From Voxel to Gene: A Scoping Review on MRI Radiogenomics' Artificial Intelligence Predictions in Adult Gliomas and Glioblastomas-The Promise of Virtual Biopsy?

BACKGROUND: Gliomas, including the most severe form known as glioblastomas, are primary brain tumors arising from glial cells, with significant impact on adults, particularly men aged 45 to 70. Recent advancements in the WHO (World Health Organization) classification now correlate genetic markers with glioma phenotypes, enhancing diagnostic precision and therapeutic strategies. AIMS AND METHODS: This scoping review aims to evaluate the current state of deep learning (DL) applications in the genetic characterization of adult gliomas, addressing the potential of these technologies for a reliable virtual biopsy. RESULTS: We reviewed 17 studies, analyzing the evolution of DL algorithms from fully convolutional networks to more advanced architectures (ResNet and DenseNet). The methods involved various validation techniques, including k-fold cross-validation and external dataset validation. CONCLUSIONS: Our findings highlight significant variability in reported performance, largely due to small, homogeneous datasets and inconsistent validation methods. Despite promising results, particularly in predicting individual genetic traits, the lack of robust external validation limits the generalizability of these models. Future efforts should focus on developing larger, more diverse datasets and integrating multidisciplinary collaboration to enhance model reliability. This review underscores the potential of DL in advancing glioma characterization, paving the way for more precise, non-invasive diagnostic tools. The development of a robust algorithm capable of predicting the somatic genetics of gliomas or glioblastomas could accelerate the diagnostic process and inform therapeutic decisions more quickly, while maintaining the same level of accuracy as the traditional diagnostic pathway, which involves invasive tumor biopsies.

Effectiveness of an Artificial Intelligence Software for Limb Radiographic Fracture Recognition in an Emergency Department.

Objectives: To assess the impact of an Artificial Intelligence (AI) limb bone fracture diagnosis software (AIS) on emergency department (ED) workflow and diagnostic accuracy. Materials and Methods: A retrospective study was conducted in two phases-without AIS (Period 1: 1 January 2020-30 June 2020) and with AIS (Period 2: 1 January 2021-30 June 2021). Results: Among 3720 patients (1780 in Period 1; 1940 in Period 2), the discrepancy rate decreased by 17% (p = 0.04) after AIS implementation. Clinically relevant discrepancies showed no significant change (-1.8%, p = 0.99). The mean length of stay in the ED was reduced by 9 min (p = 0.03), and expert consultation rates decreased by 1% (p = 0.38). Conclusions: AIS implementation reduced the overall discrepancy rate and slightly decreased ED length of stay, although its impact on clinically relevant discrepancies remains inconclusive. Key Point: After AI software deployment, the rate of radiographic discrepancies decreased by 17% (p = 0.04) but this was not clinically relevant (-2%, p = 0.99). Length of patient stay in the emergency department decreased by 5% with AI (p = 0.03). Bone fracture AI software is effective, but its effectiveness remains to be demonstrated.

Investigating brain structure and tDCS response in obsessive-compulsive disorder.

Obsessive-Compulsive Disorder (OCD) is characterized by intrusive thoughts and repetitive behaviors, with associated brain abnormalities in various regions. This study explores the correlation between neural biomarkers and the response to transcranial Direct Current Stimulation (tDCS) in OCD patients. Using structural MRI data from two tDCS trials involving 55 OCD patients and 28 controls, cortical thickness, and gray matter morphometry was analyzed. Findings revealed thicker precentral and paracentral areas in OCD patients, compared to control (p < 0.001). Correlations between cortical thickness and treatment response indicated a significant association between a thinner precentral area and reduced Yale-Brown Obsessive Compulsive Scale (YBOCS) scores (p = 0.02). While results highlight the complexity of treatment response predictors, this study sheds light on potential neural markers for tDCS response in OCD patients. Further investigations with larger datasets are warranted to better understand the underpinnings of these biomarkers and their implications for personalized treatment approaches.

Associations between 24/7 workforce coverage, organizational challenges, and well-being of emergency radiologists: a national survey.

