frontal cortex in the pathophysiology of anxiety in PD and extends the results of previous studies using magnetic resonance imaging or positron emission tomography imaging.

Can Dopamine Responsiveness Be Predicted in Parkinson's Disease Without an Acute Administration Test?

BACKGROUND

Dopamine responsiveness (dopa-sensitivity) is an important parameter in the management of patients with Parkinson's disease (PD). For quantification of this parameter, patients undergo a challenge test with acute Levodopa administration after drug withdrawal, which may lead to patient discomfort and use of significant resources.

OBJECTIVE

Our objective was to develop a predictive model combining clinical scores and imaging.

METHODS

350 patients, recruited by 13 specialist French centers and considered for deep brain stimulation, underwent an acute L-dopa challenge (dopa-sensitivity > 30%), full assessment, and MRI investigations, including T1w and R2* images. Data were randomly divided into a learning base from 10 centers and data from the remaining centers for testing. A machine selection approach was applied to choose the optimal variables and these were then used in regression modeling. Complexity of the modelling was incremental, while the first model considered only clinical variables, the subsequent included imaging features. The performances were evaluated by comparing the estimated values and actual valuesResults:Whatever the model, the variables age, sex, disease duration, and motor scores were selected as contributors. The first model used them and the coefficients of determination (R2) was 0.60 for the testing set and 0.69 in the learning set (p < 0.001). The models that added imaging features enhanced the performances: with T1w (R2 = 0.65 and 0.76, p < 0.001) and with R2* (R2 = 0.60 and 0.72, p < 0.001).

CONCLUSION

These results suggest that modeling is potentially a simple way to estimate dopa-sensitivity, but requires confirmation in a larger population, including patients with dopa-sensitivity < 30.

Heterogeneity of PD-MCI in Candidates to Subthalamic Deep Brain Stimulation: Associated Cortical and Subcortical Modifications

BACKGROUND

Parkinson's disease mild cognitive impairment (PD-MCI) is frequent and heterogenous. There is no consensus about its influence on subthalamic deep brain stimulation (STN-DBS) outcomes.

OBJECTIVE

To determine the prevalence of PD-MCI and its subtypes in candidates to STN-DBS. Secondarily, we sought to identify MRI structural markers associated with cognitive impairment in these subgroups.

METHODS

Baseline data from the French multicentric PREDISTIM cohort were used. Candidates to STN-DBS were classified according to their cognitive performance in normal cognition (PD-NC) or PD-MCI. The latter included frontostriatal (PD-FS) and posterior cortical (PD-PC) subtypes. Between-group comparisons were performed on demographical and clinical variables as well as on T1-weighted MRI sequences at the cortical and subcortical levels.

RESULTS

320 patients were included: 167 (52%) PD-NC and 153 (48%) PD-MCI patients. The latter group included 123 (80%) PD-FS and 30 (20%) PD-PC patients. There was no between-group difference regarding demographic and clinical variables. PD-PC patients had significantly lower global efficiency than PD-FS patients and significantly worse performance on visuospatial functions, episodic memory, and language. Compared to PD-NC, PD-MCI patients had cortical thinning and radiomic-based changes in the left caudate nucleus and hippocampus. There were no significant differences between the PD-MCI subtypes.

CONCLUSION

Among the candidates to STN-DBS, a significant proportion has PD-MCI which is associated with cortical and subcortical alterations. Some PD-MCI patients have posterior cortical deficits, a subtype known to be at higher risk of dementia.

Neurophysiological recordings improve the accuracy of the evaluation of the outcome in perinatal hypoxic ischemic encephalopathy

OBJECTIVES

Our objective was to evaluate the potential additional value of electroencephalogram (EEG) and evoked potentials in neonates with hypoxic-ischemic encephalopathy to predict their disability at 1 and 2 years old.

METHODS

30 full-term infants after perinatal asphyxia who underwent therapeutic hypothermia were evaluated at 1 year and 2 years for disability using International Classification of Functioning, Disability and Health classification. Scores for EEG, sensory evoked potentials and brainstem auditory evoked potentials were evaluated after withdrawal of therapeutic hypothermia that lasted 72 h. A regression approach was investigated to build models allowing to distinguish neonates according to their disability at 1 and 2 years. Two models were built, the first by considering the clinical data and EEG before and after therapeutic hypothermia and the second by incorporating evoked potentials recording.

