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Over a third of minor stroke patients experience post-stroke cognitive impairment (PSCI), but no validated tools exist to identify at-risk patients early. This study investigated whether disconnection features derived from infarcts and white matter hyperintensities (WMH) could serve as markers for short- and long-term cognitive decline in first-ever minor ischemic stroke patients. First-ever minor ischemic stroke patients (NIHSS ≤ 7) were prospectively followed at 72-h, 6 months, and 36 months post-stroke with cognitive tests and brain MRI. Infarct and WMH volumes were semi-automatically assessed on DWI and FLAIR sequences. Bayesian tract-based disconnection models estimated remote pathological effects of infarcts and WMH. Associations between disconnection features and cognitive outcomes were analyzed using canonical correlation analyses, adjusted for age, education, and multiple comparisons. Among 105 patients (31% female, mean age 63 ± 12 years), infarct volume averaged 10.28 ± 17.10 cm 3 and predominantly involved the middle cerebral artery territory (83%). WMH burden was higher in frontal periventricular white matter. Infarct-based features did not significantly relate to PCSI. However, a WMH-derived disconnection factor, involving commissural and frontal tracts, and the right superior longitudinal fasciculus, was significantly associated with PSCI at 6 months (OR = 9.96, p value = 0.02) and 36 months (OR = 12.27, p value = 0.006), particularly in executive/attention, language, and visuospatial domains. This factor, unrelated to WMH volume, outperformed demographic and clinical predictors of PSCI. WMH-induced disconnection may be associated with short- and long-term PSCI in minor stroke. Routine MR-derived features could identify at-risk patients for rehabilitation trials., (© 2025 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Objectives: To explore amide proton transfer-weighted imaging (APTwi) for the initial classification of brain masses in clinical practice by systematically reporting APTwi signal intensity (APT-SI) in tumor mimics and brain tumors., Materials and Methods: Single-center retrospective analysis (2017-2020) of APTwi in 156 patients (84 men, mean age: 50.9 ± 20) who underwent characterization imaging of a brain mass prior to any treatment, using 3-Tesla MRI. 125/156 (80%) patients presented with brain tumor and 31/156 (20%) with tumor mimics. Regions of interest were manually drawn on 2D axial slices by two readers on APTwi map in lesional and perilesional areas and APT-SI, corresponding to the Magnetization Transfer Ratio asymmetry at 3.5 ppm, was systematically reported. Student's t-test or Wilcoxon-test were used to compare groups of patients., Results: The mean APT-SI in lesional and perilesional areas were significantly higher in tumors compared to tumor mimics: 3% [2.10-4] (median [Q1-Q3]) vs 1.7% [0.80-2.55] (p < 0.001) and 1.9% [1.2-2.80] vs. 1.0% [0.55-2.3] (p < 0.01). There were no differences in mean APT-SI in the tumor core between low and high-grade tumors: 2.5% [1.80-4.0] vs. 3.25% [2.5-4.0]. The mean APT-SI was significantly higher in high-grade glioma compared to low-grade glioma: 3.4% [2.7-4] vs. 2.1% [1.7-2.5] (p < 0.001). Highest mean APT-SI in tumor core were found in mesenchymal tumors (5.83% ± 1.45, mean ± SD), embryonal tumors (5.27% ± 3.5) and meningiomas (4.28% ± 0.70). In tumor mimics, highest mean APT-SI was found in the core of infectious lesions (3.52% ± 0.67)., Conclusion: High signal on ATPwi is not exclusive to high-grade brain tumors but can be observed in some tumor mimics and subtypes of low-grade tumors., Key Points: Question What is the value of amide proton transfer-weighted imaging (APTwi) in the setting of brain mass classification? Findings High APT-signal intensity in the tumor core of a brain mass could correspond to a high- or low-grade tumor or tumor mimic. Clinical relevance In patients presenting for the initial classification of brain masses, APTwi findings should be interpreted with caution and in conjunction with other MRI parameters, as a high APTwi signal does not necessarily indicate a high-grade tumor., Competing Interests: Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Julien Savatovsky. Conflict of interest: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Statistics and biometry: Jessica Guillaume kindly provided statistical advice for this manuscript. Informed consent: Written informed consent was waived by the Institutional Review Board. Ethical approval: Institutional Review Board approval was obtained. Study subjects or cohorts overlap: Not applicable. Methodology: Retrospective Observational Performed at one institution, (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Background: Orbital tumors present a diagnostic challenge due to their varied locations and histopathological differences. Although recent advancements in imaging have improved diagnosis, classification remains a challenge. The integration of artificial intelligence in radiology and ophthalmology has demonstrated promising outcomes., Purpose: This study aimed to evaluate the performance of machine learning models in accurately distinguishing malignant orbital tumors from benign ones using multiparametric 3 T magnetic resonance imaging (MRI) data., Materials and Methods: In this single-center prospective study, patients with orbital masses underwent presurgery 3 T MRI scans between December 2015 and May 2021. The MRI protocol comprised multiparametric imaging including dynamic contrast-enhanced (DCE), diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), as well as morphological imaging acquisitions. A repeated nested cross-validation strategy using random forest classifiers was used for model training and evaluation, considering 8 combinations of explanatory features. Shapley additive explanations (SHAP) values were used to assess feature contributions, and the model performance was evaluated using multiple metrics., Results: One hundred thirteen patients were analyzed (57/113 [50.4%] were women; average age was 51.5 ± 17.5 years, range: 19-88 years). Among the 8 combinations of explanatory features assessed, the performance on predicting malignancy when using the most comprehensive model, which is the most exhaustive one incorporating all 46 explanatory features-including morphology, DWI, DCE, and IVIM, achieved an area under the curve of 0.9 [0.73-0.99]. When using the streamlined "10-feature signature" model, performance reached an area under the curve of 0.88 [0.71-0.99]. Random forest feature importance graphs measured by the mean of SHAP values pinpointed the 10 most impactful features, which comprised 3 quantitative IVIM features, 4 quantitative DCE features, 1 quantitative DWI feature, 1 qualitative DWI feature, and age., Conclusions: Our findings demonstrate that a machine learning approach, integrating multiparametric MRI data such as DCE, DWI, IVIM, and morphological imaging, offers high-performing models for differentiating malignant from benign orbital tumors. The streamlined 10-feature signature, with a performance close to the