Purpose
A new form of autosomal dominant hereditary spinocerebellar ataxia (SCA) has been recently described (SCA48), and here we investigate its conventional MRI findings to identify the presence of ...a possible imaging feature of this condition.
Methods
In this retrospective observational study, we evaluated conventional MRI scans from 10 SCA48 patients (M/F = 5/5; 44.7 ± 7.8 years). For all subjects, atrophy of both supratentorial and infratentorial compartments were recorded, as well as the presence of possible T2-weighted imaging (T2WI) signal alterations.
Results
In SCA48 patients, no meaningful supratentorial changes were found, both in terms of volume loss or MRI signal changes. Atrophy of the cerebellum was present in all cases, involving both the vermis and the hemispheres, but particularly affecting the postero-lateral portions of the cerebellar hemispheres. In all patients, with the exception of only one subject (90.0% of the cases), a T2WI hyperintensity of both dentate nuclei was found. The association of such signal alteration with the pattern of cerebellar atrophy resembled the appearance of a crab (“crab sign”).
Conclusion
Our findings suggest that SCA48 patients are characterized by cerebellar atrophy, mainly involving the postero-lateral hemisphere areas, along with a T2WI hyperintensity of dentate nuclei. We propose that the association of such signal change, along with the atrophy of the lateral portion of the cerebellar hemispheres, resembled the appearance of a crab, and therefore, we propose the “crab sign” as a neuroradiological sign present in SCA48 patients.
Purpose
The clinical presentation of idiopathic normal pressure hydrocephalus (iNPH) may overlap with progressive supranuclear palsy (PSP). The Magnetic Resonance Parkinsonism Index (MRPI), MRPI 2.0, ...and the interpeduncular angle (IPA) have been investigated to differentiate PSP from healthy controls (HC) and other parkinsonisms. We aimed to assess equivalences and differences in MRPI, MRPI 2.0, and IPA in iNPH, PSP, and HC groups.
Methods
We retrospectively recruited 99 subjects (30 iNPH, 32 PSP, 37 HC) from two institutions. MRI exams, acquired on either 1.5 T or 3 T scanners, included 3D T1-weighted images to measure MRPI, MRPI 2.0, and IPA. Inter- and intra-rater reliability was investigated with the intra-class correlation coefficient (ICC), and the two one-sided
t
tests (TOST) procedure was used to assess these markers in iNPH, PSP, and HC.
Results
For all the three measures, intra-rater and inter-rater ICC were excellent (range = 0.91–0.93).
In the comparison of iNPH and PSP with HC, differences for MRPI and MRPI 2.0 (
p
< 0.01 in all cases) and no equivalence (
p
= 1.00 in all cases) were found at TOST. iNPH and PSP MRPI showed no difference (
p
= 0.06) and no equivalence (
p
= 0.08). MRPI 2.0 was not equivalent (
p
= 0.06) and not different (
p
= 0.09) in the same two populations. PSP and HC IPA proved equivalent (
p
< 0.01) while iNPH IPA was different (
p
< 0.01) and not equivalent (
p
= 0.96 and 0.82) from both PSP and HC.
Conclusion
MRPI and MRPI 2.0 significantly overlap in iNPH and PSP, with risk of misdiagnosis, and for this reason may not be helpful in the differential diagnosis.
In the field of computer science, known as artificial intelligence, algorithms imitate reasoning tasks that are typically performed by humans. The techniques that allow machines to learn and get ...better at tasks such as recognition and prediction, which form the basis of clinical practice, are referred to as machine learning, which is a subfield of artificial intelligence. The number of artificial intelligence-and machine learnings-related publications in clinical journals has grown exponentially, driven by recent developments in computation and the accessibility of simple tools. However, clinicians are often not included in data science teams, which may limit the clinical relevance, explanability, workflow compatibility, and quality improvement of artificial intelligence solutions. Thus, this results in the language barrier between clinicians and artificial intelligence developers. Healthcare practitioners sometimes lack a basic understanding of artificial intelligence research because the approach is difficult for non-specialists to understand. Furthermore, many editors and reviewers of medical publications might not be familiar with the fundamental ideas behind these technologies, which may prevent journals from publishing high-quality artificial intelligence studies or, worse still, could allow for the publication of low-quality works. In this review, we aim to improve readers’ artificial intelligence literacy and critical thinking. As a result, we concentrated on what we consider the 10 most important qualities of artificial intelligence research: valid scientific purpose, high-quality data set, robust reference standard, robust input, no information leakage, optimal bias-variance tradeoff, proper model evaluation, proven clinical utility, transparent reporting, and open science. Before designing a study, one should have defined a sound scientific purpose. Then, it should be backed by a high-quality data set, robust input, and a solid reference standard. The artificial intelligence development pipeline should prevent information leakage. For the models, optimal bias-variance tradeoff should be achieved, and generalizability assessment must be adequately performed. The clinical value of the final models must also be established. After the study, thought should be given to transparency in publishing the process and results as well as open science for sharing data, code, and models. We hope this work may improve the artificial intelligence literacy and mindset of the readers.
