How to evaluate perfusion imaging in post-treatment glioma: a comparison of three different analysis methods.

INTRODUCTION: Dynamic susceptibility contrast (DSC) perfusion weighted (PW)-MRI can aid in differentiating treatment related abnormalities (TRA) from tumor progression (TP) in post-treatment glioma patients. Common methods, like the 'hot spot', or visual approach suffer from oversimplification and subjectivity. Using perfusion of the complete lesion potentially offers an objective and accurate alternative. This study aims to compare the diagnostic value and assess the subjectivity of these techniques. METHODS: 50 Glioma patients with enhancing lesions post-surgery and chemo-radiotherapy were retrospectively included. Outcome was determined by clinical/radiological follow-up or biopsy. Imaging analysis used the 'hot spot', volume of interest (VOI) and visual approach. Diagnostic accuracy was compared using receiving operator characteristics (ROC) curves for the VOI and 'hot spot' approach, visual assessment was analysed with contingency tables. Inter-operator agreement was determined with Cohens kappa and intra-class coefficient (ICC). RESULTS: 29 Patients suffered from TP, 21 had TRA. The visual assessment showed poor to substantial inter-operator agreement (κ = -0.72 - 0.68). Reliability of the 'hot spot' placement was excellent (ICC = 0.89), while reference placement was variable (ICC = 0.54). The area under the ROC (AUROC) of the mean- and maximum relative cerebral blood volume (rCBV) (VOI-analysis) were 0.82 and 0.72, while the rCBV-ratio ('hot spot' analysis) was 0.69. The VOI-analysis had a more balanced sensitivity and specificity compared to visual assessment. CONCLUSIONS: VOI analysis of DSC PW-MRI data holds greater diagnostic accuracy in single-moment differentiation of TP and TRA than 'hot spot' or visual analysis. This study underlines the subjectivity of visual placement and assessment.

Prognosis and histology of sporadic synchronous and metachronous meningiomas and comparative analyses with singular lesions.

Synchronous or metachronous growth of multiple tumors (≥ 2) is found in up to 20% of meningioma patients. However, biological as well as histological features and prognosis are largely unexplored. Clinical and histological characteristics were retrospectively investigated in 95 patients harboring 226 multiple meningiomas (MMs) and compared with 135 cases of singular meningiomas (SM) using uni- and multivariate analyses. In MM, tumors occurred synchronously and metachronously in 62% and 38%, respectively. WHO grade was intra-individually constant in all but two MMs, and histological subtype varied in 13% of grade 1 tumors. MM occurred more commonly in convexity/parasagittal locations, while SM were more frequent at the skull base (p < .001). In univariate analyses, gross total resection (p = .014) and high-grade histology in MM were associated with a prolonged time to progression (p < .001). Most clinical characteristics and rates of high-grade histology were similar in both groups (p ≥ .05, each). Multivariate analyses showed synchronous/metachronous meningioma growth (HR 4.50, 95% CI 2.26-8.96; p < .001) as an independent predictor for progression. Compared to SM, risk of progression was similar in cases with two (HR 1.56, 95% CI .76-3.19; p = .224), but exponentially raised in patients with 3-4 (HR 3.25, 1.22-1.62; p = .018) and ≥ 5 tumors (HR 13.80, 4.06-46.96; p < .001). Clinical and histological characteristics and risk factors for progression do not relevantly differ between SM and MM. Although largely constant, histology and WHO grade occasionally intra-individually vary in MM. A distinctly higher risk of disease progression in MM as compared to SM might reflect different underlying molecular alterations.

Iatrogenic lumbosacral infiltration with petroleum (hydrodesulfurized heavy) with secondary intrathecal distribution-a case report.

Petroleum is commonly used as a solvent, and primary intrathecal administration or secondary diffusion and subsequent clinical management has not been reported. We report the case of a male patient with intrathecal petroleum diffusion following accidental lumbar infiltration. After the onset of secondary myeloencephalopathy with coma and tetraparesis, continuous cranio-lumbar irrigation using an external ventricular and a lumbar drain was established. Cranial imaging revealed distinct supra- and infratentorial alterations. The patient improved slowly and was referred to rehabilitation. Intrathecal petroleum leads to myeloencephalopathy and continuous cranio-lumbar irrigation might be a safe treatment option.