comprehensive model, may be more suitable for clinical application., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background and Purpose: Orbital lesions are rare but serious. Their characterization remains challenging. Diagnosis is based on biopsy or surgery, which implies functional risks. It is necessary to develop noninvasive diagnostic tools. The goal of this study was to evaluate the diagnostic performance of dynamic contrast-enhanced MR imaging at 3T when distinguishing malignant from benign orbital tumors on a large prospective cohort., Materials and Methods: This institutional review board-approved prospective single-center study enrolled participants presenting with an orbital lesion undergoing a 3T MR imaging before surgery from December 2015 to May 2021. Morphologic, diffusion-weighted, and dynamic contrast-enhanced MR images were assessed by 2 readers blinded to all data. Univariable and multivariable analyses were performed. To assess diagnostic performance, we used the following metrics: area under the curve, sensitivity, and specificity. Histologic analysis, obtained through biopsy or surgery, served as the criterion standard for determining the benign or malignant status of the tumor., Results: One hundred thirty-one subjects (66/131 [50%] women and 65/131 [50%] men; mean age, 52 [SD, 17.1] years; range, 19-88 years) were enrolled. Ninety of 131 (69%) had a benign lesion, and 41/131 (31%) had a malignant lesion. Univariable analysis showed a higher median of transfer constant from blood plasma to the interstitial environment ( K trans ) and of transfer constant from the interstitial environment to the blood plasma (minute -1 ) (Kep) and a higher interquartile range of K trans in malignant-versus-benign lesions (1.1 minute -1 versus 0.65 minute -1 , P  = .03; 2.1 minute -1 versus 1.1 minute -1 , P  = .01; 0.81 minute -1 versus 0.65 minute -1 , P  = .009, respectively). The best-performing multivariable model in distinguishing malignant-versus-benign lesions included parameters from dynamic contrast-enhanced imaging, ADC, and morphology and reached an area under the curve of 0.81 (95% CI, 0.67-0.96), a sensitivity of 0.82 (95% CI, 0.55-1), and a specificity of 0.81 (95% CI, 0.65-0.96)., Conclusions: Dynamic contrast-enhanced MR imaging at 3T appears valuable when characterizing orbital lesions and provides complementary information to morphologic imaging and DWI., (© 2024 by American Journal of Neuroradiology.)

Purpose: The purpose of this study was to compare the performance of three magnetic resonance imaging (MRI) reading methods in the follow-up of patients with multiple sclerosis (MS)., Materials and Methods: This retrospective study included patients with MS who underwent two brain follow-up MRI examinations with three-dimensional fluid-attenuated inversion recovery (FLAIR) sequences between September 2016 and December 2019. Two neuroradiology residents independently reviewed FLAIR images using three post-processing methods including conventional reading (CR), co-registration fusion (CF), and co-registration subtraction with color-coding (CS), while being blinded to all data but FLAIR images. The presence and number of new, growing, or shrinking lesions were compared between reading methods. The reading time, reading confidence, and inter- and intra-observer agreements were also assessed. An expert neuroradiologist established the standard of reference. Statistical analyses were corrected for multiple testing., Results: A total of 198 patients with MS were included. There were 130 women and 68 men, with a mean age of 41 ± 12 (standard deviation) years (age range: 21-79 years). Using CS and CF, more patients were detected with new lesions compared to CR (93/198 [47%] and 79/198 [40%] vs. 54/198 [27%], respectively; P < 0.01). The median number of new hyperintense FLAIR lesions detected was significantly greater using CS and CF compared to CR (2 [Q1, Q3: 0, 6] and 1 [Q1, Q3: 0, 3] vs. 0 [Q1, Q3: 0, 1], respectively; P < 0.001). The mean reading time was significantly shorter using CS and CF compared to CR (P < 0.001), with higher confidence in readings and higher inter- and intra-observer agreements., Conclusion: Post-processing tools such as CS and CF substantially improve the accuracy of follow-up MRI examinations in patients with MS while reducing reading time and increasing readers' confidence and reproducibility., (Copyright © 2023 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Objectives: Lung carcinoids and atypical hamartomas may be difficult to differentiate but require different treatment. The aim was to differentiate these tumors using contrast-enhanced CT semantic and radiomics criteria., Methods: Between November 2009 and June 2020, consecutives patient operated for hamartomas or carcinoids with contrast-enhanced chest-CT were retrospectively reviewed. Semantic criteria were recorded and radiomics features were extracted from 3D segmentations using Pyradiomics. Reproducible and non-redundant radiomics features were used to training a random forest algorithm with cross-validation. A validation-set from another institution was used to evaluate of the radiomics signature, the 3D 'median' attenuation feature (3D-median) alone and the mean value from 2D-ROIs., Results: Seventy-three patients (median 58 years [43‒70]) were analyzed (16 hamartomas; 57 carcinoids). The radiomics signature predicted hamartomas vs carcinoids on the external dataset (22 hamartomas; 32 carcinoids) with an AUC = 0.76. The 3D-median was the most important in the model. Density thresholds < 10 HU to predict hamartoma and > 60 HU to predict carcinoids were chosen for their high specificity > 0.90. On the external dataset, sensitivity and specificity of the 3D-median and 2D-ROIs were, respectively, 0.23, 1.00 and 0.13, 1.00 < 10 HU; 0.63, 0.95 and 0.69, 0.91 > 60 HU. The 3D-median was more reproducible than 2D-ROIs (ICC = 0.97 95% CI [0.95‒0.99]; bias: 3 ± 7 HU limits of agreement (LoA) [- 10‒16] vs. ICC = 0.90 95% CI [0.85‒0.94]; bias: - 0.7 ± 21 HU LoA [- 4‒40], respectively)., Conclusions: A radiomics signature can distinguish hamartomas from carcinoids with an AUC = 0.76. Median density < 10 HU and > 60 HU on 3D or 2D-ROIs may be useful in clinical practice to diagnose these tumors with confidence, but 3D is more reproducible., Critical Relevance Statement: Radiomic features help to identify the most discriminating imaging signs using random forest. 'Median' attenuation value (Hounsfield units), extracted from 3D-segmentations on contrast-enhanced chest-CTs, could distinguish carcinoids from atypical hamartomas (AUC = 0.85), was reproducible (ICC = 0.97), and generalized to an external dataset., Key Points: • 3D-'Median' was the best feature to differentiate carcinoids from atypical hamartomas (AUC = 0.85). • 3D-'Median' feature is reproducible (ICC = 0.97) and was generalized to an external dataset. • Radiomics signature from 3D-segmentations differentiated carcinoids from atypical hamartomas with an AUC = 0.76. • 2D-ROI value reached similar performance to 3D-'median' but was less reproducible (ICC = 0.90)., (© 2023. European Society of Radiology (ESR).)