RESULTS

Adding EEG and evoked potentials data after rewarming improved dramatically the accuracy of the model considering outcome at 1 and 2 years.

INTERPRETATION

We propose to record systematically EEG and evoked potentials following rewarming to predict the outcome of neonates with hypoxic ischemic encephalopathy. Combination of altered evoked potentials with no improvement of EEG after rewarming appeared to be a robust criterion for a poor outcome.

Posterior Cortical Cognitive Deficits Are Associated With Structural Brain Alterations in Mild Cognitive Impairment in Parkinson's Disease

Context: Cognitive impairments are common in patients with Parkinson's disease (PD) and are heterogeneous in their presentation. The "dual syndrome hypothesis" suggests the existence of two distinct subtypes of mild cognitive impairment (MCI) in PD: a frontostriatal subtype with predominant attentional and/or executive deficits and a posterior cortical subtype with predominant visuospatial, memory, and/or language deficits. The latter subtype has been associated with a higher risk of developing dementia. Objective: The objective of this study was to identify structural modifications in cortical and subcortical regions associated with each PD-MCI subtype. Methods: One-hundred and fourteen non-demented PD patients underwent a comprehensive neuropsychological assessment as well as a 3T magnetic resonance imaging scan. Patients were categorized as having no cognitive impairment (n = 41) or as having a frontostriatal (n = 16), posterior cortical (n = 25), or a mixed (n = 32) MCI subtype. Cortical regions were analyzed using a surface-based Cortical thickness (CTh) method. In addition, the volumes, shapes, and textures of the caudate nuclei, hippocampi, and thalami were studied. Tractometric analyses were performed on associative and commissural white matter (WM) tracts. Results: There were no between-group differences in volumetric measurements and cortical thickness. Shape analyses revealed more abundant and more extensive deformations fields in the caudate nuclei, hippocampi, and thalami in patients with posterior cortical deficits compared to patients with no cognitive impairment. Decreased fractional anisotropy (FA) and increased mean diffusivity (MD) were also observed in the superior longitudinal fascicle, the inferior fronto-occipital fascicle, the striato-parietal tract, and the anterior and posterior commissural tracts. Texture analyses showed a significant difference in the right hippocampus of patients with a mixed MCI subtype. Conclusion: PD-MCI patients with posterior cortical deficits have more abundant and more extensive structural alterations independently of age, disease duration, and severity, which may explain why they have an increased risk of dementia.

Do kinematic gait parameters help to discriminate between fallers and non-fallers with Parkinson's disease?

OBJECTIVE

Although a number of clinical factors have been linked to falls in Parkinson's disease (PD), the diagnostic value of gait parameters remains subject to debate. The objective of this retrospective study was to determine to what extent the combination of gait parameters with clinical characteristics can distinguish between fallers and non-fallers.

METHODS

Using a video motion system, we recorded gait in 174 patients with PD. The patients' clinical characteristics (including motor status, cognitive status, disease duration, dopaminergic treatment and any history of falls or freezing of gait) were noted. The considered kinematic gait parameters included indices of gait bradykinesia and hypokinesia, asymmetry, variability, and foot clearance. After a parameters selection using an ANCOVA analysis, support vector machine algorithm was used to build classification models for distinguishing between fallers and non-fallers. Two models were built, the first included clinical data only while the second incorporated the selected gait parameters.

RESULTS

The "clinical-only" model had an accuracy of 94% for distinguishing between fallers and non-fallers. The model incorporating additional gait parameters including stride time and foot clearance performed even better, with an accuracy of up to 97%.

CONCLUSION

Although fallers differed significantly from non-fallers with regard to disease duration, motor impairment or dopaminergic treatment, the addition of gait parameters such as foot clearance or stride time to clinical variables increased the model's discriminant power.

SIGNIFICANCE

This predictive model now needs to be validated in prospective cohorts.

Quantitative approach to early neonatal EEG visual analysis in hypoxic-ischemic encephalopathy severity: Bridging the gap between eyes and machine

OBJECTIVES

To identify relevant quantitative parameters for early classification of neonatal hypoxic-ischemic encephalopathy (HIE) severity from conventional EEGs.