Contrast-Enhanced T1-Weighted Head and Neck MRI: Prospective Intraindividual Image Quality Comparison of Spiral GRE, Cartesian GRE, and Cartesian TSE Sequences.

BACKGROUND. Sequences with noncartesian k-space sampling may improve image quality of head and neck MRI. OBJECTIVE. The purpose of this study was to compare intraindividually the image quality of a spiral gradient-recalled echo (GRE) sequence and conventional cartesian GRE and cartesian turbo spin-echo (TSE) sequences for contrast-enhanced T1-weighted head and neck MRI. METHODS. This prospective study included patients referred for contrast-enhanced head and neck MRI from August 2020 to May 2021. Patients underwent 1.5-T MRI including contrast-enhanced spiral GRE (2 minutes 28 seconds), cartesian GRE (4 minutes 27 seconds), and cartesian TSE (3 minutes 41 seconds) sequences, acquired in rotating order across patients. Three radiologists independently assessed image quality measures, including conspicuity of prespecified lesions, using 5-point Likert scales. One reader measured maximal extent of dental material artifact and contrast-to-noise ratio (CNR). RESULTS. Thirty-one patients (13 men, 18 women; mean age, 63.8 years) were enrolled. Nineteen patients had a focal lesion; 22 had dental material. Interreader agreement for image quality measures was substantial to excellent (Krippendorff alpha, 0.681-1.000). Scores for overall image quality (whole head and neck, neck only, and head only), pulsation artifact, muscular contour delineation, vessel contour delineation, motion artifact, and differentiation between mucosa and pharyngeal muscles were significantly better for spiral GRE than for cartesian GRE and cartesian TSE for all readers (p < .05). Scores for lesion conspicuity (whole head and neck, neck only, and head only), quality of fat suppression, flow artifact, and foldover artifact were not significantly different between spiral GRE and the cartesian sequences for any reader (p > .05). Dental material artifact scores were significantly worse for spiral GRE than the other sequences for all readers (p < .05). The mean maximum extent of dental material artifact was 39.6 ± 25.5 (SD) mm for spiral GRE, 35.6 ± 24.3 mm for cartesian GRE, and 29.6 ± 21.4 mm for cartesian TSE; the mean CNR was 221.1 ± 94.5 for spiral GRE, 151.8 ± 85.7 for cartesian GRE, and 153.0 ± 63.2 for cartesian TSE (p < .001 between spiral GRE and other sequences for both measures). CONCLUSION. Three-dimensional spiral GRE improves subjective image quality and CNR of head and neck MRI with shorter scan time versus cartesian sequences, though it exhibits larger dental material artifact. CLINICAL IMPACT. A spiral sequence may help overcome certain challenges of conventional cartesian sequences for head and neck MRI.

Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging.

This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process. Examples include machine learning applications in stroke, aneurysms, multiple sclerosis, neuro-oncology, neurodegeneration, and epilepsy.

[Chronic brachioradial pruritus in cervical spine meningioma]. / Chronischer brachioradialer Pruritus bei spinalem Meningeom.

We report a case of a 57-year-old slightly obese woman with localized itch on the arms accompanied by stinging and burning sensations. A few excoriations were observed upon clinical examination. The MRI examination of the cervical spine revealed a meningioma at C5/C6 level. The diagnosis of brachioradial pruritus due to compression of the cervical myelon was further supported by a positive ice-pack sign. Disc herniation or prolapse, foraminal stenosis and degenerative alterations constitute other possible causes of brachioradial pruritus.

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis.