There are no current recommendations on which machine learning (ML) algorithms should be used in radiomics. The objective was to compare performances of ML algorithms in radiomics when applied to different clinical questions to determine whether some strategies could give the best and most stable performances regardless of datasets. This study compares the performances of nine feature selection algorithms combined with fourteen binary classification algorithms on ten datasets. These datasets included radiomics features and clinical diagnosis for binary clinical classifications including COVID-19 pneumonia or sarcopenia on CT, head and neck, orbital or uterine lesions on MRI. For each dataset, a train-test split was created. Each of the 126 (9 × 14) combinations of feature selection algorithms and classification algorithms was trained and tuned using a ten-fold cross validation, then AUC was computed. This procedure was repeated three times per dataset. Best overall performances were obtained with JMI and JMIM as feature selection algorithms and random forest and linear regression models as classification algorithms. The choice of the classification algorithm was the factor explaining most of the performance variation (10% of total variance). The choice of the feature selection algorithm explained only 2% of variation, while the train-test split explained 9%., (© 2023. Springer Nature Limited.)

Artificial intelligence has demonstrated utility and is increasingly being used in the field of radiology. The use of generative pre-trained transformer (GPT)-based models has the potential to revolutionize the field of radiology, offering new possibilities for improving accuracy, efficiency, and patient outcome. Current applications of GPT-based models in radiology include report generation, educational support, clinical decision support, patient communication, and data analysis. As these models continue to advance and improve, it is likely that more innovative uses for GPT-based models in the field of radiology at large will be developed, further enhancing the role of technology in the diagnostic process. ChatGPT is a variant of GPT that is specifically fine-tuned for conversational language understanding and generation. This article reports some answers provided by ChatGPT to various questions that radiologists may have regarding ChatGPT and identifies the potential benefits ChatGPT may offer in their daily practice but also current limitations. Similar to other applications of artificial intelligence in the field of imaging, further formal validation of ChatGPT is required., Competing Interests: Disclosure of interest Philippe Soyer is the Editor-in-Chief of Diagnostic & Interventional Imaging. The other authors have no conflicts of interest to declare., (Copyright © 2023 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Background: Using reliable contrast-enhanced T1 sequences is crucial to detect enhancing brain lesions for multiple sclerosis (MS) at the time of diagnosis and over follow-up. Contrast-enhanced 3D gradient-recalled echo (GRE) T1-weighted imaging (WI) and 3D turbo spin echo (TSE) T1-WI are both available for clinical practice and have never been compared within the context of this diagnosis., Purpose: The aim of this study was to compare contrast-enhanced 3D GRE T1-WI and 3D TSE T1-WI for the detection of enhancing lesions in the brains of MS patients., Methods: This single-center prospective study enrolled patients with MS who underwent a 3.0 T brain MRI from August 2017 to April 2021 for follow-up. Contrast-enhanced 3D GRE T1-WI and 3D TSE T1-WI were acquired in randomized order. Two independent radiologists blinded to all data reported all contrast-enhanced lesions in each sequence. Their readings were compared with a reference standard established by a third expert neuroradiologist. Interobserver agreement, contrast ratio, and contrast-to-noise ratio were calculated for both sequences., Results: A total of 158 MS patients were included (mean age, 40 ± 11 years; 95 women). Significantly more patients had at least 1 contrast-enhanced lesion on 3D TSE T1-WI than on 3D GRE T1-WI for both readers (61/158 [38.6%] vs 48/158 [30.4%] and 60/158 [38.6%] vs 47/158 [29.7%], P < 0.001). Significantly more contrast-enhanced lesions per patient were detected on 3D TSE T1-WI (mean 2.47 vs 1.56 and 2.56 vs 1.39, respectively, P < 0.001). Interobserver agreement was excellent for both sequences, κ = 0.96 (confidence interval [CI], 0.91-1.00) for 3D TSE T1-WI and 0.92 (CI, 0.86-0.99) for 3D GRE T1-WI. Contrast ratio and contrast-to-noise ratio were significantly higher on 3D TSE T1-WI (0.84 vs 0.53, P < 0.001, and 87.9 vs 57.8, P = 0.03, respectively)., Conclusions: At 3.0 T, contrast-enhanced 3D TSE-T1-WI supports the detection of significantly more enhancing lesions than 3D GRE T1-WI and should therefore be used for MS patients requiring contrast-enhanced examination., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: To differentiate OCVM from other orbital lesions using structural MRI., Methods: This IRB-approved a historical-prospective cohort single-center analysis of a prospective cohort that included consecutive adult patients presenting with an orbital lesion undergoing a 3T MRI before surgery from December 2015 to May 2021. Two readers blinded to all data read all MRIs assessing structural MRI characteristics. A univariate analysis followed by a stepwise multivariate analysis identified structural MRI features showing the highest sensitivity and specificity when diagnosing OCVM., Results: One hundred ninety-one patients with 30/191 (16%) OCVM and 161/191 (84%) other orbital lesions were included. OCVM were significantly more likely to present with a higher signal intensity than that of the cortex on T2WI: 26/29 (89.7%) versus 28/160 (17.5%), p < 0.001, or with a chemical shift artifact (CSA): 26/29 (89.7%) versus 16/155 (10.3%), p < 0.001, or to present with a single starting point of enhancement, as compared to other orbital lesions: 18/29 (62.1%) versus 4/159 (2.5%), p = 0.001. The step-wise analysis identified 2 signatures increasing performances. Signature 1 combined a higher signal intensity than that of the cortex on T2WI and a CSA. Signature 2 included these two features and the presence of a single starting point of enhancement. Sensitivity, specificity, and accuracy were 0.83, 0.94, and 0.92 for signature 1 and 0.97, 0.93, and 0.93 for signature 2, respectively., Conclusion: Structural MRI yields high sensitivity and specificity when diagnosing OCVM., Key Points: • Structural MRI shows high sensitivity and specificity when diagnosing orbital cavernous venous malformation. • We identified two signatures combining structural MRI features which might be used easily in routine clinical practice. • The combination of higher signal intensity of the lesion as compared to the cortex on T2WI and of a chemical shift artifact yields a sensitivity and specificity of 0.83 and 0.94 for the diagnosis of orbital cavernous venous malformation, respectively., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