BACKGROUND: The last decade has seen a surge in the demand for imaging exams in emergency radiology (ER), necessitating an evolution in organizational systems for departments offering round-the-clock care, while safeguarding patient care quality and physician well-being to prevent burnout. PURPOSE: To develop a nationwide overview of ER organizations in France and identify structures that promote job satisfaction. MATERIAL AND METHODS: Two surveys were sent to 709 radiological centers across France from March to June 2022, inquiring about organizational aspects and quality of life (QOL), incorporating four validated QOL questionnaires. The organization of each center was mapped, and correlations between respondent characteristics and mental health were analyzed using Pearson's and Wilcoxon tests. RESULTS: A total of 284 centers answered the organizational survey, with a response rate of about 41.6%. Among them, there were 32 university hospitals, 208 general hospitals, 2 teaching army hospitals, and 42 private facilities. Of these, night-time operations showed 14% on-site coverage, 12% on-call from home, 69% utilized external teleradiology, and 4% used in-house teleradiology. These trends persisted over weekends and holidays. Regarding the quality of working life, academic, general, and private radiologists are more satisfied with their practice compared to trainees. Depersonalization, part of the three dimensions of burnout, was high in every class, at 60% (n = 210/350). CONCLUSION: Outside of university hospitals, most radiology centers in France no longer have on-site radiologists during off hours. Residents are prone to lower job satisfaction and quality of life than more experienced radiologists. CLINICAL RELEVANCE STATEMENT: The survey illustrates how French ER is structured, pointing out the escalating significance of teleradiology and noting that radiologists generally experience high job satisfaction while also confronting typical organizational challenges. KEY POINTS: The need for continuous radiology coverage comes with unique logistical challenges, especially in ER. Night shifts show a significant reliance on teleradiology services, especially by external companies. Pay, shift patterns, and seniority affect the well-being of emergency radiologists, particularly the residents.

Dual-Energy CT in Acute Stroke: Could Non-Contrast CT Be Replaced by Virtual Non-Contrast CT? A Feasibility Study.

Purpose: We aimed to evaluate whether virtual non-contrast cerebral computed tomography (VNCCT) reconstructed from intravenous contrast-enhanced dual-energy CT (iv-DECT) could replace non-contrast CT (NCCT) in patients with suspected acute cerebral ischemia. Method: This retrospective study included all consecutive patients in whom NCCT followed by iv-DECT were performed for suspected acute ischemia in our emergency department over a 1-month period. The Alberta Stroke Program Early CT Score (ASPECTS) was used to determine signs of acute ischemia in the anterior and posterior circulation, the presence of hemorrhage, and alternative findings, which were randomly evaluated via the consensus reading of NCCT and VNCCT by two readers blinded to the final diagnosis. An intraclass correlation between VNCCT and NCCT was calculated for the ASPECTS values. Both techniques were evaluated for their ability to detect ischemic lesions (ASPECTS <10) when compared with the final discharge diagnosis (reference standard). Results: Overall, 148 patients (80 men, mean age 64 years) were included, of whom 46 (30%) presented with acute ischemia, 6 (4%) presented with intracerebral hemorrhage, 11 (7%) had an alternative diagnosis, and 85 (59%) had no pathological findings. The intraclass correlation coefficients of the two modalities were 0.97 (0.96-0.98) for the anterior circulation and 0.77 (0.69-0.83) for the posterior circulation. The VNCCT's sensitivity for detecting acute ischemia was higher (41%, 19/46) than that of NCCT (33%, 15/46). Specificity was similar between the two techniques, at 94% (97/103) and 98% (101/103), respectively. Conclusions: Our results show that VNCCT achieved a similar diagnostic performance as NCCT and could, thus, replace NCCT in assessing patients with suspected acute cerebral ischemia.

Early rise of glutamate-glutamine levels in mild cognitive impairment: Evidence for emerging excitotoxicity.

