Prediction of shunt response in idiopathic normal pressure hydrocephalus by combined lumbar infusion test and preoperative imaging scoring.

BACKGROUND AND PURPOSE: Idiopathic normal pressure hydrocephalus (iNPH) is a potentially treatable disorder, but prognostic tests or biomarkers are lacking. The aim was to study the predictive power of clinical, neuroimaging and lumbar infusion test parameters (resistance to outflow Rout , cardiac-related pulse amplitude PA and the PA to intracranial pressure ICP ratio). METHODS: In all, 127 patients diagnosed with iNPH who had a lumbar infusion test, a subsequent ventriculo-peritoneal shunt operation and at least 2 months of postoperative follow-up were retrospectively included. Preoperative magnetic resonance images were visually scored for NPH features using the iNPH Radscale. Preoperative and postoperative assessment was performed using cognitive testing, as well as gait and incontinence scales. RESULTS: At follow-up (7.4 months, range 2-20 months), an overall positive response was seen in 82% of the patients. Gait was more severely impaired at baseline in responders compared to non-responders. The iNPH Radscale score was borderline significantly higher in responders compared with non-responders, whereas no significant differences in infusion test parameters were seen between responders and non-responders. Infusion test parameters performed modestly with high positive (75%-92%) but low negative (17%-23%) predictive values. Although not significant, PA and PA/ICP seemed to perform better than Rout , and the odds ratio for shunt response seemed to increase in patients with higher PA/ICP, especially in patients with lower iNPH Radscale scores. CONCLUSION: Although only indicative, lumbar infusion test results increased the likelihood of a positive shunt outcome. Pulse amplitude measures showed promising results that should be further explored in prospective studies.

Ensuring competence in ultrasound-guided procedures-a validity study of a newly developed assessment tool.

OBJECTIVES: To investigate the validity of the Interventional Ultrasound Skills Evaluation (IUSE) tool for assessment of procedural competence in ultrasound-guided procedures in a clinical environment, including a pass/fail score. METHODS: Novices and experienced radiologists were recruited from four hospitals and were observed and assessed while performing ultrasound-guided procedures. Performances were assessed using the IUSE tool by two independent raters. Validity evidence was gathered in accordance with Messick's framework: response process was ensured by standardisation of written rater instructions. Internal structure was explored using Cronbach's alpha for internal consistency reliability; inter-rater reliability was calculated as Pearson's r independently across all ratings, and test-retest reliability was reported using Cronbach's alpha. Relationship to other variables was investigated by comparing performances of the participants in each group. Consequences evidence was explored by calculating a pass/fail standard using the contrasting groups method. RESULTS: Six novices and twelve experienced radiologists were enrolled. The IUSE tool had high internal consistency (Cronbach's alpha = 0.96, high inter-rater reliability (Pearson's r = 0.95), and high test-retest reliability (Cronbach's alpha = 0.98), and the mean score was 33.28 for novices and 59.25 for experienced with a highly significant difference (p value < 0.001). The pass/fail score was set at 55 resulting in no false positives or false negatives. CONCLUSIONS: Validity evidence from multiple sources supports the use of the IUSE tool for assessment of competence in ultrasound-guided procedures in a clinical environment and its use in high-stakes assessment such as certification. A credible pass/fail criterion was established to inform decision-making. KEY POINTS: • A multi-site validity investigation established that the Interventional Ultrasound Skills Evaluation (IUSE) tool can be used to assess procedural competence in ultrasound-guided procedures. • Validity evidence was gathered according to Messick's framework validity from the following sources: response process, internal structure, relationship to other variables, and consequences evidence. • The IUSE tool can be used for both formative and summative assessment, and a credible pass/fail score was established to help inform decision-making such as certification.

Can preoperative brain imaging features predict shunt response in idiopathic normal pressure hydrocephalus? A PRISMA review.

PURPOSE: This systematic literature review aimed to identify brain computed tomography (CT) and magnetic resonance imaging (MRI) features that could be used to discriminate idiopathic normal pressure hydrocephalus (iNPH) shunt responders from non-responders. METHODS: PubMed, Embase, Web of Science, and Cochrane were searched following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles reporting preoperative CT and/or MRI features and iNPH shunt response evaluated by changes in gait, dementia, and urinary incontinence were included. Title and abstract screening and full-text article evaluation were done by two authors. Data on patient demographics and inclusion criteria, brain image evaluation, shunting methods, and shunt response evaluation were recorded. RESULTS: The search resulted in 1274 studies after removing duplicates. Twenty-seven studies were chosen for final review. Both structural (i.e., callosal angle, disproportionately enlarged subarachnoid space hydrocephalus (DESH), and temporal horn diameter) and physiological brain imaging (including aqueductal flow measurement and brain perfusion) had been examined. Fourteen out of 27 studies found no difference in any assessed imaging parameters between responders and non-responders, and none of the examined imaging parameters was repeatedly and consistently reported as significantly different between the two groups. CONCLUSIONS: No brain imaging parameters were consistently and repeatedly reported as different between iNPH shunt responders and non-responders.