OBJECTIVES: Different machine learning algorithms (MLAs) for automated segmentation of gliomas have been reported in the literature. Automated segmentation of different tumor characteristics can be of added value for the diagnostic work-up and treatment planning. The purpose of this study was to provide an overview and meta-analysis of different MLA methods. METHODS: A systematic literature review and meta-analysis was performed on the eligible studies describing the segmentation of gliomas. Meta-analysis of the performance was conducted on the reported dice similarity coefficient (DSC) score of both the aggregated results as two subgroups (i.e., high-grade and low-grade gliomas). This study was registered in PROSPERO prior to initiation (CRD42020191033). RESULTS: After the literature search (n = 734), 42 studies were included in the systematic literature review. Ten studies were eligible for inclusion in the meta-analysis. Overall, the MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82-0.86). In addition, a DSC score of 0.83 (95% CI: 0.80-0.87) and 0.82 (95% CI: 0.78-0.87) was observed for the automated glioma segmentation of the high-grade and low-grade gliomas, respectively. However, heterogeneity was considerably high between included studies, and publication bias was observed. CONCLUSION: MLAs facilitating automated segmentation of gliomas show good accuracy, which is promising for future implementation in neuroradiology. However, before actual implementation, a few hurdles are yet to be overcome. It is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set. KEY POINTS: • MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82-0.86), indicating a good performance. • MLA performance was comparable when comparing the segmentation results of the high-grade gliomas and the low-grade gliomas. • For future studies using MLAs, it is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.

Artificial Intelligence and Texture Analysis in Cardiac Imaging.

PURPOSE OF REVIEW: The aim of this structured review is to summarize the current research applications and opportunities arising from artificial intelligence (AI) and texture analysis with regard to cardiac imaging. RECENT FINDINGS: Current research findings suggest tremendous potential for AI in cardiac imaging, especially with regard to objective image analyses, overcoming the limitations of an observer-dependent subjective image interpretation. Researchers have used this technique across multiple imaging modalities, for instance to detect myocardial scars in cardiac MR imaging, to predict contrast enhancement in non-contrast studies, and to improve image acquisition and reconstruction. AI in medical imaging has the potential to provide novel, much-needed applications for improving patient care pertaining to the cardiovascular system. While several shortcomings are still present in the current methodology, AI may serve as a resourceful assistant to radiologists and clinicians alike.

Correlation of texture analysis of paraspinal musculature on MRI with different clinical endpoints: Lumbar Stenosis Outcome Study (LSOS).

OBJECTIVES: The aim of this study was to apply texture analysis (TA) on paraspinal musculature in T2-weighted (T2w) magnetic resonance images (MRI) of symptomatic lumbar spinal stenosis (LSS) patients and correlate the findings with clinical outcome measures. METHODS: Ninety patients were prospectively enrolled in the multi-centric Lumbar Stenosis Outcome Study (LSOS). All patients received a T2w MRI, from which we selected axial images perpendicular to the intervertebral disc at level L3/4 for TA. Regions-of-interest (ROI) were drawn of the paraspinal musculature and 304 TA features/ ROI were calculated. As clinical outcome measurements, we analysed three commonly applied measures: Spinal Stenosis Measure (SSM), Roland-Morris Disability Questionnaire (RMDQ), as well as the Numeric Rating Scale (NRS). We used two machine learning-based classifiers: Decision table, and k-nearest neighbours (k-NN). RESULTS: We observed no meaningful correlation between TA in paraspinal musculature and the two clinical outcome measures SSM symptoms and SSM function, while a moderate correlation was observed regarding the outcome measures RMDQ (k-NN: r = 0.56) and NRS (Decision Table: r = 0.72). CONCLUSIONS: In conclusion, MR TA is a viable tool to quantify medical images and illustrate correlations of microarchitectural changes invisible to a human reader with potential clinical impact. KEY POINTS: • TA is feasible on paraspinal musculature using MRI. • TA on paraspinal musculature correlates with SSM and RMDQ. • TA may enable a statement regarding clinical impact of imaging findings.

Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning.