The sellar region and its boundaries represent a challenging area, harboring a variety of tissues of different linings. Therefore, a variety of diseases can arise or involve in this area (i.e., ...neoplastic or not). A total of three challenging cases of "chameleon" sellar lesions treated via EEA were described, and the lesions mimicked radiological features of common sellar masses such as craniopharyngiomas and/or pituitary adenomas, and we also report a literature review of similar cases.
A retrospective analysis of three primary cases was conducted at the Università degli Studi di Napoli Federico II, Naples, Italy. Clinical information, radiological examinations, and pathology reports were illustrated.
A total of three cases of so-called "chameleon" sellar lesions comprising two men and one woman were reported. Based on the intraoperative finding and pathological examination, we noticed that case 1 had suprasellar glioblastoma, case 2 had a primary neuroendocrine tumor, and case 3 had cavernous malformation.
Neurosurgeons should consider "unexpected" lesions of the sellar/suprasellar region in the preoperative differential diagnosis. A multidisciplinary approach with the collaboration of neurosurgeons, neuroradiologists, and pathologists plays a fundamental role. The recognition of unusual sellar lesions can help surgeons with better preoperative planning; so an endoscopic endonasal approach may represent a valid surgical technique to obtain decompression of the optic apparatus and vascular structures and finally a pathological diagnosis.
Background
White matter hyperintensities (WMHs) of the brain are observed in normal aging, in various subtypes of dementia and in chronic pain, playing a crucial role in pain processing. The aim of ...the study has been to assess the WMHs in Burning Mouth Syndrome (BMS) patients by means of the Age-Related White Matter Changes scale (ARWMCs) and to analyze their predictors.
Methods
One hundred BMS patients were prospectively recruited and underwent magnetic resonance imaging (MRI) of the brain. Their ARWMCs scores were compared with those of an equal number of healthy subjects matched for age and sex. Intensity and quality of pain, psychological profile, and blood biomarkers of BMS patients were further investigated to find potential predictors of WMHs. Specifically, the Numeric Rating Scale (NRS), Short-Form McGill Pain Questionnaire (SF-MPQ), Hamilton rating scale for Depression and Anxiety (HAM-D and HAM-A), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS) were administered.
Results
The BMS patients presented statistically significant higher scores on the ARWMCs compared to the controls, especially in the right frontal, left frontal, right parietal-occipital, left parietal-occipital, right temporal and left temporal lobes (
p
-values: <0.001, <0.001, 0.005, 0.002, 0.009, 0.002, and <0.001, respectively). Age, a lower educational level, unemployment, essential hypertension, and hypercholesterolemia were correlated to a higher total score on the ARWMCs (
p
-values: <0.001, 0.016, 0.014, 0.001, and 0.039, respectively). No correlation was found with the blood biomarkers, NRS, SF-MPQ, HAM-A, HAM-D, PSQI, and ESS.
Conclusion
Patients with BMS showed a higher frequency of WMHs of the brain as suggested by the higher ARWCs scores compared with the normal aging of the healthy subjects. These findings could have a role in the pathophysiology of the disease and potentially affect and enhance pain perception.
Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and ...caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes.
Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a ...framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status.
Three hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features across glioma grades and mutation status were tested using the Wilcoxon two-sample test. A random-forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features.
Shape, distribution and texture features showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases (87% of the gliomas grades predicted with distance less than 1).