The emergence of massively parallel yet affordable computing devices has been a game changer for research in the field of artificial intelligence (AI). In addition, dramatic investment from the web giants has fostered the development of a high-quality software stack. Going forward, the combination of faster computers with dedicated software libraries and the widespread availability of data has opened the door to more flexibility in the design of AI models. Radiomics is a process used to discover new imaging biomarkers that has multiple applications in radiology and can be used in conjunction with AI. AI can be used throughout the various processes of diagnostic imaging, including data acquisition, reconstruction, analysis and reporting. Today, the concept of "AI-augmented" radiologists is preferred to the theory of the replacement of radiologists by AI in many indications. Current evidence bolsters the assumption that AI-assisted radiologists work better and faster. Interventional radiology becomes a data-rich specialty where the entire procedure is fully recorded in a standardized DICOM format and accessible via standard picture archiving and communication systems. No other interventional specialty can bolster such readiness. In this setting, interventional radiology could lead the development of AI-powered applications in the broader interventional community. This article provides an update on the current status of radiomics and AI research, analyzes upcoming challenges and also discusses the main applications in AI in interventional radiology to help radiologists better understand and criticize articles reporting AI in medical imaging., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial or personal relationships that could be viewed as influencing the work reported in this paper., (Copyright © 2022 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Purpose: Characterizing orbital lesions remains challenging with imaging. The purpose of this study was to compare 3 Tesla (T) to 7 T magnetic resonance imaging (MRI) for characterizing orbital lesions., Materials and Methods: This prospective single-center study enrolled participants presenting with orbital lesions from May to October 2019, who underwent both 7 T and 3 T MRI examinations. Two neuroradiologists, blinded to all data, read both datasets independently and randomly. They assessed general characteristics of each orbital lesion as well as image quality and presence of artifacts. Comparison between both datasets was made using Fisher exact test., Results: Seven patients (4 women, 3 men) with a median age of 52 years were enrolled. Orbital lesion conspicuity was better scored at 7 T compared to 3 T MRI, with 3/7 lesions (43%) scored as very conspicuous at 7 T compared to 0/7 lesion (0%) at 3 T, although the difference was not significant (P = 0.16). Delineation of lesion margins was better scored at 7 T compared to 3 T with 3/7 lesions (43%) scored as very well delineated on 7 T compared to 0/7 lesions (0%) at 3 T, although the difference was not significant (P = 0.34). Details of internal structure were better assessed at 7 T compared to 3 T, with 4/7 lesions (57%) displaying numerous internal details compared to 0/7 lesions (0%) at 3 T (P = 0.10). Internal microvessels were visible in 3/7 lesions (43%) at 7 T compared to 0/7 lesions (0%) at 3 T (P = 0.19)., Conclusion: Although no significant differences were found between 7 T and 3 T MRI, assumably due to a limited number of patients, our study suggests that 7 Tesla MRI might help improve the characterization of orbital lesions. However, further studies with more patients are needed., (Copyright © 2022 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Objectives: To validate a deep learning (DL) algorithm for measurement of skeletal muscular index (SMI) and prediction of overall survival in oncology populations., Methods: A retrospective single-center observational study included patients with metastatic renal cell carcinoma between 2007 and 2019. A set of 37 patients was used for technical validation of the algorithm, comparing manual vs DL-based evaluations. Segmentations were compared using mean Dice similarity coefficient (DSC), SMI using concordance correlation coefficient (CCC) and Bland-Altman plots. Overall survivals (OS) were compared using log-rank (Kaplan-Meier) and Mann-Whitney tests. Generalizability of the prognostic value was tested in an independent validation population (N = 87)., Results: Differences between two manual segmentations (DSC = 0.91, CCC = 0.98 for areas) or manual vs. automated segmentation (DSC = 0.90, CCC = 0.98 for areas, CCC = 0.97 for SMI) had the same order of magnitude. Bland-Altman plots showed a mean difference of -3.33 cm 2 [95%CI: -15.98, 9.1] between two manual segmentations, and -3.28 cm 2 [95% CI: -14.77, 8.21] for manual vs. automated segmentations. With each method, 20/37 (56%) patients were classified as sarcopenic. Sarcopenic vs. non-sarcopenic groups had statistically different survival curves with median OS of 6.0 vs. 12.5 (p = 0.008) and 6.0 vs. 13.9 (p = 0.014) months respectively for manual and DL methods. In the independent validation population, sarcopenic patients according to DL had a lower OS (10.7 vs. 17.3 months, p = 0.033)., Conclusion: A DL algorithm allowed accurate estimation of SMI compared to manual reference standard. The DL-calculated SMI