PURPOSE: To evaluate the diagnostic performance of bone texture analysis (TA) combined with machine learning (ML) algorithms in standard CT scans to identify patients with vertebrae at risk for insufficiency fractures. MATERIALS AND METHODS: Standard CT scans of 58 patients with insufficiency fractures of the spine, performed between 2006 and 2013, were analyzed retrospectively. Every included patient had at least two CT scans. Intact vertebrae in a first scan that either fractured ("unstable") or remained intact ("stable") in the consecutive scan were manually segmented on mid-sagittal reformations. TA features for all vertebrae were extracted using open-source software (MaZda). In a paired control study, all vertebrae of the study cohort "cases" and matched controls were classified using ROC analysis of Hounsfield unit (HU) measurements and supervised ML techniques. In a within-subject vertebra comparison, vertebrae of the cases were classified into "unstable" and "stable" using identical techniques. RESULTS: One hundred twenty vertebrae were included. Classification of cases/controls using ROC analysis of HU measurements showed an AUC of 0.83 (95% confidence interval [CI], 0.77-0.88), and ML-based classification showed an AUC of 0.97 (CI, 0.97-0.98). Classification of unstable/stable vertebrae using ROC analysis showed an AUC of 0.52 (CI, 0.42-0.63), and ML-based classification showed an AUC of 0.64 (CI, 0.61-0.67). CONCLUSION: TA combined with ML allows to identifying patients who will suffer from vertebral insufficiency fractures in standard CT scans with high accuracy. However, identification of single vertebra at risk remains challenging. KEY POINTS: • Bone texture analysis combined with machine learning allows to identify patients at risk for vertebral body insufficiency fractures on standard CT scans with high accuracy. • Compared to mere Hounsfield unit measurements on CT scans, application of bone texture analysis combined with machine learning improve fracture risk prediction. • This analysis has the potential to identify vertebrae at risk for insufficiency fracture and may thus increase diagnostic value of standard CT scans.

Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images.

Purpose To test whether texture analysis (TA) allows for the diagnosis of subacute and chronic myocardial infarction (MI) on noncontrast material-enhanced cine cardiac magnetic resonance (MR) images. Materials and Methods In this retrospective, institutional review board-approved study, 120 patients who underwent cardiac MR imaging and showed large transmural (volume of enhancement on late gadolinium enhancement [LGE] images >20%, n = 72) or small (enhanced volume ≤20%, n = 48) subacute or chronic ischemic scars were included. Sixty patients with normal cardiac MR imaging findings served as control subjects. Regions of interest for TA encompassing the left ventricle were drawn by two blinded, independent readers on cine images in end systole by using a freely available software package. Stepwise dimension reduction and texture feature selection based on reproducibility, machine learning, and correlation analyses were performed for selecting features, enabling the diagnosis of MI on nonenhanced cine MR images by using LGE imaging as the standard of reference. Results Five independent texture features allowed for differentiation between ischemic scar and normal myocardium on cine MR images in both subgroups: Teta1, Perc.01, Variance, WavEnHH.s-3, and S(5,5)SumEntrp (in patients with large MI: all P values < .001; in patients with small MI: Teta1 and Perc.01, P < .001; Variance, P = .026; WavEnHH.s-3, P = .007; S[5,5]SumEntrp, P = .045). Multiple logistic regression models revealed that combining the features Teta1 and Perc.01 resulted in the highest accuracy for diagnosing large and small MI on cine MR images, with an area under the curve of 0.93 and 0.92, respectively. Conclusion This proof-of-concept study indicates that TA of nonenhanced cine MR images allows for the diagnosis of subacute and chronic MI with high accuracy. © RSNA, 2017 Online supplemental material is available for this article.

Three-Dimensional Texture Analysis with Machine Learning Provides Incremental Predictive Information for Successful Shock Wave Lithotripsy in Patients with Kidney Stones.