Despite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background:
Due to its common association with chronic pain experience, cognitive impairment (CI) has never been evaluated in patients with burning mouth syndrome (BMS). The purpose of this study is ...to assess the prevalence of CI in patients with BMS and to evaluate its relationship with potential predictors such as pain, mood disorders, blood biomarkers, and white matter changes (WMCs).
Methods:
A case-control study was conducted by enrolling 40 patients with BMS and an equal number of healthy controls matched for age, gender, and education. Neurocognitive assessment Mini Mental State Examination (MMSE), Digit Cancellation Test (DCT), the Forward and Backward Digit Span task (FDS and BDS), Corsi Block-Tapping Test (CB-TT), Rey Auditory Verbal Learning Test (RAVLT), Copying Geometric Drawings (CGD), Frontal Assessment Battery (FAB), and Trail Making A and B (TMT-A and TMT-B), psychological assessment Hamilton Rating Scale for Depression and Anxiety (HAM-D and HAM-A), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and 36-Item Short Form Health Survey (SF-36), and pain assessment Visual Analogic Scale (VAS), Total Pain Rating index (T-PRI), Brief Pain Inventory (BPI), and Pain DETECT Questionnaire (PD-Q) were performed. In addition, blood biomarkers and MRI of the brain were recorded for the detection of Age-Related WMCs (ARWMCs). Descriptive statistics, the Mann-Whitney
U
-test, the Pearson Chi-Squared test and Spearman's correlation analysis were used.
Results:
Patients with BMS had impairments in most cognitive domains compared with controls (
p
< 0.001
**
) except in RAVLT and CGD. The HAM-D, HAM-A, PSQI, ESS, SF-36, VAS, T-PRI, BPI and PD-Q scores were statistically different between BMS patients and controls (
p
< 0.001
**
) the WMCs frequency and ARWMC scores in the right temporal (RT) and left temporal (LT) lobe were higher in patients with BMS (
p
= 0.023
*
).
Conclusions:
Meanwhile, BMS is associated with a higher decline in cognitive functions, particularly attention, working memory, and executive functions, but other functions such as praxis-constructive skills and verbal memory are preserved. The early identification of CI and associated factors may help clinicians to identify patients at risk of developing time-based neurodegenerative disorders, such as Alzheimer's disease (AD) and vascular dementia (VD), for planning the early, comprehensive, and multidisciplinary assessment and treatment.
Objectives
To systematically review current research applications of radiomics in patients with cholangiocarcinoma and to assess the quality of CT and MRI radiomics studies.
Methods
A systematic ...search was conducted on PubMed/Medline, Web of Science, and Scopus databases to identify original studies assessing radiomics of cholangiocarcinoma on CT and/or MRI. Three readers with different experience levels independently assessed quality of the studies using the radiomics quality score (RQS). Subgroup analyses were performed according to journal type, year of publication, quartile and impact factor (from the Journal Citation Report database), type of cholangiocarcinoma, imaging modality, and number of patients.
Results
A total of 38 original studies including 6242 patients (median 134 patients) were selected. The median RQS was 9 (corresponding to 25.0% of the total RQS; IQR 1–13) for reader 1, 8 (22.2%, IQR 3–12) for reader 2, and 10 (27.8%; IQR 5–14) for reader 3. The inter-reader agreement was good with an ICC of 0.75 (95% CI 0.62–0.85) for the total RQS. All studies were retrospective and none of them had phantom assessment, imaging at multiple time points, nor performed cost-effectiveness analysis. The RQS was significantly higher in studies published in journals with impact factor > 4 (median 11 vs. 4,
p
= 0.048 for reader 1) and including more than 100 patients (median 11.5 vs. 0.5,
p
< 0.001 for reader 1).
Conclusions
Quality of radiomics studies on cholangiocarcinoma is insufficient based on the radiomics quality score. Future research should consider prospective studies with a standardized methodology, validation in multi-institutional external cohorts, and open science data.
Key points
The quality of current radiomics studies on cholangiocarcinoma is insufficient, with a median radiomics quality score of 8–10, corresponding to 22–28% of the ideal quality score.
None of the current studies conducted phantom assessment, imaging at multiple time points, prospective registration in a trial database, nor cost-effectiveness analysis.
The inter-reader agreement of the radiomics quality score is good (ICC of 0.75; 95% CI 0.62–0.85) among readers with different levels of experience.
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