demonstrated a prognostic value in terms of OS., Key Points: • A deep learning algorithm allows accurate estimation of skeletal muscle index compared to a manual reference standard with a concordance correlation coefficient of 0.97. • Sarcopenic patients according to SMI thresholds after segmentation by the deep learning algorithm had statistically significantly lower overall survival compared to non-sarcopenic patients., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Purpose: The purpose of this study was to assess the inter-slice radiomic feature repeatability in ultrasound imaging and the impact of preprocessing using intensity standardization and grey-level discretization to help improve radiomics reproducibility., Materials and Methods: This single-center study enrolled consecutive patients with an orbital lesion who underwent ultrasound examination of the orbit from December 2015 to July 2019. Two images per lesion were randomly assigned to two subsets. Radiomic features were extracted and inter-slice repeatability was assessed using the intraclass correlation coefficient (ICC) between the subsets. The impact of preprocessing on feature repeatability was assessed using image intensity standardization with or without outliers removal on whole images, bounding boxes or regions of interest (ROI), and fixed bin size or fixed bin number grey-level discretization. Number of inter-slice repeatable features (ICC ≥0.7) between methods was compared., Results: Eighty-eight patients (37 men, 51 women) with a mean age of 51.5 ± 17 (SD) years (range: 20-88 years) were enrolled. Without preprocessing, 29/101 features (28.7%) were repeatable between slices. The greatest number of repeatable features (41/101) was obtained using intensity standardization with outliers removal on the ROI and fixed bin size discretization. Standardization performed better with outliers removal than without (P < 0.001), and on ROIs than on native images (P < 0.001). Fixed bin size discretization performed better than fixed bin number (P = 0.008)., Conclusion: Radiomic features extracted from ultrasound images are impacted by the slice and preprocessing. The use of intensity standardization with outliers removal applied to the ROI and a fixed bin size grey-level discretization may improve feature repeatability., (Copyright © 2021 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

The past decade has seen tremendous development in digital health, including in innovative new technologies such as Electronic Health Records, telemedicine, virtual visits, wearable technology and sophisticated analytical tools such as artificial intelligence (AI) and machine learning for the deep-integration of big data. In the field of rare connective tissue diseases (rCTDs), these opportunities include increased access to scarce and remote expertise, improved patient monitoring, increased participation and therapeutic adherence, better patient outcomes and patient empowerment. In this review, we discuss opportunities and key-barriers to improve application of digital health technologies in the field of autoimmune diseases. We also describe what could be the fully digital pathway of rCTD patients. Smart technologies can be used to provide real-world evidence about the natural history of rCTDs, to determine real-life drug utilization, advanced efficacy and safety data for rare diseases and highlight significant unmet needs. Yet, digitalization remains one of the most challenging issues faced by rCTD patients, their physicians and healthcare systems. Digital health technologies offer enormous potential to improve autoimmune rCTD care but this potential has so far been largely unrealized due to those significant obstacles. The need for robust assessments of the efficacy, affordability and scalability of AI in the context of digital health is crucial to improve the care of patients with rare autoimmune diseases., (Copyright © 2021. Published by Elsevier B.V.)

Purpose: To assess the diagnostic performance of radiomic analysis using high temporal resolution (HTR)-dynamic contrast enhancement (DCE) MR sequences compared to BI-RADS analysis to distinguish benign from malignant breast lesions., Materials and Methods: We retrospectively analyzed data from consecutive women who underwent breast MRI including HTR-DCE MR sequencing for abnormal enhancing lesions and who had subsequent pathological analysis at our tertiary center. Semi-quantitative enhancement parameters and textural features were extracted. Temporal change across each phase of textural features in HTR-DCE MR sequences was calculated and called "kinetic textural parameters." Statistical analysis by LASSO logistic regression and cross validation was performed to build a model. The diagnostic performance of the radiomic model was compared to the results of BI-RADS MR score analysis., Results: We included 117 women with a mean age of 54 years (28-88). Of the 174 lesions analyzed, 75 were benign and 99 malignant. Seven semi-quantitative enhancement parameters and 57 textural features were extracted. Regression analysis selected 15 significant variables in a radiomic model (called "malignant probability score") which displayed an AUC = 0.876 (sensitivity = 0.98, specificity = 0.52, accuracy = 0.78). The performance of the malignant probability score to distinguish benign from malignant breast lesions (AUC = 0.876, 95%CI 0.825-0.925) was significantly better than that of BI-RADS analysis (AUC = 0.831, 95%CI 0.769-0.892). The radiomic model significantly reduced false positives (42%) with the same number of missed cancers (n = 2)., Conclusion: A radiomic model including kinetic textural features extracted from an HTR-DCE MR sequence improves diagnostic performance over BI-RADS analysis., Key Points: • Radiomic analysis using HTR-DCE is of better diagnostic performance (AUC = 0.876) than conventional breast MRI reading with BI-RADS (AUC = 0.831) (p < 0.001). • A radiomic malignant probability score under 19.5% gives a negative predictive value of 100% while a malignant probability score over 81% gives a positive predictive value of 100%. • Kinetic textural features extracted from HTR-DCE-MRI have a major role to play in distinguishing benign from malignant breast lesions.