PURPOSE: We sought to determine the predictive value of 3-dimensional texture analysis of computerized tomography images for successful shock wave lithotripsy in patients with kidney stones. MATERIALS AND METHODS: Patients with preoperative and postoperative computerized tomography, previously untreated kidney stones and a stone diameter of 5 to 20 mm were included in study. A total of 224, 3-dimensional texture analysis features of each kidney stone, including attenuation measured in HU and the clinical variables body mass index, initial stone size and skin to stone distance, were analyzed using 5 commonly used machine learning models. The data set was split in a ratio of 2/3 for model derivation and 1/3 for validation. Machine learning based predictions of shock wave lithotripsy success in the validation cohort were evaluated by calculating sensitivity, specificity and the AUC. RESULTS: For shock wave lithotripsy success the 3 clinical variables body mass index, initial stone size and skin to stone distance showed an AUC of 0.68, 0.58 and 0.63, respectively. No predictive value was found for HU. A random forest classifier using 3, 3-dimensional texture analysis features had an AUC of 0.79. By combining these 3 features with clinical variables discriminatory accuracy improved further with an AUC of 0.85 for 3-dimensional texture analysis features and skin to stone distance, an AUC of 0.8 for 3-dimensional texture analysis features and body mass index, and an AUC of 0.81 for 3-dimensional texture analysis and stone size. CONCLUSIONS: This preliminary study indicates that the clinical variables body mass index, initial stone size and skin to stone distance show limited value to predict shock wave lithotripsy success while stone HU values were not predictive. Select 3-dimensional texture analysis features identified by machine learning provided incremental accuracy to predict the success of shock wave lithotripsy.

3D image fusion of whole-heart dynamic cardiac MR perfusion and late gadolinium enhancement: Intuitive delineation of myocardial hypoperfusion and scar.

BACKGROUND: Since patients with myocardial hypoperfusion due to coronary artery disease (CAD) with preserved viability are known to benefit from revascularization, accurate differentiation of hypoperfusion from scar is desirable. PURPOSE: To develop a framework for 3D fusion of whole-heart dynamic cardiac MR perfusion and late gadolinium enhancement (LGE) to delineate stress-induced myocardial hypoperfusion and scar. STUDY TYPE: Prospective feasibility study. SUBJECTS: Sixteen patients (61 ± 14 years, two females) with known/suspected CAD. FIELD STRENGTH/SEQUENCE: 1.5T (nine patients); 3.0T (seven patients); whole-heart dynamic 3D cardiac MR perfusion (3D-PERF, under adenosine stress); 3D LGE inversion recovery sequences (3D-SCAR). ASSESSMENT: A software framework was developed for 3D fusion of 3D-PERF and 3D-SCAR. Computation steps included: 1) segmentation of the left ventricle in 3D-PERF and 3D-SCAR; 2) semiautomatic thresholding of perfusion/scar data; 3) automatic calculation of ischemic/scar burden (ie, pathologic relative to total myocardium); 4) projection of perfusion/scar values onto artificial template of the left ventricle; 5) semiautomatic coregistration to an exemplary heart contour easing 3D orientation; and 6) 3D rendering of the combined datasets using automatically defined color tables. All tasks were performed by two independent, blinded readers (J.S. and R.M.). STATISTICAL TESTS: Intraclass correlation coefficients (ICC) for determining interreader agreement. RESULTS: Image acquisition, postprocessing, and 3D fusion were feasible in all cases. In all, 10/16 patients showed stress-induced hypoperfusion in 3D-PERF; 8/16 patients showed LGE in 3D-SCAR. For 3D-PERF, semiautomatic thresholding was possible in all patients. For 3D-SCAR, automatic thresholding was feasible where applicable. Average ischemic burden was 11 ± 7% (J.S.) and 12 ± 7% (R.M.). Average scar burden was 8 ± 5% (J.S.) and 7 ± 4% (R.M.). Interreader agreement was excellent (ICC for 3D-PERF = 0.993, for 3D-SCAR = 0.99). DATA CONCLUSION: 3D fusion of 3D-PERF and 3D-SCAR facilitates intuitive delineation of stress-induced myocardial hypoperfusion and scar. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1129-1138.

