Diagnostic and Therapeutic Issues in Glioma Using Imaging Data: The Challenge of Numerical Twinning.

Glial tumors represent the leading etiology of primary brain tumors. Their particularities lie in (i) their location in a highly functional organ that is difficult to access surgically, including for biopsy, and (ii) their rapid, anisotropic mode of extension, notably via the fiber bundles of the white matter, which further limits the possibilities of resection. The use of mathematical tools enables the development of numerical models representative of the oncotype, genotype, evolution, and therapeutic response of lesions. The significant development of digital technologies linked to high-resolution NMR exploration, coupled with the possibilities offered by AI, means that we can envisage the creation of digital twins of tumors and their host organs, thus reducing the use of physical sampling.

A Hybrid Method for 3D Reconstruction of MR Images.

Three-dimensional surface reconstruction is a well-known task in medical imaging. In procedures for intervention or radiation treatment planning, the generated models should be accurate and reflect the natural appearance. Traditional methods for this task, such as Marching Cubes, use smoothing post processing to reduce staircase artifacts from mesh generation and exhibit the natural look. However, smoothing algorithms often reduce the quality and degrade the accuracy. Other methods, such as MPU implicits, based on adaptive implicit functions, inherently produce smooth 3D models. However, the integration in the implicit functions of both smoothness and accuracy of the shape approximation may impact the precision of the reconstruction. Having these limitations in mind, we propose a hybrid method for 3D reconstruction of MR images. This method is based on a parallel Marching Cubes algorithm called Flying Edges (FE) and Multi-level Partition of Unity (MPU) implicits. We aim to combine the robustness of the Marching Cubes algorithm with the smooth implicit curve tracking enabled by the use of implicit models in order to provide higher geometry precision. Towards this end, the regions that closely fit to the segmentation data, and thus regions that are not impacted by reconstruction issues, are first extracted from both methods. These regions are then merged and used to reconstruct the final model. Experimental studies were performed on a number of MRI datasets, providing images and error statistics generated from our results. The results obtained show that our method reduces the geometric errors of the reconstructed surfaces when compared to the MPU and FE approaches, producing a more accurate 3D reconstruction.

A multi-sequences MRI deep framework study applied to glioma classfication.

Glioma is one of the most important central nervous system tumors, ranked 15th in the most common cancer for men and women. Magnetic Resonance Imaging (MRI) represents a common tool for medical experts to the diagnosis of glioma. A set of multi-sequences from an MRI is selected according to the severity of the pathology. Our proposed approach aims moreto create a computer-aided system that is capable of helping morethe expert diagnose the brain gliomas. moreWe propose a supervised learning regime based on a convolutional neural network based framework and transfer learning techniques. Our research morefocuses on the performance of different pre-trained deep learning models with respect to different MRI sequences. We highlight the best combinations of such model-MRI sequence couple for our specific task of classifying healthy brain against brain with glioma. moreWe also propose to visually analyze the extracted deep features for studying the existing relation of the MRI sequences and models. This interpretability analysis gives some hints for medical expert to understand the diagnosis made by the models. Our study is based on the well-known BraTS datasets including multi-sequence images and expert diagnosis.

Assessment of Compliance and Impact of the COVID-19 RSNA Recommendations on Radiology Departments: A French Survey.

Online supplemental material is available for this article. See also the editorial by Tuite in this issue.

Efficacy of chest CT scan for COVID-19 diagnosis in a low prevalence and incidence region.