Background The interpretation of radiographs suffers from an ever-increasing workload in emergency and radiology departments, while missed fractures represent up to 80% of diagnostic errors in the emergency department. Purpose To assess the performance of an artificial intelligence (AI) system designed to aid radiologists and emergency physicians in the detection and localization of appendicular skeletal fractures. Materials and Methods The AI system was previously trained on 60 170 radiographs obtained in patients with trauma. The radiographs were randomly split into 70% training, 10% validation, and 20% test sets. Between 2016 and 2018, 600 adult patients in whom multiview radiographs had been obtained after a recent trauma, with or without one or more fractures of shoulder, arm, hand, pelvis, leg, and foot, were retrospectively included from 17 French medical centers. Radiographs with quality precluding human interpretation or containing only obvious fractures were excluded. Six radiologists and six emergency physicians were asked to detect and localize fractures with ( n = 300) and fractures without ( n = 300) the aid of software highlighting boxes around AI-detected fractures. Aided and unaided sensitivity, specificity, and reading times were compared by means of paired Student t tests after averaging of performances of each reader. Results A total of 600 patients (mean age ± standard deviation, 57 years ± 22; 358 women) were included. The AI aid improved the sensitivity of physicians by 8.7% (95% CI: 3.1, 14.2; P = .003 for superiority) and the specificity by 4.1% (95% CI: 0.5, 7.7; P < .001 for noninferiority) and reduced the average number of false-positive fractures per patient by 41.9% (95% CI: 12.8, 61.3; P = .02) in patients without fractures and the mean reading time by 15.0% (95% CI: -30.4, 3.8; P = .12). Finally, stand-alone performance of a newer release of the AI system was greater than that of all unaided readers, including skeletal expert radiologists, with an area under the receiver operating characteristic curve of 0.94 (95% CI: 0.92, 0.96). Conclusion The artificial intelligence aid provided a gain of sensitivity (8.7% increase) and specificity (4.1% increase) without loss of reading speed. © RSNA, 2021 Online supplemental material is available for this article.

Objective: Test a practical realignment approach to compensate the technical variability of MR radiomic features., Methods: T1 phantom images acquired on 2 scanners, FLAIR and contrast-enhanced T1-weighted (CE-T1w) images of 18 brain tumor patients scanned on both 1.5-T and 3-T scanners, and 36 T2-weighted (T2w) images of prostate cancer patients scanned in one of two centers were investigated. The ComBat procedure was used for harmonizing radiomic features. Differences in statistical distributions in feature values between 1.5- and 3-T images were tested before and after harmonization. The prostate studies were used to determine the impact of harmonization to distinguish between Gleason grades (GGs)., Results: In the phantom data, 40 out of 42 radiomic feature values were significantly different between the 2 scanners before harmonization and none after. In white matter regions, the statistical distributions of features were significantly different (p < 0.05) between the 1.5- and 3-T images for 37 out of 42 features in both FLAIR and CE-T1w images. After harmonization, no statistically significant differences were observed. In brain tumors, 41 (FLAIR) or 36 (CE-T1w) out of 42 features were significantly different between the 1.5- and 3-T images without harmonization, against 1 (FLAIR) or none (CE-T1w) with harmonization. In prostate studies, 636 radiomic features were significantly different between GGs after harmonization against 461 before. The ability to distinguish between GGs using radiomic features was increased after harmonization., Conclusion: ComBat harmonization efficiently removes inter-center technical inconsistencies in radiomic feature values and increases the sensitivity of studies using data from several scanners., Key Points: • Radiomic feature values obtained using different MR scanners or imaging protocols can be harmonized by combining off-the-shelf image standardization and feature realignment procedures. • Harmonized radiomic features enable one to pool data from different scanners and centers without a substantial loss of statistical power caused by intra- and inter-center variability. • The proposed realignment method is applicable to radiomic features from different MR sequences and tumor types and does not rely on any phantom acquisition.