Texture analysis of paraspinal musculature in MRI of the lumbar spine: analysis of the lumbar stenosis outcome study (LSOS) data.

OBJECTIVE: To evaluate association of fatty infiltration in paraspinal musculature with clinical outcomes in patients suffering from lumbar spinal stenosis (LSS) using qualitative and quantitative grading in magnetic resonance imaging (MRI). MATERIALS AND METHODS: In this retrospective study, texture analysis (TA) was performed on postprocessed axial T2 weighted (w) MR images at level L3/4 using dedicated software (MaZda) in 62 patients with LSS. Associations in fatty infiltration between qualitative Goutallier and quantitative TA findings with two clinical outcome measures, Spinal stenosis measure (SSM) score and walking distance, at baseline and regarding change over time were assessed using machine learning algorithms and multiple logistic regression models. RESULTS: Quantitative assessment of fatty infiltration using the histogram TA feature "mean" showed higher interreader reliability (ICC 0.83-0.97) compared to the Goutallier staging (κ = 0.69-0.93). No correlation between Goutallier staging and clinical outcome measures was observed. Among 151 TA features, only TA feature "mean" of the spinotransverse group showed a significant but weak correlation with worsened SSM (p = 0.046). TA feature "S(3,3) entropy" showed a significant but weak association with worsened WD over 12 months (p = 0.046). CONCLUSION: MR TA is a reproducible tool to quantitatively assess paraspinal fatty infiltration, but there is no clear association with the clinical outcome in asymptomatic LSS patients.

Biomarkers predict outcome in Charcot-Marie-Tooth disease 1A.

BACKGROUND: Charcot-Marie-Tooth disease type 1A (CMT1A) is the most common inherited neuropathy, a debilitating disease without known cure. Among patients with CMT1A, disease manifestation, progression and severity are strikingly variable, which poses major challenges for the development of new therapies. Hence, there is a strong need for sensitive outcome measures such as disease and progression biomarkers, which would add powerful tools to monitor therapeutic effects in CMT1A. METHODS: We established a pan-European and American consortium comprising nine clinical centres including 311 patients with CMT1A in total. From all patients, the CMT neuropathy score and secondary outcome measures were obtained and a skin biopsy collected. In order to assess and validate disease severity and progression biomarkers, we performed qPCR on a set of 16 animal model-derived potential biomarkers in skin biopsy mRNA extracts. RESULTS: In 266 patients with CMT1A, a cluster of eight cutaneous transcripts differentiates disease severity with a sensitivity and specificity of 90% and 76.1%, respectively. In an additional cohort of 45 patients with CMT1A, from whom a second skin biopsy was taken after 2-3 years, the cutaneous mRNA expression of GSTT2, CTSA, PPARG, CDA, ENPP1 and NRG1-Iis changing over time and correlates with disease progression. CONCLUSIONS: In summary, we provide evidence that cutaneous transcripts in patients with CMT1A serve as disease severity and progression biomarkers and, if implemented into clinical trials, they could markedly accelerate the development of a therapy for CMT1A.

Normative values for CT-based texture analysis of vertebral bodies in dual X-ray absorptiometry-confirmed, normally mineralized subjects.

OBJECTIVES: To develop age-, gender-, and regional-specific normative values for texture analysis (TA) of spinal computed tomography (CT) in subjects with normal bone mineral density (BMD), as defined by dual X-ray absorptiometry (DXA), and to determine age-, gender-, and regional-specific differences. MATERIALS AND METHODS: In this retrospective, IRB-approved study, TA was performed on sagittal CT bone images of the thoracic and lumbar spine using dedicated software (MaZda) in 141 individuals with normal DXA BMD findings. Numbers of female and male subjects were balanced in each of six age decades. Three hundred and five TA features were analyzed in thoracic and lumbar vertebrae using free-hand regions-of-interest. Intraclass correlation (ICC) coefficients were calculated for determining intra- and inter-observer agreement of each feature. Further dimension reduction was performed with correlation analyses. RESULTS: The TA features with an ICC < 0.81 indicating compromised intra- and inter-observer agreement and with Pearson correlation scores r > 0.8 with other features were excluded from further analysis for dimension reduction. From the remaining 31 texture features, a significant correlation with age was found for the features mean (r = -0.489, p < 0.001), variance (r = -0.681, p < 0.001), kurtosis (r = 0.273, p < 0.001), and WavEnLL_s4 (r = 0.273, p < 0.001). Significant differences were found between genders for various higher-level texture features (p < 0.001). Regional differences among the thoracic spine, thoracic-lumbar junction, and lumbar spine were found for most TA features (p < 0.021). CONCLUSION: This study established normative values of TA features on CT images of the spine and showed age-, gender-, and regional-specific differences in individuals with normal BMD as defined by DXA.