Ultrasound Elastography Is Useful for Evaluation of Liver Fibrosis in Children-A Systematic Review.

OBJECTIVES: Adult studies have proven ultrasound elastography as a validated measure of liver fibrosis. The present study aimed to review the available literature on ultrasound elastography in children to evaluate the ability of the method to distinguish healthy from fibrotic liver tissue and investigate whether cutoff values for liver fibrosis in children have been established. METHODS: A literature search was performed in MEDLINE, EMBASE, the Cochrane Library, and Web of Science to identify studies on ultrasound elastography of the liver in children. Only original research articles in English concerning ultrasound elastography in children with and without liver disease, younger than 18 years, were included. All reference lists of the included articles were hand-searched for further references. RESULTS: Twenty-seven articles were included. Elastography in children without liver disease was investigated in 14 studies and were comparable to those existing for adults. Twelve studies compared elastography with liver biopsy in children with liver disease and found that cirrhosis was correctly diagnosed, whereas it was more difficult to assess severe fibrosis correctly. For the distinction between no, mild, and moderate fibrosis in children with liver disease the method was less accurate. Ultrasound elastography was able to differentiate between children with and without liver fibrosis. In children without liver disease ultrasound, elastography showed consistent liver stiffness values comparable to those found in adults. No fibrosis-specific cutoffs were proposed. CONCLUSIONS: Ultrasound elastography was able to diagnose cirrhosis, distinguish healthy from fibrotic liver tissue, and showed consistent liver stiffness values in children without liver disease.

Prognostic relevance of radiological findings on early postoperative MRI for 187 consecutive glioblastoma patients receiving standard therapy.

Several prognostic factors are known to influence survival for patients treated with IDH-wildtype glioblastoma, but unknown factors may remain. We aimed to investigate the prognostic implications of early postoperative MRI findings. A total of 187 glioblastoma patients treated with standard therapy were consecutively included. Patients either underwent a biopsy or surgery followed by an early postoperative MRI. Progression-free survival (PFS) and overall survival (OS) were analysed for known prognostic factors and MRI-derived candidate factors: resection status as defined by the response assessment in neuro-oncology (RANO)-working group (no contrast-enhancing residual tumour, non-measurable contrast-enhancing residual tumour, or measurable contrast-enhancing residual tumour) with biopsy as reference, contrast enhancement patterns (no enhancement, thin linear, thick linear, diffuse, nodular), and the presence of distant tumours. In the multivariate analysis, patients with no contrast-enhancing residual tumour or non-measurable contrast-enhancing residual tumour on the early postoperative MRI displayed a significantly improved progression-free survival compared with patients receiving only a biopsy. Only patients with non-measurable contrast-enhancing residual tumour showed improved overall survival in the multivariate analysis. Contrast enhancement patterns were not associated with survival. The presence of distant tumours was significantly associated with both poor progression-free survival and overall survival and should be considered incorporated into prognostic models.

Trigeminal ganglion neurons are directly activated by influx of CSF solutes in a migraine model.

Classical migraine patients experience aura, which is transient neurological deficits associated with cortical spreading depression (CSD), preceding headache attacks. It is not currently understood how a pathological event in cortex can affect peripheral sensory neurons. In this study, we show that cerebrospinal fluid (CSF) flows into the trigeminal ganglion, establishing nonsynaptic signaling between brain and trigeminal cells. After CSD, ~11% of the CSF proteome is altered, with up-regulation of proteins that directly activate receptors in the trigeminal ganglion. CSF collected from animals exposed to CSD activates trigeminal neurons in naïve mice in part by CSF-borne calcitonin gene-related peptide (CGRP). We identify a communication pathway between the central and peripheral nervous system that might explain the relationship between migrainous aura and headache.

Pseudo-Label Guided Image Synthesis for Semi-Supervised COVID-19 Pneumonia Infection Segmentation.