BACKGROUND: Use proton magnetic resonance spectroscopy (1H-MRS) non invasive technique to assess the modifications of glutamate-glutamine (Glx) and gammaaminobutyric acid (GABA) brain levels in patients reporting a cognitive complain METHODS: Posterior cingular cortex 1H-MRS spectra of 46 patients (19 male, 27 female) aged 57 to 87 years (mean : 73.32 ± 7.33 years) with a cognitive complaint were examined with a MEGA PRESS sequence at 3T, and compounds Glutamateglutamine (Glx), GABA, Creatine (Cr) and NAA were measured. From this data the metabolite ratios Glx/Cr, GABA/Cr and NAA/Cr were calculated. In addition, all patient performed the Mini Mental State Evaluation (MMSE) and 2 groups were realized with the clinical threshold of 24. RESULTS: 16 patients with MMSE 〈 24 and 30 patients with MMSE 〉 24. Significant increase of Glx/Cr in PCC of patients with MMSE 〈 24 compared to patients with MMSE 〉 24. Moreover, GABA/Cr ratio exhibited a trend for a decrease in PCC between the two groups, while they showed a significant decrease NAA/Cr ratio. CONCLUSION: Our results concerning Glx are in agreement with a physiopathological hypothesis involving a biphasic variation of glutamate levels associated with excitotoxicity, correlated with the clinical evolution of the disease. These observations suggest that MRS assessment of glutamate levels could be helpful for both diagnosis and classification of cognitive impairment in stage.

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.

Machine learning decision tree models for multiclass classification of common malignant brain tumors using perfusion and spectroscopy MRI data.

Background: To investigate the contribution of machine learning decision tree models applied to perfusion and spectroscopy MRI for multiclass classification of lymphomas, glioblastomas, and metastases, and then to bring out the underlying key pathophysiological processes involved in the hierarchization of the decision-making algorithms of the models. Methods: From 2013 to 2020, 180 consecutive patients with histopathologically proved lymphomas (n = 77), glioblastomas (n = 45), and metastases (n = 58) were included in machine learning analysis after undergoing MRI. The perfusion parameters (rCBVmax, PSRmax) and spectroscopic concentration ratios (lac/Cr, Cho/NAA, Cho/Cr, and lip/Cr) were applied to construct Classification and Regression Tree (CART) models for multiclass classification of these brain tumors. A 5-fold random cross validation was performed on the dataset. Results: The decision tree model thus constructed successfully classified all 3 tumor types with a performance (AUC) of 0.98 for PCNSLs, 0.98 for GBM and 1.00 for METs. The model accuracy was 0.96 with a RSquare of 0.887. Five rules of classifier combinations were extracted with a predicted probability from 0.907 to 0.989 for that end nodes of the decision tree for tumor multiclass classification. In hierarchical order of importance, the root node (Cho/NAA) in the decision tree algorithm was primarily based on the proliferative, infiltrative, and neuronal destructive characteristics of the tumor, the internal node (PSRmax), on tumor tissue capillary permeability characteristics, and the end node (Lac/Cr or Cho/Cr), on tumor energy glycolytic (Warburg effect), or on membrane lipid tumor metabolism. Conclusion: Our study shows potential implementation of machine learning decision tree model algorithms based on a hierarchical, convenient, and personalized use of perfusion and spectroscopy MRI data for multiclass classification of these brain tumors.

Functional outcome in low-ASPECTS (0-5) acute ischemic stroke treated with mechanical thrombectomy: impact of laterality explored in a single-center study.