Prediction of Seropositivity in Suspected Autoimmune Encephalitis by Use of Radiomics: A Radiological Proof-of-Concept Study.

In this study, we sought to evaluate the capabilities of radiomics and machine learning in predicting seropositivity in patients with suspected autoimmune encephalitis (AE) from MR images obtained at symptom onset. In 83 patients diagnosed with AE between 2011 and 2022, manual bilateral segmentation of the amygdala was performed on pre-contrast T2 images using 3D Slicer open-source software. Our sample of 83 patients contained 43 seropositive and 40 seronegative AE cases. Images were obtained at our tertiary care center and at various secondary care centers in North Rhine-Westphalia, Germany. The sample was randomly split into training data and independent test data. A total of 107 radiomic features were extracted from bilateral regions of interest (ROIs). Automated machine learning (AutoML) was used to identify the most promising machine learning algorithms. Feature selection was performed using recursive feature elimination (RFE) and based on the determination of the most important features. Selected features were used to train various machine learning algorithms on 100 different data partitions. Performance was subsequently evaluated on independent test data. Our radiomics approach was able to predict the presence of autoantibodies in the independent test samples with a mean AUC of 0.90, a mean accuracy of 0.83, a mean sensitivity of 0.84 and a mean specificity of 0.82, with Lasso regression models yielding the most promising results. These results indicate that radiomics-based machine learning could be a promising tool in predicting the presence of autoantibodies in suspected AE patients. Given the implications of seropositivity for definitive diagnosis of suspected AE cases, this may expedite diagnostic workup even before results from specialized laboratory testing can be obtained. Furthermore, in conjunction with recent publications, our results indicate that characterization of AE subtypes by use of radiomics may become possible in the future, potentially allowing physicians to tailor treatment in the spirit of personalized medicine even before laboratory workup is completed.

2-Hydroxyglutarate as an MR spectroscopic predictor of an IDH mutation in gliomas. / 2-Hydroxyglutarat als MR-spektroskopisch erfassbarer Prädiktor einer IDH-Mutation bei Gliomen.

The mutated enzyme isocitrate dehydrogenase (IDH) 1 and 2 has been detected in various tumor entities such as gliomas and can convert α-ketoglutarate into the oncometabolite 2-hydroxyglutarate (2-HG). This neuro-oncologically significant metabolic product can be detected by MR spectroscopy and is therefore suitable for noninvasive glioma classification and therapy monitoring.This paper provides an up-to-date overview of the methodology and relevance of 1H-MR spectroscopy (MRS) in the oncological primary and follow-up diagnosis of gliomas. The possibilities and limitations of this MR spectroscopic examination are evaluated on the basis of the available literature.By detecting 2-HG, MRS can in principle offer a noninvasive alternative to immunohistological analysis thus avoiding surgical intervention in some cases. However, in addition to an adapted and optimized examination protocol, the individual measurement conditions in the examination region are of decisive importance. Due to the inherently small signal of 2-HG, unfavorable measurement conditions can influence the reliability of detection. · MR spectroscopy enables the non-invasive detection of 2-hydroxyglutarate.. · The measurement of this metabolite allows the detection of an IDH mutation in gliomas.. · The choice of MR examination method is particularly important.. · Detection reliability is influenced by glioma size, necrotic tissue and the existing measurement conditions.. · Bauer J, Raum HN, Kugel H et al. 2-Hydroxyglutarate as an MR spectroscopic predictor of an IDH mutation in gliomas. Fortschr Röntgenstr 2024; DOI 10.1055/a-2285-4923.