Coronavirus disease 2019 (COVID-19) has become a severe global pandemic. Accurate pneumonia infection segmentation is important for assisting doctors in diagnosing COVID-19. Deep learning-based methods can be developed for automatic segmentation, but the lack of large-scale well-annotated COVID-19 training datasets may hinder their performance. Semi-supervised segmentation is a promising solution which explores large amounts of unlabelled data, while most existing methods focus on pseudo-label refinement. In this paper, we propose a new perspective on semi-supervised learning for COVID-19 pneumonia infection segmentation, namely pseudo-label guided image synthesis. The main idea is to keep the pseudo-labels and synthesize new images to match them. The synthetic image has the same COVID-19 infected regions as indicated in the pseudo-label, and the reference style extracted from the style code pool is added to make it more realistic. We introduce two representative methods by incorporating the synthetic images into model training, including single-stage Synthesis-Assisted Cross Pseudo Supervision (SA-CPS) and multi-stage Synthesis-Assisted Self-Training (SA-ST), which can work individually as well as cooperatively. Synthesis-assisted methods expand the training data with high-quality synthetic data, thus improving the segmentation performance. Extensive experiments on two COVID-19 CT datasets for segmenting the infections demonstrate our method is superior to existing schemes for semi-supervised segmentation, and achieves the state-of-the-art performance on both datasets. Code is available at: https://github.com/FeiLyu/SASSL.

Timing of Early Postoperative MRI following Primary Glioblastoma Surgery-A Retrospective Study of Contrast Enhancements in 311 Patients.

An early postoperative MRI is recommended following Glioblastoma surgery. This retrospective, observational study aimed to investigate the timing of an early postoperative MRI among 311 patients. The patterns of the contrast enhancement (thin linear, thick linear, nodular, and diffuse) and time from surgery to the early postoperative MRI were recorded. The primary endpoint was the frequencies of the different contrast enhancements within and beyond the 48-h from surgery. The time dependence of the resection status and the clinical parameters were analysed as well. The frequency of the thin linear contrast enhancements significantly increased from 99/183 (50.8%) within 48-h post-surgery to 56/81 (69.1%) beyond 48-h post-surgery. Similarly, MRI scans with no contrast enhancements significantly declined from 41/183 (22.4%) within 48-h post-surgery to 7/81 (8.6%) beyond 48-h post-surgery. No significant differences were found for the other types of contrast enhancements and the results were robust in relation to the choice of categorisation of the postoperative periods. Both the resection status and the clinical parameters were not statistically different in patients with an MRI performed before and after 48 h. The findings suggest that surgically induced contrast enhancements are less frequent when an early postoperative MRI is performed earlier than 48-h, supporting the recommendation of a 48-h window for an early postoperative MRI.

Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI.

In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation of a state-of-the-art deep learning 3D brain tumour segmentation algorithm (HD-GLIO) on an independent cohort of consecutive, post-operative patients. For 66 consecutive magnetic resonance imaging examinations, we compared delineations of contrast-enhancing (CE) tumour lesions and non-enhancing T2/FLAIR hyperintense abnormality (NE) lesions by the HD-GLIO algorithm and radiologists using Dice similarity coefficients (Dice). Volume agreement was assessed using concordance correlation coefficients (CCCs) and Bland-Altman plots. The algorithm performed very well regarding the segmentation of NE volumes (median Dice = 0.79) and CE tumour volumes larger than 1.0 cm3 (median Dice = 0.86). If considering all cases with CE tumour lesions, the performance dropped significantly (median Dice = 0.40). Volume agreement was excellent with CCCs of 0.997 (CE tumour volumes) and 0.922 (NE volumes). The findings have implications for the application of the HD-GLIO algorithm in the routine radiological workflow where small contrast-enhancing tumours will constitute a considerable share of the follow-up cases. Our study underlines that independent validations on clinical datasets are key to asserting the robustness of deep learning algorithms.

Artificial Intelligence for Automated DWI/FLAIR Mismatch Assessment on Magnetic Resonance Imaging in Stroke: A Systematic Review.

We conducted this Systematic Review to create an overview of the currently existing Artificial Intelligence (AI) methods for Magnetic Resonance Diffusion-Weighted Imaging (DWI)/Fluid-Attenuated Inversion Recovery (FLAIR)-mismatch assessment and to determine how well DWI/FLAIR mismatch algorithms perform compared to domain experts. We searched PubMed Medline, Ovid Embase, Scopus, Web of Science, Cochrane, and IEEE Xplore literature databases for relevant studies published between 1 January 2017 and 20 November 2022, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We assessed the included studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Five studies fit the scope of this review. The area under the curve ranged from 0.74 to 0.90. The sensitivity and specificity ranged from 0.70 to 0.85 and 0.74 to 0.84, respectively. Negative predictive value, positive predictive value, and accuracy ranged from 0.55 to 0.82, 0.74 to 0.91, and 0.73 to 0.83, respectively. In a binary classification of ±4.5 h from stroke onset, the surveyed AI methods performed equivalent to or even better than domain experts. However, using the relation between time since stroke onset (TSS) and increasing visibility of FLAIR hyperintensity lesions is not recommended for the determination of TSS within the first 4.5 h. An AI algorithm on DWI/FLAIR mismatch assessment focused on treatment eligibility, outcome prediction, and consideration of patient-specific data could potentially increase the proportion of stroke patients with unknown onset who could be treated with thrombolysis.