METHODS

Ninety EEGs, recorded in full-term infants within 6 h of life after perinatal hypoxia, were visually classified according to the French EEG classification into three groups of increasing HIE severity. Physiologically significant EEG features (signal amplitude, continuity and frequency content) were automatically quantified using different parameters. The EEG parameters selection was based on their ability to reproduce the visual EEG classification. Post hoc analysis based on clinical outcome was performed.

RESULTS

Six EEG parameters were selected, with overall EEG classification performances between 61% and 70%. All parameters differed significantly between group 3 (severe) and groups 1 (normal-mildly abnormal) and 2 (moderate) EEGs (p < 0.001). Amplitude and discontinuity parameters were different between the 3 groups (p < 0.01) and were also the best predictors of clinical outcome. Conversely, pH and lactate did not differ between groups.

DISCUSSION

This study provides quantitative EEG parameters that are complementary to visual analysis as early markers of neonatal HIE severity. These parameters could be combined in a multiparametric algorithm to improve their classification performance. The absence of relationship between pH lactate and HIE severity reinforces the central role of early neonatal EEG.

Anxiety in Parkinson's disease is associated with changes in the brain fear circuit

BACKGROUND

Anxiety is frequent in Parkinson's disease (PD) and has a negative impact on disease symptoms and quality of life. The underlying mechanisms remain largely unknown. The aim of this study was to identify anatomical and functional changes associated to PD-related anxiety by comparing the volume, shape and texture of the amygdala, the cortical thickness as well as the functional connectivity (FC) of the fear circuit in patients with and without clinically relevant anxiety.

METHODS

Non-demented PD patients were recruited, and anxiety was quantified using the Parkinson Anxiety Scale. Structural MRI was used to compare cortical thickness and amygdala structure and resting-state functional MRI to compare FC patterns of the amygdala and resting-state functional networks in both groups.

RESULTS

We included 118 patients: 34 with (A+) and 84 without (A-) clinically relevant anxiety. Clusters of cortical thinning were identified in the bilateral fronto-cingulate and left parietal cortices of the A+ group. The texture and the shape of the left amygdala was different in the A+ group but the overall volume did not differ between groups. FC between the amygdala and the whole brain regions did not differ between groups. The internetwork resting-state FC was higher between the "fear circuit" and salience network in the A+ group.

CONCLUSION

Anxiety in PD induces structural modifications of the left amygdala, atrophy of the bilateral fronto-cingulate and the left parietal cortices, and a higher internetwork resting-state FC between the fear circuit and the salience network.

Texture features of magnetic resonance images: A marker of slight cognitive deficits in Parkinson's disease

BACKGROUND

Cognitive impairment is a frequent nonmotor symptom of Parkinson's disease. Depending on severity, patients are considered to have mild cognitive impairment or dementia. However, among the cognitively intact patients, some may have deficits in a less severe range. The early detection of such subtle symptoms may be important for the initiation of care strategies.

OBJECTIVE

To identify imaging markers of early cognitive symptoms, potentially before usual signs, such as atrophy, become manifest.

METHODS

A total of 102 patients with Parkinson's disease and 17 age-matched cognitively intact healthy controls underwent extensive neuropsychological assessment and T1-weighted magnetic resonance imaging. Parkinson's disease patients were separated into 3 groups according to their cognitive status: intact, with slight slowing, and with mild deficits in executive functions. Texture features as measured by first-order and second-order statistics were computed in the following 6 brain regions: the hippocampus, thalamus, amygdala, putamen, caudate nucleus, and pallidum. They were tested between the groups, and their correlation with cognition was examined. Volumetric measurements were made for comparison.

RESULTS

Texture analysis showed significant between-group differences for 2 features-skewness and entropy in the hippocampus, the thalamus, and the amygdala-and the volume analysis revealed no between-group difference. These features were significantly correlated with cognitive performance.

CONCLUSION

These results support the assumption that signal alterations associated with Parkinson's disease-related cognitive decline can be captured very early by texture analysis. As these changes appear to reflect clinical phenomena, texture analysis may be a promising marker for helping cognitive phenotyping in Parkinson's disease. © 2019 International Parkinson and Movement Disorder Society.