Case-Based Collaborative Learning in Undergraduate Radiology Teaching-Are Essential Conditions for Group Discussions Met?

BACKGROUND: Delivering case-based collaborative learning (cCBL) at scale using technology that both presents the clinical problem authentically and seeks to foster quality group discussion is a challenge, especially argumentation which is critical for effective learning. The aim of this study was to investigate the presence of essential conditions to capitalize on a technology-enhanced cCBL scenario for teaching radiology and facilitating quality group discussion. METHODS: A questionnaire was administered to 114 fourth-year medical students who completed a technology-enhanced cCBL scenario for teaching neuroradiology. It consisted of individual online pre-class work and face-to-face in-class work, where group discussion followed individual work at a workstation. Items from the "Heedful Interrelating in Collaborative Educational Settings" scale and "positive emotional engagement" questionnaire assessed the quality of social-cognitive processes and emotional engagement during the group discussions. Structured interviews were used to explore the teachers' awareness of and engagement with the technology. RESULTS: The mean scores of most "heedfulness" items were below 3.5 (7-point scale), suggesting that participants did not enter the debriefing with a mindset conducive for argumentation. However, for the affective states "interest" and "enjoyment" the mean scores were above 5. Free text comments suggested participants enjoyed the superficial interactions, but did not necessarily engage in argumentation. Structured interviews revealed teachers were aware of the possibilities of the learning dashboard and used it as a common frame of reference, but did not really succeed to use it as a springboard for discussion. CONCLUSION: A technology-enhanced cCBL scenario is useful for teaching radiology in undergraduate medical education, but the added value of acquiring in-depth knowledge will only be achieved when students are aware of the importance of an "heedful" mind-set.

Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine Learning.

OBJECTIVES: Regarding the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the isocitrate dehydrogenase (IDH) mutation status is one of the most important factors for CNS tumor classification. The aim of our study is to analyze which of the commonly used magnetic resonance imaging (MRI) sequences is best suited to obtain this information non-invasively using radiomics-based machine learning models. We developed machine learning models based on different MRI sequences and determined which of the MRI sequences analyzed yields the highest discriminatory power in predicting the IDH mutation status. MATERIAL AND METHODS: In our retrospective IRB-approved study, we used the MRI images of 106 patients with histologically confirmed gliomas. The MRI images were acquired using the T1 sequence with and without administration of a contrast agent, the T2 sequence, and the Fluid-Attenuated Inversion Recovery (FLAIR) sequence. To objectively compare performance in predicting the IDH mutation status as a function of the MRI sequence used, we included only patients in our study cohort for whom MRI images of all four sequences were available. Seventy-one of the patients had an IDH mutation, and the remaining 35 patients did not have an IDH mutation (IDH wild-type). For each of the four MRI sequences used, 107 radiomic features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects associated with the data partitioning. Feature preselection and subsequent model development were performed using Random Forest, Lasso regression, LDA, and Naïve Bayes. The performance of all models was determined with independent test data. RESULTS: Among the different approaches we examined, the T1-weighted contrast-enhanced sequence was found to be the most suitable for predicting IDH mutations status using radiomics-based machine learning models. Using contrast-enhanced T1-weighted MRI images, our seven-feature model developed with Lasso regression achieved a mean area under the curve (AUC) of 0.846, a mean accuracy of 0.792, a mean sensitivity of 0.847, and a mean specificity of 0.681. The administration of contrast agents resulted in a significant increase in the achieved discriminatory power. CONCLUSIONS: Our analyses show that for the prediction of the IDH mutation status using radiomics-based machine learning models, among the MRI images acquired with the commonly used MRI sequences, the contrast-enhanced T1-weighted images are the most suitable.