An Automatic DWI/FLAIR Mismatch Assessment of Stroke Patients.

DWI/FLAIR mismatch assessment for ischemic stroke patients shows promising results in determining if patients are eligible for recombinant tissue-type plasminogen activator (r-tPA) treatment. However, the mismatch criteria suffer from two major issues: binary classification of a non-binary problem and the subjectiveness of the assessor. In this article, we present a simple automatic method for segmenting stroke-related parenchymal hyperintensities on FLAIR, allowing for an automatic and continuous DWI/FLAIR mismatch assessment. We further show that our method's segmentations have comparable inter-rater agreement (DICE 0.820, SD 0.12) compared to that of two neuro-radiologists (DICE 0.856, SD 0.07), that our method appears robust to hyper-parameter choices (suggesting good generalizability), and lastly, that our methods continuous DWI/FLAIR mismatch assessment correlates to mismatch assessments made for a cohort of wake-up stroke patients at hospital submission. The proposed method shows promising results in automating the segmentation of parenchymal hyperintensity within ischemic stroke lesions and could help reduce inter-observer variability of DWI/FLAIR mismatch assessment performed in clinical environments as well as offer a continuous assessment instead of the current binary one.

Performance and Agreement When Annotating Chest X-ray Text Reports-A Preliminary Step in the Development of a Deep Learning-Based Prioritization and Detection System.

A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray reports. Reports written between 1 January 2015 and 11 March 2022 were selected based on search words. Annotators included three board-certified radiologists, two trained radiologists (physicians), two radiographers (radiological technicians), a non-radiological physician, and a medical student. Consensus labels by two or more of the experienced radiologists were considered "gold standard". Matthew's correlation coefficient (MCC) was calculated to assess annotation performance, and descriptive statistics were used to assess agreement between individual annotators and labels. The intermediate radiologist had the best correlation to "gold standard" (MCC 0.77). This was followed by the novice radiologist and medical student (MCC 0.71 for both), the novice radiographer (MCC 0.65), non-radiological physician (MCC 0.64), and experienced radiographer (MCC 0.57). Our findings showed that for developing an artificial intelligence-based support system, if trained radiologists are not available, annotations from non-radiological annotators with basic and general knowledge may be more aligned with radiologists compared to annotations from sub-specialized medical staff, if their sub-specialization is outside of diagnostic radiology.

Multimodality Imaging in Cranial Giant Cell Arteritis: First Experience with High-Resolution T1-Weighted 3D Black Blood without Contrast Enhancement Magnetic Resonance Imaging.

In order to support or refute the clinical suspicion of cranial giant cell arteritis (GCA), a supplemental imaging modality is often required. High-resolution black blood Magnetic Resonance Imaging (BB MRI) techniques with contrast enhancement can visualize artery wall inflammation in GCA. We compared findings on BB MRI without contrast enhancement with findings on 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/low-dose computed tomography (2-[18F]FDG PET/CT) in ten patients suspected of having GCA and in five control subjects who had a 2-[18F]FDG PET/CT performed as a routine control for malignant melanoma. BB MRI was consistent with 2-[18F]FDG PET/CT in 10 out of 10 cases in the group with suspected GCA. In four out of five cases in the control group, the BB MRI was consistent with 2-[18F]FDG PET/CT. In this small population, BB MRI without contrast enhancement shows promising performance in the diagnosis of GCA, and might be an applicable imaging modality in patients.

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review.