OBJECTIVES: Value of chest CT was mainly studied in area of high COVID-19 incidence. The aim of this study was therefore to evaluate chest CT performances to diagnose COVID-19 pneumonia with regard to RT-PCR as reference standard in a low incidence area. METHODS: A survey was sent to radiology department in 4 hospitals in an administrative French region of weak disease prevalence (3.4%). Study design was approved by the local institutional review board and recorded on the clinicaltrial.gov website (NCT04339686). Written informed consent was waived due to retrospective anonymized data collection. Patients who underwent a RT-PCR and a chest CT scan within 48 h for COVID-19 pneumonia suspicion were consecutively included. Diagnostic accuracy including the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of chest CT regarding RT-PCR as reference standard were calculated. RESULTS: One hundred twenty-nine patients had abnormal chest CT findings compatible with a COVID-19 pneumonia (26%, 129/487). Among the 358 negative chest CT findings, 3% (10/358) were RT-PCR positive. Chest CT sensitivity, specificity, positive, and negative predictive value were respectively 87% (IC95: 85, 89; 69/79), 85% (IC95: 83, 87; 348/408), 53% (IC95: 50, 56; 69/129), and 97% (IC95: 95, 99; 348/358). CONCLUSIONS: In a low prevalence area, chest CT scan is a good diagnostic tool to rule out COVID-19 infection among symptomatic suspected patients. KEY POINTS: • In a low prevalence area (3.4% in the administrative area and 5.8% at mean in the study) chest CT sensitivity and specificity for diagnosing COVID-19 pneumonia were 87% and 85% respectively. • In patients with negative chest CT for COVID-19 pneumonia, the negative predictive value of COVID-19 infection was 97% (348/358 subjects). • Performance of CT was equivalent between the 4 centers participating to this study.

Efficacy of Chest CT for COVID-19 Pneumonia Diagnosis in France.

Background The role and performance of chest CT in the diagnosis of the coronavirus disease 2019 (COVID-19) pandemic remains under active investigation. Purpose To evaluate the French national experience using chest CT for COVID-19, results of chest CT and reverse transcription polymerase chain reaction (RT-PCR) assays were compared together and with the final discharge diagnosis used as the reference standard. Materials and Methods A structured CT scan survey (NCT04339686) was sent to 26 hospital radiology departments in France between March 2, 2020, and April 24, 2020. These dates correspond to the peak of the national COVID-19 epidemic. Radiology departments were selected to reflect the estimated geographic prevalence heterogeneities of the epidemic. All symptomatic patients suspected of having COVID-19 pneumonia who underwent both initial chest CT and at least one RT-PCR test within 48 hours were included. The final discharge diagnosis, based on multiparametric items, was recorded. Data for each center were prospectively collected and gathered each week. Test efficacy was determined by using the Mann-Whitney test, Student t test, χ2 test, and Pearson correlation coefficient. P < .05 indicated a significant difference. Results Twenty-six of 26 hospital radiology departments responded to the survey, with 7500 patients entered; 2652 did not have RT-PCR test results or had unknown or excess delay between the RT-PCR test and CT. After exclusions, 4824 patients (mean age, 64 years ± 19 [standard deviation], 2669 male) were included. With final diagnosis as the reference, 2564 of the 4824 patients had COVID-19 (53%). Sensitivity, specificity, negative predictive value, and positive predictive value of chest CT in the diagnosis of COVID-19 were 2319 of 2564 (90%; 95% CI: 89, 91), 2056 of 2260 (91%; 95% CI: 91, 92), 2056 of 2300 (89%; 95% CI: 87, 90), and 2319 of 2524 (92%; 95% CI: 91, 93), respectively. There was no significant difference for chest CT efficacy among the 26 geographically separate sites, each with varying amounts of disease prevalence. Conclusion Use of chest CT for the initial diagnosis and triage of patients suspected of having coronavirus disease 2019 was successful. © RSNA, 2021 Online supplemental material is available for this article.

Comparison of Spinal Cord Stimulation vs. Dorsal Root Ganglion Stimulation vs. Association of Both in Patients with Refractory Chronic Back and/or Lower Limb Neuropathic Pain: An International, Prospective, Randomized, Double-Blinded, Crossover Trial (BOOST-DRG Study).