Background: There is no consensus regarding the influence of infarct laterality in patients with acute ischemic stroke due to anterior large vessel occlusion (AIS-LVO) treated with mechanical thrombectomy (MT), particularly in low-ASPECT (0-5) patients who were excluded from the initial MT studies and that participated in dedicated randomized-controlled trials that do not consider the side of the occlusion. We aimed to evaluate the role of infarct laterality on the clinical outcome in low-ASPECT AIS patients treated with MT. Material and methods: We retrospectively analyzed our institutional stroke database in our Thrombectomy-Capable Stroke Center (TCSC), including patient characteristics, procedural variables, and outcomes, between January 2015 and January 2022. Patients with acute intracranial ICA and/or proximal MCA occlusions with ASPECT ≤ 5 either on CT or MRI were included and divided into 2 groups according to the location of ischemia. The primary endpoint was a good clinical outcome at 90 days (modified Rankin Scale (mRS) score of 0-3). Results: Between January 2015 and November 2021, 817 MT were performed, of which 82 were low-ASPECT (10.0%): 41 left-sided and 41 right-sided strokes. The rates of good clinical outcome were 30.8% (12/41) for the left-sided group and 43.6% (17/41) for the right-sided group, with a p-value of 0.349. The morality rate showed no significant difference between the two groups: 39.0% (16/41) in the right stroke group and 36.6% (15/41) in the left stroke group. Conclusion: The clinical outcome was not significantly influenced by stroke laterality. The results of this single-center retrospective study indicate either a lack of strength or equal value in performing mechanical thrombectomy regardless of stroke laterality.

Usefulness of computed tomography textural analysis in renal cell carcinoma nuclear grading.

Purpose: To evaluate the usefulness of computed tomography (CT) texture descriptors integrated with machine-learning (ML) models in the identification of clear cell renal cell carcinoma (ccRCC) and for the first time papillary renal cell carcinoma (pRCC) tumor nuclear grades [World Health Organization (WHO)/International Society of Urologic Pathologists (ISUP) 1, 2, 3, and 4]. Approach: A total of 143 ccRCC and 21 pRCC patients were analyzed in this study. Texture features were extracted from late arterial phase CT images. A complete separation of training/validation and testing subsets from the beginning to the end of the pipeline was adopted. Feature dimension was reduced by collinearity analysis and Gini impurity-based feature selection. The synthetic minority over-sampling technique was employed for imbalanced datasets. The ML classifiers were logistic regression, SVM, RF, multi-layer perceptron, and K -NN. The differentiation between low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and between all grades was assessed for ccRCC and pRCC datasets. The classification performance was assessed and compared by certain metrics. Results: Textures-based classifiers were able to efficiently identify ccRCC and pRCC grades. An accuracy and area under the characteristic operating curve (AUC) up to 91%/0.9, 91%/0.9, 90%/0.9, and 88%/1 were reached when discriminating ccRCC low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and all grades, respectively. An accuracy and AUC up to 96%/1, 81%/0.8, 86%/0.9, and 88%/0.9 were found when differentiating pRCC low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and all grades, respectively. Conclusion: CT texture-based ML models can be used to assist radiologist in predicting the WHO/ISUP grade of ccRCC and pRCC pre-operatively.

On a tumor growth model with brain lactate kinetics.

Our aim in this paper is to study a mathematical model for high grade gliomas, taking into account lactates kinetics, as well as chemotherapy and antiangiogenic treatment. In particular, we prove the existence and uniqueness of biologically relevant solutions. We also perform numerical simulations based on different therapeutical situations that can be found in the literature. These simulations are consistent with what is expected in these situations.

Baseline Clinical and Neuroimaging Biomarkers of Treatment Response to High-Frequency rTMS Over the Left DLPFC for Resistant Depression.

Background: Repetitive transcranial magnetic stimulation (rTMS) has proven to be an efficient treatment option for patients with treatment-resistant depression (TRD). However, the success rate of this method is still low, and the treatment outcome is unpredictable. The objective of this study was to explore clinical and structural neuroimaging factors as potential biomarkers of the efficacy of high-frequency (HF) rTMS (20 Hz) over the left dorso-lateral pre-frontal cortex (DLPFC). Methods: We analyzed the records of 131 patients with mood disorders who were treated with rTMS and were assessed at baseline at the end of the stimulation and at 1 month after the end of the treatment. The response is defined as a 50% decrease in the MADRS score between the first and the last assessment. Each of these patients underwent a T1 MRI scan of the brain, which was subsequently segmented with FreeSurfer. Whole-brain analyses [Query, Design, Estimate, Contrast (QDEC)] were conducted and corrected for multiple comparisons. Additionally, the responder status was also analyzed using binomial multivariate regression models. The explored variables were clinical and anatomical features of the rTMS target obtained from T1 MRI: target-scalp distance, DLPFC gray matter thickness, and various cortical measures of interest previously studied. Results: The results of a binomial multivariate regression model indicated that depression type (p = 0.025), gender (p = 0.010), and the severity of depression (p = 0.027) were found to be associated with response to rTMS. Additionally, the resistance stage showed a significant trend (p = 0.055). Whole-brain analyses on volume revealed that the average volume of the left part of the superior frontal and the caudal middle frontal regions is associated with the response status. Other MRI-based measures are not significantly associated with response to rTMS in our population. Conclusion: In this study, we investigated the clinical and neuroimaging biomarkers associated with responsiveness to high-frequency rTMS over the left DLPFC in a large sample of patients with TRD. Women, patients with bipolar depressive disorder (BDD), and patients who are less resistant to HF rTMS respond better. Responders present a lower volume of the left part of the superior frontal gyrus and the caudal middle frontal gyrus. These findings support further investigation into the use of clinical variables and structural MRI as possible biomarkers of rTMS treatment response.