Texture Features of Magnetic Resonance Images: an Early Marker of Post-stroke Cognitive Impairment

Stroke is frequently associated with delayed, long-term cognitive impairment (CI) and dementia. Recent research has focused on identifying early predictive markers of CI occurrence. We carried out a texture analysis of magnetic resonance (MR) images to identify predictive markers of CI occurrence based on a combination of preclinical and clinical data. Seventy-two-hour post-stroke T1W MR images of 160 consecutive patients were examined, including 75 patients with confirmed CI at the 6-month post-stroke neuropsychological examination. Texture features were measured in the hippocampus and entorhinal cortex and compared between patients with CI and those without. A correlation study determined their association with MoCA and MMSE clinical scores. Significant features were then combined with the classical prognostic factors, age and gender, to build a machine learning algorithm as a predictive model for CI occurrence. A middle cerebral artery transient occlusion model was used. Texture features were compared in the hippocampus of sham and lesioned rats and were correlated with histologically assessed neural loss. In clinical studies, two texture features, kurtosis and inverse difference moment, differed significantly between patients with and without CI and were significantly correlated with MoCA and MMSE scores. The prediction model had an accuracy of 88 ± 3%. The preclinical model revealed a significant correlation between texture features and neural density in the hippocampus contralateral to the ischemic area. These preliminary results suggest that texture features of MR images are representative of neural alteration and could be a part of a screening strategy for the early prediction of post-stroke CI.

Electroencephalography-based machine learning for cognitive profiling in Parkinson's disease: Preliminary results

BACKGROUND

Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step toward the identification of predictors of cognitive worsening.

OBJECTIVE

The aim of this study was to investigate the use of the combination of resting-state EEG and data-mining techniques to build characterization models.

METHODS

Dense EEG data from 118 patients with Parkinson's disease, classified into 5 different groups according to the severity of their cognitive impairments, were considered. Spectral power analysis within 7 frequency bands was performed on the EEG signals. The obtained quantitative EEG features of 100 patients were mined using 2 machine-learning algorithms to build and train characterization models, namely, support vector machines and k-nearest neighbors models. The models were then blindly tested on data from 18 patients.

RESULTS

The overall classification accuracies were 84% and 88% for the support vector machines and k-nearest algorithms, respectively. The worst classifications were observed for patients from groups with small sample sizes, corresponding to patients with the severe cognitive deficits. Whereas for the remaining groups for whom an accurate diagnosis was required to plan the future healthcare, the classification was very accurate.

CONCLUSION

These results suggest that EEG features computed from a daily clinical practice exploration modality in-that it is nonexpensive, available anywhere, and requires minimal cooperation from the patient-can be used as a screening method to identify the severity of cognitive impairment in patients with Parkinson's disease. © 2018 International Parkinson and Movement Disorder Society.

Gland and Zonal Segmentation of Prostate on T2W MR Images

For many years, prostate segmentation on MR images concerned only the extraction of the entire gland. Currently, in the focal treatment era, there is a continuously increasing need for the separation of the different parts of the organ. In this paper, we propose an automatic segmentation method based on the use of T2W images and atlas images to segment the prostate and to isolate the peripheral and transition zones. The algorithm consists of two stages. First, the target image is registered with each zonal atlas image then the segmentation is obtained by the application of an evidential C-Means clustering. The method was evaluated on a representative and multi-centric image base and yielded mean Dice accuracy values of 0.81, 0.70, and 0.62 for the prostate, the transition zone, and peripheral zone, respectively.

Malignant pleural mesothelioma segmentation for photodynamic therapy planning

Medical imaging modalities such as computed tomography (CT) combined with computer-aided diagnostic processing have already become important part of clinical routine specially for pleural diseases. The segmentation of the thoracic cavity represents an extremely important task in medical imaging for different reasons. Multiple features can be extracted by analyzing the thoracic cavity space and these features are signs of pleural diseases including the malignant pleural mesothelioma (MPM) which is the main focus of our research. This paper presents a method that detects the MPM in the thoracic cavity and plans the photodynamic therapy in the preoperative phase. This is achieved by using a texture analysis of the MPM region combined with a thoracic cavity segmentation method. The algorithm to segment the thoracic cavity consists of multiple stages. First, the rib cage structure is segmented using various image processing techniques. We used the segmented rib cage to detect feature points which represent the thoracic cavity boundaries. Next, the proposed method segments the structures of the inner thoracic cage and fits 2D closed curves to the detected pleural cavity features in each slice. The missing bone structures are interpolated using a prior knowledge from manual segmentation performed by an expert. Next, the tumor region is segmented inside the thoracic cavity using a texture analysis approach. Finally, the contact surface between the tumor region and the thoracic cavity curves is reconstructed in order to plan the photodynamic therapy. Using the adjusted output of the thoracic cavity segmentation method and the MPM segmentation method, we evaluated the contact surface generated from these two steps by comparing it to the ground truth. For this evaluation, we used 10 CT scans with pathologically confirmed MPM at stages 1 and 2. We obtained a high similarity rate between the manually planned surface and our proposed method. The average value of Jaccard index was about 0.79 and we obtained a value of 0.88 using the Dice index.