While spinal cord stimulation (SCS) is a well-established therapy to address refractory persistent spinal pain syndrome after spinal surgery (PSPS-T2), its lack of spatial selectivity and reported discomfort due to positional effects can be considered as significant limitations. As alternatives, new waveforms, such as burst stimulation and different spatial neural targets, such as dorsal root ganglion stimulation (DRGS), have shown promising results. Comparisons between DRGS and standard SCS, or their combination, have never been studied on the same patients. "BOOST DRG" is the first prospective, randomized, double-blinded, crossover study to compare SCS vs. DRGS vs. SCS+DRGS. Sixty-six PSPS-T2 patients will be recruited internationally in three centers. Before crossing over, patients will receive each stimulation modality for 1 month, using tonic conventional stimulation. After 3 months, stimulation will consist in switching to burst for 1 month, and patients will choose which modality/waveform they receive and will then be reassessed at 6 and 12 months. In addition to our primary outcome based on pain rating, this study is designed to assess quality of life, functional disability, psychological distress, pain surface coverage, global impression of change, medication quantification, adverse events, brain functional imaging and electroencephalography, with the objective being to provide a multidimensional insight based on composite pain assessment.

Altered phospholipid and high-energy phosphate metabolism in the basal ganglia and thalamus of severe obsessive compulsive patients with treatment resistance: A phosphorus 31 nuclear magnetic resonance spectroscopy study.

INTRODUCTION: Cerebral metabolism in obsessive-compulsive-disorder(OCD) has been the subject of numerous studies using proton magnetic resonance spectroscopy(MRS). Despite heterogeneous results, some studies have unraveled membrane turnover and energy metabolism abnormalities in different brain regions, suggesting that alterations in these processes may contribute to the pathophysiology. So far, no authors have explored phospholipids and high-energy phosphate metabolism using 31P-MRS, which allows in vivo quantification of phosphorus metabolites that are considered to be related to membrane turnover and energy metabolism. MATERIALS AND METHODS: The aim of our study was to describe and compare brain metabolic changes using 31P-MRS in the striatum and the thalamus, between 23 severe OCD patients and 22 healthy controls. All subject underwent a clinical examination and a same 31P-MRS protocol. RESULTS: Significantly, increased concentrations of PC, PDE,PME,GPC,PME/PCr,PDE/PCr were found in patients compared to controls in the striatum and the thalamus. PCr and tATP were decreased in the striatum. Finally, significant correlations were found in the striatum and the thalamus between illness duration and some specific measured parameters. CONCLUSION: Our results showed significant modifications of the membrane and energy metabolism in the basal ganglia of severe OCD patients and suggests a link between energetic buffer and serotonin metabolism disorder.

COVID-19 impact assessment on the French radiological centers: a nationwide survey.

PURPOSE: To determine the impact of the COVID-19 on the CT activities in French radiological centers during the epidemic peak. MATERIALS AND METHODS: A cross-sectional prospective CT scan survey was conducted between March 16 and April 12, 2020, in accordance with the local IRB. Seven hundred nine radiology centers were invited to participate in a weekly online survey. Numbers of CT examinations related to COVID-19 including at least chest (CTcovid) and whole chest CT scan activities (CTchest) were recorded each week. A sub-analysis on French departments was performed during the 4 weeks of the study. The impact of the number of RT-PCRs (reverse transcriptase polymerase chain reactions) on the CT workflow was tested using two-sample t test and Pearson's test. RESULTS: Five hundred seventy-seven structures finally registered (78%) with mean response numbers of 336 ± 18.9 (323; 351). Mean CTchest activity per radiologic structure ranged from 75.8 ± 133 (0-1444) on week 12 to 99.3 ± 138.6 (0-1147) on week 13. Mean ratio of CTcovid on CTchest varied from 0.36 to 0.59 on week 12 and week 14 respectively. There was a significant relationship between the number of RT-PCR performed and the number of CTcovid (r = 0.73, p = 3.10-16) but no link with the number of positive RT-PCR results. CONCLUSION: In case of local high density COVID-19, CT workflow is strongly modified and redirected to the management of these specific patients. KEY POINTS: • Over the 4-week survey period, 117,686 chest CT (CTtotal) were performed among the responding centers, including 61,784 (52%) CT performed for COVID-19 (CTcovid). • Across the country, the ratio CTcovid/CTtotal varied from 0.36 to 0.59 and depended significantly on the local epidemic density (p = 0.003). • In clinical practice, in a context of growing epidemic, in France, chest CT was used as a surrogate to RT-PCR for patient triage.