Attention-guided neural network for early dementia detection using MRS data.

Imaging bio-markers have been widely used for Computer-Aided Diagnosis (CAD) of Alzheimer's Disease (AD) with Deep Learning (DL). However, the structural brain atrophy is not detectable at an early stage of the disease (namely for Mild Cognitive Impairment (MCI) and Mild Alzheimer's Disease (MAD)). Indeed, potential biological bio-markers have been proved their ability to early detect brain abnormalities related to AD before brain structural damage and clinical manifestation. Proton Magnetic Resonance Spectroscopy (1H-MRS) provides a promising solution for biological brain changes detection in a no invasive manner. In this paper, we propose an attention-guided supervised DL framework for early AD detection using 1H-MRS data. In the early stages of AD, features may be closely related and often complex to delineate between subjects. Hence, we develop a 1D attention mechanism that explicitly guides the classifier to focus on diagnostically relevant metabolites for classes discrimination. Synthetic data are used to tackle the lack of data problem and to help in learning the feature space. Data used in this paper are collected in the University Hospital of Poitiers, which contained 111 1H-MRS samples extracted from the Posterior Cingulate Cortex (PCC) brain region. The data contain 33 Normal Control (NC), 49 MCI due to AD, and 29 MAD subjects. The proposed model achieves an average classification accuracy of 95.23%. Our framework outperforms state of the art imaging-based approaches, proving the robustness of learning metabolites features against traditional imaging bio-markers for early AD detection.

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.

Carotid artery direct access for mechanical thrombectomy: the Carotid Artery Puncture Evaluation (CARE) study.

BACKGROUND: In acute ischemic stroke due to anterior large vessel occlusion (AIS-LVO), accessing the target occluded vessel for mechanical thrombectomy (MT) is sometimes impossible through the femoral approach. We aimed to evaluate the safety and efficacy of direct carotid artery puncture (DCP) for MT in patients with failed alternative vascular access. METHODS: We retrospectively analyzed data from 45 stroke centers in France, Switzerland and Germany through two research networks from January 2015 to July 2019. We collected physician-centered data on DCP practices and baseline characteristics, procedural variables and clinical outcome after DCP. Uni- and multivariable models were conducted to assess risk factors for complications. RESULTS: From January 2015 to July 2019, 28 149 MT were performed, of which 108 (0.39%) resulted in DCP due to unsuccessful vascular access. After DCP, 77 patients (71.3%) had successful reperfusion (modified Thrombolysis In Cerebral Infarction (mTICI) score ≥2b) and 28 (25.9%) were independent (modified Rankin Scale (mRS) score 0-2) at 3 months. 20 complications (18.5%) attributed to DCP occurred, all of them during or within 1 hour of the procedure. Complications led to extension of the intubation time in the intensive care unit in 7 patients (6.4%) and resulted in death in 3 (2.8%). The absence of use of a hemostatic closure device was associated with a higher complication risk (OR 3.04, 95% CI 1.03 to 8.97; p=0043). CONCLUSION: In this large multicentric study, DCP was scantly performed for vascular access to perform MT (0.39%) in patients with AIS-LVO and had a high rate of complications (18.5%). Our results provide arguments for not closing the cervical access by manual compression after MT.