Objectives: Distinguishing benign from malignant orbital lesions remains challenging both clinically and with imaging, leading to risky biopsies. The objective was to differentiate benign from malignant orbital lesions using radiomics on 3 T magnetic resonance imaging (MRI) examinations., Materials and Methods: This institutional review board-approved prospective single-center study enrolled consecutive patients presenting with an orbital lesion undergoing a 3 T MRI prior to surgery from December 2015 to July 2019. Radiomics features were extracted from 6 MRI sequences (T1-weighted images [WIs], DIXON-T2-WI, diffusion-WI, postcontrast DIXON-T1-WI) using the Pyradiomics software. Features were selected based on their intraobserver and interobserver reproducibility, nonredundancy, and with a sequential step forward feature selection method. Selected features were used to train and optimize a Random Forest algorithm on the training set (75%) with 5-fold cross-validation. Performance metrics were computed on a held-out test set (25%) with bootstrap 95% confidence intervals (95% CIs). Five residents, 4 general radiologists, and 3 expert neuroradiologists were evaluated on their ability to visually distinguish benign from malignant lesions on the test set. Performance comparisons between reader groups and the model were performed using McNemar test. The impact of clinical and categorizable imaging data on algorithm performance was also assessed., Results: A total of 200 patients (116 [58%] women and 84 [42%] men; mean age, 53.0 ± 17.9 years) with 126 of 200 (63%) benign and 74 of 200 (37%) malignant orbital lesions were included in the study. A total of 606 radiomics features were extracted. The best performing model on the training set was composed of 8 features including apparent diffusion coefficient mean value, maximum diameter on T1-WIs, and texture features. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity on the test set were respectively 0.869 (95% CI, 0.834-0.898), 0.840 (95% CI, 0.806-0.874), 0.684 (95% CI, 0.615-0.751), and 0.935 (95% CI, 0.905-0.961). The radiomics model outperformed all reader groups, including expert neuroradiologists (P < 0.01). Adding clinical and categorizable imaging data did not significantly impact the algorithm performance (P = 0.49)., Conclusions: An MRI radiomics signature is helpful in differentiating benign from malignant orbital lesions and may outperform expert radiologists., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: To determine the diagnostic accuracy of MRI intravoxel incoherent motion (IVIM) when characterizing orbital lesions, which is challenging due to a wide range of locations and histologic types., Methods: This IRB-approved prospective single-center study enrolled participants presenting with an orbital lesion undergoing a 3-T MRI prior to surgery from December 2015 to July 2019. An IVIM sequence with 15 b values ranging from 0 to 2000 s/mm 2 was performed. Two neuroradiologists, blinded to clinical data, individually analyzed morphological MRIs. They drew one region of interest inside each orbital lesion, providing apparent diffusion coefficient (ADC), true diffusion coefficient (D), perfusion fraction (f), and pseudodiffusion coefficient (D*) values. T test, Mann-Whitney U test, and receiver operating characteristic curve analyses were performed to discriminate between orbital lesions and to determine the diagnostic accuracy of the IVIM parameters., Results: One hundred fifty-six participants (84 women and 72 men, mean age 54.4 ± 17.5 years) with 167 orbital lesions (98/167 [59%] benign lesions including 54 orbital inflammations and 69/167 [41%] malignant lesions including 32 lymphomas) were included in the study. ADC and D were significantly lower in malignant than in benign lesions: 0.8 × 10 -3  mm 2 /s [0.45] versus 1.04 × 10 -3  mm 2 /s [0.33], p < 0.001, and 0.75 × 10 -3  mm 2 /s [0.40] versus 0.98 × 10 -3  mm 2 /s [0.42], p < 0.001, respectively. D* was significantly higher in malignant lesions than in benign ones: 12.8 × 10 -3  mm 2 /s [20.17] versus 7.52 × 10 -3  mm 2 /s [7.57], p = 0.005. Area under curve was of 0.73, 0.74, 0.72, and 0.81 for ADC, D, D*, and a combination of D, f, and D*, respectively., Conclusions: Our study showed that IVIM might help better characterize orbital lesions., Key Points: • Intravoxel incoherent motion (IVIM) helps clinicians to assess patients with orbital lesions. • Intravoxel incoherent motion (IVIM) helps clinicians to characterize orbital lymphoma versus orbital inflammation. • Management of patients becomes more appropriate.

Background and Purpose: To compare the accuracy and utility of contrast enhanced magnetic resonance angiography (MRA) (CEMRA) to Time of Flight MRA (TOF MRA) during detection and evaluation of occlusions on patients diagnosed with acute ischemic stroke (AIS)., Methods: This single-center study was approved by our local institutional research ethics board. From August 2014 to July 2016, 131 consecutive adult patients with confirmed AIS were included. Detection of an arterial occlusion and its characterization were evaluated independently with CEMRA or TOF MRA by two blinded neuroradiologists, then by consensus using all available MR sequences. A Cohen's Kappa coefficient (κ) and intra-class correlation coefficients (ICC) were used to compare the two techniques., Results: There was substantial concordance in the detection of arterial occlusion between CEMRA and TOF MRA (κ = 0.75). TOF MRA was more likely to show an arterial occlusion than CEMRA (63 versus 52 patients respectively). There were 13 and 1 false positive arterial occlusion with TOF MRA and CEMRA respectively, and 1 false negative with TOF MRA. There was excellent concordance between the location of arterial occlusions and CEMRA and TOF MRA [κ = 0.89 (0.72-0.97)]. CEMRA was significantly more likely to allow measurement of the thrombus than was TOF MRA [38 (75%) versus 14 (22%)] (P < 0.0001)., Conclusions: Our study showed that CEMRA imaging detected arterial occlusions better than TOF MRA in AIS patients and more precisely such that thrombus length and location could be known, which improves the patient's management and care., (Copyright © 2019 Elsevier Masson SAS. All rights reserved.)

Background: Anterior ischemic optic neuropathy (AION) is the most common cause of acute optic neuropathy in older patients. Distinguishing between arteritic AION (A-AION) and nonarteritic (NA-AION) is paramount for improved patient management., Purpose: The aim of this study was to evaluate 3-dimensional high-resolution vessel wall (HR-VW) magnetic resonance imaging (MRI) at 3 T to discriminate A-AION from NA-AION., Materials and Methods: This prospective single-center study was approved by a national research ethics board and included 27 patients (17 A-AION and 10 NA-AION) with 36 AIONs from December 2014 to August 2017 who underwent 3 T HR-VW MRI. Two radiologists blinded to clinical data individually analyzed the imaging separately and in random order. Discrepancies were resolved by consensus with a third neuroradiologist. The primary diagnostic criterion was the presence of inflammatory changes of the ophthalmic artery. Secondary diagnostic criteria included the presence of an enhancement of the optic nerve or its sheath, the optic disc, or inflammatory changes of posterior ciliary or extracranial arteries. A Fisher exact test was used to compare A-AION from NA-AION patients., Results: Inflammatory changes of the ophthalmic artery were present in all patients with A-AION but in none of NA-AION (P < 0.0001). Its sensitivity, specificity, positive predictive value, and negative predictive value were 100%. Inflammatory changes of posterior ciliary arteries were significantly more likely in A-AOIN (82% vs 0%, P < 0.0001). Interreader and intrareader agreements were almost perfect (κ = 0.82-1)., Conclusions: High-resolution vessel wall MRI seems highly accurate when distinguishing A-AION from NA-AION and might be useful to improve patient management.