We conducted a systematic review of the current status of machine learning (ML) algorithms' ability to identify multiple brain diseases, and we evaluated their applicability for improving existing scan acquisition and interpretation workflows. PubMed Medline, Ovid Embase, Scopus, Web of Science, and IEEE Xplore literature databases were searched for relevant studies published between January 2017 and February 2022. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The applicability of ML algorithms for successful workflow improvement was qualitatively assessed based on the satisfaction of three clinical requirements. A total of 19 studies were included for qualitative synthesis. The included studies performed classification tasks (n = 12) and segmentation tasks (n = 7). For classification algorithms, the area under the receiver operating characteristic curve (AUC) ranged from 0.765 to 0.997, while accuracy, sensitivity, and specificity ranged from 80% to 100%, 72% to 100%, and 65% to 100%, respectively. For segmentation algorithms, the Dice coefficient ranged from 0.300 to 0.912. No studies satisfied all clinical requirements for successful workflow improvements due to key limitations pertaining to the study's design, study data, reference standards, and performance reporting. Standardized reporting guidelines tailored for ML in radiology, prospective study designs, and multi-site testing could help alleviate this.

Inter- and Intra-Observer Agreement When Using a Diagnostic Labeling Scheme for Annotating Findings on Chest X-rays-An Early Step in the Development of a Deep Learning-Based Decision Support System.

Consistent annotation of data is a prerequisite for the successful training and testing of artificial intelligence-based decision support systems in radiology. This can be obtained by standardizing terminology when annotating diagnostic images. The purpose of this study was to evaluate the annotation consistency among radiologists when using a novel diagnostic labeling scheme for chest X-rays. Six radiologists with experience ranging from one to sixteen years, annotated a set of 100 fully anonymized chest X-rays. The blinded radiologists annotated on two separate occasions. Statistical analyses were done using Randolph's kappa and PABAK, and the proportions of specific agreements were calculated. Fair-to-excellent agreement was found for all labels among the annotators (Randolph's Kappa, 0.40-0.99). The PABAK ranged from 0.12 to 1 for the two-reader inter-rater agreement and 0.26 to 1 for the intra-rater agreement. Descriptive and broad labels achieved the highest proportion of positive agreement in both the inter- and intra-reader analyses. Annotating findings with specific, interpretive labels were found to be difficult for less experienced radiologists. Annotating images with descriptive labels may increase agreement between radiologists with different experience levels compared to annotation with interpretive labels.

New Trends in Vascular Imaging.

Blood flow is essential to life and intertwined with all processes in the human body [...].

Surgically Induced Contrast Enhancements on Intraoperative and Early Postoperative MRI Following High-Grade Glioma Surgery: A Systematic Review.

For the radiological assessment of resection of high-grade gliomas, a 72-h diagnostic window is recommended to limit surgically induced contrast enhancements. However, such enhancements may occur earlier than 72 h post-surgery. This systematic review aimed to assess the evidence on the timing of the postsurgical MRI. PubMed, Embase, Web of Science and Cochrane were searched following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles describing surgically induced contrast enhancements on MRI after resection for high-grade gliomas were included and analysed. The frequency of different contrast enhancement patterns on intraoperative MRI (iMRI) and early postoperative MRI (epMRI) was recorded. The search resulted in 1443 studies after removing duplicates, and a total of 12 studies were chosen for final review. Surgically induced contrast enhancements were reported at all time points after surgery, including on iMRI, but their type and frequency vary. Thin linear contrast enhancements were commonly found to be surgically induced and were less frequently recorded on postoperative days 1 and 2. This suggests that the optimal time to scan may be at or before this time. However, the evidence is limited, and higher-quality studies using larger and consecutively sampled populations are needed.

MRI of the Entire Spinal Cord-Worth the While or Waste of Time? A Retrospective Study of 74 Patients with Multiple Sclerosis.

Spinal cord lesions are included in the diagnosis of multiple sclerosis (MS), yet spinal cord MRI is not mandatory for diagnosis according to the latest revisions of the McDonald Criteria. We investigated the distribution of spinal cord lesions in MS patients and examined how it influences the fulfillment of the 2017 McDonald Criteria. Seventy-four patients with relapsing-remitting MS were examined with brain and entire spinal cord MRI. Sixty-five patients received contrast. The number and anatomical location of MS lesions were assessed along with the Expanded Disability Status Scale (EDSS). A Chi-square test, Fischer's exact test, and one-sided McNemar's test were used to test distributions. MS lesions were distributed throughout the spinal cord. Diagnosis of dissemination in space (DIS) was increased from 58/74 (78.4%) to 67/74 (90.5%) when adding cervical spinal cord MRI to brain MRI alone (p = 0.004). Diagnosis of dissemination in time (DIT) was not significantly increased when adding entire spinal cord MRI to brain MRI alone (p = 0.04). There was no association between the number of spinal cord lesions and the EDSS score (p = 0.71). MS lesions are present throughout the spinal cord, and spinal cord MRI may play an important role in the diagnosis and follow-up of MS patients.