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.

TEMOBIC: Phase II Trial of Neoadjuvant Chemotherapy for Unresectable Anaplastic Gliomas: An ANOCEF Study.

LESSONS LEARNED: Treatment with temozolomide and BCNU was associated with substantial response and survival rates for patients with unresectable anaplastic glioma, suggesting potential therapeutic alternative for these patients. The optimal treatment for unresectable large anaplastic gliomas remains debated. BACKGROUND: The optimal treatment for unresectable large anaplastic gliomas remains debated. METHODS: Adult patients with histologically proven unresectable anaplastic oligodendroglioma or mixed gliomas (World Health Organization [WHO] 2007) were eligible. Treatment consisted of BCNU (150 mg/m2 ) and temozolomide (110 mg/m2 for 5 days) every 6 weeks for six cycles before radiotherapy. RESULTS: Between December 2005 and December 2009, 55 patients (median age of 53.1 years; range, 20.5-70.2) were included. Forty percent of patients presented with wild-type IDH1 gliomas, and 30% presented with methylated MGMT promoter. Median progression-free survival (PFS), centralized PFS, and overall survival (OS) were 16.6 (95% confidence interval [CI], 12.8-20.3), 15.4 (95% CI, 10.0-20.8), and 25.4 (95% CI, 17.5-33.2) months, respectively. Complete and partial responses under chemotherapy were observed for 28.3% and 17% of patients, respectively. Radiotherapy completion was achieved for 75% of patients. Preservation of functional status and self-care capability (Karnofsky performance status [KPS] ≥70) were preserved until disease progression for 69% of patients. Grade ≥ 3 toxicities were reported for 52% of patients, and three deaths were related to treatment. By multivariate analyses including age and KPS, IDH mutation was associated with better prognostic for both PFS and OS, whereas MGMT promoter methylation was associated with better OS. CONCLUSION: The association of BCNU and temozolomide upfront is active for patients with unresectable anaplastic gliomas, but toxicity limits its use.

Effectiveness of CT-guided epidural infiltration of steroids and local anesthetics for acute and chronic herpes zoster neuralgia.

PURPOSE: The purpose of this study was to evaluate the effectiveness and complication rate of computed tomography (CT)-guided epidural injection of steroids and local anesthetics for pain relief in patients with neuralgia due to acute or chronic herpes zoster (HZ). MATERIALS AND METHODS: A prospective study was conducted from April 2017 to February 2019 including patients with HZ neuralgia (HZN) at any stage (acute or chronic, the latter being defined as pain lasting more than 3 months and also called post herpetic neuralgia [PHN]). The sensory ganglion of the affected dermatome and/or the affected sensory nerve was targeted under CT-guidance and local injection of a mixture of two vials of methylprednisolone 40mg/mL and 2mL of Lidocaine 1% was performed. Using a visual analogue scale (VAS, 0 to 10), pain was assessed prior to the procedure, and at day 7, 1 month, 3 months and 6 months. Adverse effects were graded according to the Society of Interventional Radiology classification. RESULTS: Twenty patients were included. There were 9 men and 11 women with a mean age of 67±13.9 (SD) years (range: 27-83 years). Of these, 14 patients had acute HZN and 6 had PHN. Mean VAS at baseline was 8.1±1.2 (SD) (range: 6-10) with significant decrease (P<0.0001) at day 7 (3.4±3.2 [SD]; range: 0-10), day 30 (3.4±3.2 [SD]; range: 0-9), day 90 (2.9±3.2 [SD]; range: 0-9), and day 180 (2.5±3.1 [SD]; range: 0-9). Infiltrations were significantly more effective on acute HZN than on PHN (P<0.001) and required significantly fewer infiltrations for pain relef (P=0.002). Only one grade A adverse event was reported. CONCLUSION: Epidural injection of a mixture of steroids and local anesthetics under CT-guidance is effective on HZN with a persisting effect over 6 months.