1 H-31 P magnetic resonance spectroscopy: effect of biotin in multiple sclerosis.

Biotin is thought to improve functional impairment in progressive multiple sclerosis (MS) by upregulating bioenergetic metabolism. We enrolled 19 patients suffering from progressive MS (5 primary and 14 secondary Progressive-MS). Using cerebral multinuclear magnetic resonance spectroscopy (MMRS) and clinical evaluation before and after 6 months of biotin cure, we showed significant modifications of: PME/PDE, ATP, and lactate resonances; an improvement of EDSS Neuroscore. Our results are consistent with metabolic pathways concerned with biotin action and could suggest the usefulness of MMRS for monitoring.

Biofunctionalized and self-supported polypyrrole frameworks as nanostructured ECM-like biointerfaces.

Hybrid nanobiointerfaces were designed as an original contribution to the challenge of synthesizing nanostructured biomaterials integrating a set of cell fate-determining cues, originally provided to cells by the extracellular matrix (ECM). The produced biointerfaces consist of a stiff framework of intersected polypyrrole (PPy) nanotubes supporting a soft multilayer composed of ECM-derived biomacromolecules: collagen (Col) and hyaluronic acid (HA). PPy frameworks with highly tunable characteristics were synthesized through chemical oxidative polymerization of pyrrole monomers, templated within track-etched polycarbonate (PC) membranes featuring a network of intersected nanopores. PPy interfaces with a porosity of 80%, composed of nanotubes with an average diameter ranging from 40 to 300 nm, intersecting at an angle of 90°, were shown to be self-supported. These rigid PPy nanostructured interfaces were functionalized with a self-assembling (HA/Col) multilayer deposited via a layer-by-layer process. Biofunctionalized and unmodified PPy frameworks were both shown to promote sustained cell adhesion, therefore demonstrating the cytocompatibility of the engineered matrices. Such nanobiointerfaces, combining a mechanically-stable framework of tunable dimensions with a soft biopolymeric multilayer of highly versatile nature, pave the way towards cell-instructive biomaterials able to gather a wide range of cues guiding cell behavior. The developed self-supported structures could be used as a coating or as membranes bridging different tissues.

Matching methods evaluation framework for stereoscopic breast x-ray images.

Three-dimensional (3-D) imaging has been intensively studied in the past few decades. Depth information is an important added value of 3-D systems over two-dimensional systems. Special focuses were devoted to the development of stereo matching methods for the generation of disparity maps (i.e., depth information within a 3-D scene). Dedicated frameworks were designed to evaluate and rank the performance of different stereo matching methods but never considering x-ray medical images. Yet, 3-D x-ray acquisition systems and 3-D medical displays have already been introduced into the diagnostic market. To access the depth information within x-ray stereoscopic images, computing accurate disparity maps is essential. We aimed at developing a framework dedicated to x-ray stereoscopic breast images used to evaluate and rank several stereo matching methods. A multiresolution pyramid optimization approach was integrated to the framework to increase the accuracy and the efficiency of the stereo matching techniques. Finally, a metric was designed to score the results of the stereo matching compared with the ground truth. Eight methods were evaluated and four of them [locally scaled sum of absolute differences (LSAD), zero mean sum of absolute differences, zero mean sum of squared differences, and locally scaled mean sum of squared differences] appeared to perform equally good with an average error score of 0.04 (0 is the perfect matching). LSAD was selected for generating the disparity maps.