Vascular targeted photodynamic therapy with TOOKAD® Soluble (WST11) in localized prostate cancer: efficiency of automatic pre-treatment planning

Vascular targeted photodynamic therapy (VTP) with WST11 is a novel non-thermal focal treatment for localized prostate cancer that has shown favorable and early efficacy results in previously published studies. In this work, we investigate the efficiency of automatic dosimetric treatment planning. An action model established in a previous study was used in an image-guided optimization scheme to define the personalized optimal light dose for each patient. The calculated light dose is expressed as the number of optical cylindrical fibers to be used, their positions according to an external insertion grid, and the lengths of their diffuser parts. Evaluation of the method was carried out on data collected from 17 patients enrolled in two multi-centric clinical trials. The protocol consisted of comparing the method-simulated necrosis to the result observed on day 7 MR enhanced images. The method performances showed that the final result can be estimated with an accuracy of 10%, corresponding to a margin of 3 mm. In addition, this process was compatible with clinical conditions in terms of calculation times. The overall process took less than 10 min. Different aspects of the VTP procedure were already defined and optimized. Personalized treatment planning definition remained as an issue needing further investigation. The method proposed herein completes the standardization of VTP and opens new pathways for the clinical development of the technique.

Quantified analysis of histological components and architectural patterns of gleason grades in apparent diffusion coefficient restricted areas upon diffusion weighted MRI for peripheral or transition zone cancer locations

PURPOSE

To quantify and compare the histological components and architectural patterns of Gleason grades in cancerous areas with restriction on apparent diffusion coefficient (ADC) maps.

MATERIALS AND METHODS

Twelve consecutive cases with 14 separate ADC restriction areas, positive for cancer in the peripheral zone (PZ) and transition zone (TZ) were included. All had 3 Tesla MRI and radical prostatectomy. Ten regions of interest (ROIs) within and outside the 14 ADC restriction areas positive for cancer were selected. For each ROI, we performed quantitative analysis of (a) prostate benign and malignant histological component surface ratios, including stroma, glands, epithelium, lumen, cellular nuclei; (b) percent of Gleason grades and measures of ADC values. Means of histological components according to ADC restriction for cancerous area were compared with analyses of variance with repeated measures.

RESULTS

Independent predictors of the probability of cancer were median epithelium/ROI ratio (P = 0.001) and nuclei/ROI ratio (P = 0.03). Independent predictors of the probability of ADC restriction were malignant glands/ROI and luminal space/ROI (P < 0.0001). Effect of malignant glands/ROI area was different according to the localization of the ROI (P = 0.03). We observed an overall difference between the means for all of the histological components for the comparison of true positive and false negative (P < 0.0001), except for the percent of Gleason grade 4 (P = 0.18). In TZ cancers, a predominant grade 3 pattern was associated with low ADC values. In PZ cancers, a predominant grade 4 pattern was associated with low ADC values.

CONCLUSION

Determinants of low ADC were high ratio of malignant glands/ROI area which may be seen in Gleason grades 3 or 4 cancers.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1786-1796.

Real-time light dosimetry for intra-cavity photodynamic therapy: Application for pleural mesothelioma treatment

Complete and homogeneous illumination of the target is necessary for the success of a photodynamic therapy (PDT) procedure. In most applications, light dosimetry is done using detectors placed at strategic locations of the target. In this study we propose a novel approach based on the combination of light distribution modeling with spatial localization of the light applicator for real time estimation and display of the applied dose on medical images. The feasibility approach is demonstrated for intrapleural PDT of malignant pleural mesothelioma.

Focal therapy as primary treatment for localized prostate cancer: definition, needs and future

Focal therapy (FT) may offer a promising treatment option in the field of low to intermediate risk localized prostate cancer. The aim of this concept is to combine minimal morbidity with cancer control as well as maintain the possibility of retreatment. Recent advances in MRI and targeted biopsy has improved the diagnostic pathway of prostate cancer and increased the interest in FT. However, before implementation of FT in routine clinical practice, several challenges are still to overcome including patient selection, treatment planning, post-therapy monitoring and definition of oncologic outcome surrogates. In this article, relevant questions regarding the key steps of FT are critically discussed and the main available energy modalities are analyzed taking into account their advantages and unmet needs.

Illumination profile characterization of a light device for the dosimetry of intra-pleural photodynamic therapy for mesothelioma

BACKGROUND

Complete and homogeneous illumination of the pleural cavity is essential to the success of photodynamic therapy (PDT) for mesothelioma, but remains a challenge. Knowing the repartition and propagation of light around the light applicator could be the first step towards optimizing dosimetry. Here we propose a characterization method of the illumination profile of a specific light device.

METHODS

The light wand, made of a cylindrical diffuser located inside an endotracheal tube, was fixed in a tank filled with dilute 0.01% intralipid. Light dosimetry was performed around the tip of the wand using two complementary methods: direct measurements of light power with an isotropic probe and measurements of light distribution characterization.

RESULTS

Dosimetry with the isotropic probe showed an ellipse-shaped illumination. An optimized effective attenuation coefficient was deduced. Combined with the spatial representation, a theoretical illumination profile was established with iso-surfaces of fluence rate, defining a gradient light dose according to the distance from the diffuser.

CONCLUSION

A theoretical illumination profile of a light device was established and could be part of an intra-operative dosimetry system to improve light delivery during intrapleural PDT.

Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians' manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging. Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used. Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results. The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging.

Assessment of the specificity of a new folate-targeted photosensitizer for peritoneal metastasis of epithelial ovarian cancer to enable intraperitoneal photodynamic therapy. A preclinical study

BACKGROUND

Ovarian cancer's prognosis remains dire after primary therapy. Recurrence rate is disappointingly high as 60% of women with epithelial ovarian cancer considered in remission will develop recurrent disease within 5 years. Special attention to undetected peritoneal metastasis during surgery is necessary as they are the main predictive factors of recurrences. Folate Receptor α (FRα) shows promising prospects in targeting ovarian cancerous cells and intraperitoneal photodynamic therapy (PDT) could be a solution in addition to macroscopic cytoreductive surgery to treat peritoneal micrometastasis. The aim of this preclinical study is to assess the specificity of a folate-targeted photosensitizer for ovarian peritoneal micrometastasis.

METHODS

We used the NuTu-19 epithelial ovarian cancer cell line to induce peritoneal carcinomatosis in female Fischer 344 rats. Three groups of 6 rats were studied (Control (no photosensitizer)/Non-conjugated photosensitizer (Porph)/Folate-conjugated photosensitizer (Porph-s-FA)). Four hours after the administration of the photosensitizer, animals were sacrificed and intraperitoneal organs tissues were sampled. FRα tissue expression was evaluated by immunohistochemistry. Tissue incorporation of photosensitizers was assessed by confocal microscopy and tissue quantification.

RESULTS

FRα is overexpressed in tumor, ovary, and liver whereas, peritoneum, colon, small intestine, and kidney do not express it. Cytoplasmic red endocytosis vesicles observed by confocal microscopy are well correlated to FRα tissue expression. Photosensitizer tissue quantification shows a mean tumor-to-normal tissue ratio of 9.6.

CONCLUSION

We demonstrated that this new generation folate-targeted photosensitizer is specific of epithelial ovarian peritoneal metastasis and may allow the development of efficient and safe intraperitoneal PDT procedure.

An image guided treatment platform for prostate cancer photodynamic therapy

This study describes a multimodality images based platform to drive photodynamic therapies of prostate cancer using WST 11 TOOKAD Soluble drug. The platform integrates a pre-treatment planning tool based on magnetic resonance imaging and a per-treatment guidance tool based on transrectal ultrasound images. Evaluation of the platform on clinical data showed that prediction of the therapy outcome was possible with an accuracy of 90 %.