Multinodular and vacuolating neuronal tumor of the cerebrum is a rare supratentorial brain tumor described for the first time in 2013. Here, we report 11 cases of infratentorial lesions showing similar striking imaging features consisting of a cluster of low T1-weighted imaging and high T2-FLAIR signal intensity nodules, which we referred to as multinodular and vacuolating posterior fossa lesions of unknown significance. No relationship was found between the location of the lesion and clinical symptoms. A T2-FLAIR hypointense central dot sign was present in images of 9/11 (82%) patients. Cortical involvement was present in 2/11 (18%) of patients. Only 1 nodule of 1 multinodular and vacuolating posterior fossa lesion of unknown significance showed enhancement on postcontrast T1WI. DWI, SWI, MRS, and PWI showed no malignant pattern. Lesions did not change in size or signal during a median follow-up of 3 years, suggesting that multinodular and vacuolating posterior fossa lesions of unknown significance are benign malformative lesions that do not require surgical intervention or removal., (© 2019 by American Journal of Neuroradiology.)

Purpose: The DAWN trial (Diffusion weighted imaging or CT perfusion Assessment with clinical mismatch in the triage of Wake-up and late presenting strokes undergoing Neurointervention with Trevo) has demonstrated the benefits of thrombectomy in patients with unknown or late onset strokes, using automated software (RAPID) for measurement of infarct volume. Because RAPID is not available in all centers, we aimed to assess the accuracy and repeatability of visual infarct volume estimation by clinicians and the consequences for thrombectomy decisions based on the DAWN criteria., Materials and Methods: 18 physicians, who routinely depend on MRI for acute stroke imaging, assessed 32 MR scans selected from a prospective databaseover two independent sessions. Raters were asked to visually estimate the diffusion weighted imaging (DWI) infarct volume for each case. Sensitivity, specificity, and accuracy of the estimated volumes were compared with the available RAPID measurements for various volume cut-off points. Thrombectomy decisions based on DAWN criteria with RAPID measurements and raters' visual estimates were compared. Inter-rater and intra-rater agreement was measured using kappa statistics., Results: The mean accuracy of raters was <90% for all volume cut-points. Inter-rater agreement was below substantial for each DWI infarct volume cut-off points. Intra-rater agreement was substantial for 55-83% of raters, depending on the selected cut-off points. Applying DAWN criteria with visual estimates instead of RAPID measurements led to 19% erroneous thrombectomy decisions, and showed a lack of reproducibility., Conclusion: The visual assessment of DWI infarct volume lacks accuracy and repeatability, and could lead to a significant number of erroneous decisions when applying the DAWN criteria., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: To assess the influence of gray-level discretization on inter- and intra-observer reproducibility of texture radiomics features on clinical MR images., Materials and Methods: We studied two independent MRI datasets of 74 lacrymal gland tumors and 30 breast lesions from two different centers. Two pairs of readers performed three two-dimensional delineations for each dataset. Texture features were extracted using two radiomics softwares (Pyradiomics and an in-house software). Reproducible features were selected using a combination of intra-class correlation coefficient (ICC) and concordance and coherence coefficient (CCC) with 0.8 and 0.9 as thresholds, respectively. We tested six absolute and eight relative gray-level discretization methods and analyzed the distribution and highest number of reproducible features obtained for each discretization. We also analyzed the number of reproducible features extracted from computer simulated delineations representative of inter-observer variability., Results: The gray-level discretization method had a direct impact on texture feature reproducibility, independent of observers, software or method of delineation (simulated vs. human). The absolute discretization consistently provided statistically significantly more reproducible features than the relative discretization. Varying the bin number of relative discretization led to statistically significantly more variable results than varying the bin size of absolute discretization., Conclusions: When considering inter-observer reproducible results of MRI texture radiomics features, an absolute discretization should be favored to allow the extraction of the highest number of potential candidates for new imaging biomarkers. Whichever the chosen method, it should be systematically documented to allow replicability of results., Competing Interests: The authors have declared that no competing interests exist.

Background: Magnetic resonance imaging (MRI) is the diagnostic cornerstone for precisely identifying acute ischaemic strokes and locating vascular occlusions, especially since mechanical thrombectomy has become a reference treatment. We observed that a post-contrast three-dimensional turbo-spin-echo T1-weighted sequence showed striking post-contrast vascular hyperintensities (PCVH) in ischaemic territories. We aimed to evaluate the prevalence and the meaning of this finding., Methods: This retrospective single centre study included 130 consecutive patients admitted for acute ischaemic stroke with a 3-T MRI performed in the first 12 h of symptom onset from September 2014 through September 2016. Two neuroradiologists blinded to clinical data analysed the first MRI assessments. The association between PCVH and clinical, radiological and follow-up findings was assessed, as well as inter- and intra-observer agreements., Results: Of 130 patients, 105 (81%) had PCVH in the ischaemic territory. PCVH were associated with the presence of thrombus on susceptibility weighted imaging (p < 0.0001) and vascular occlusions on MR angiography (p < 0.0001). All patients with a visible thrombus had PCVH closely surrounding the clot. PCVH were associated with higher initial (p < 0.01) and follow-up (p < 0.01) National Institutes of Health Stroke Scale score, and higher mRS score (p < 0.05). Thrombectomy was the reference treatment for all patients with arterial occlusions. Inter- and intra-observer agreements for the detection of PCVH were excellent (κ = 0.95 and κ = 0.91, respectively)., Conclusions: PCVH during acute strokes are a striking sensitive and reproducible tool for diagnosing and locating vascular occlusions. It may help triage patients who can benefit from thrombectomy., Key Points: • Post-contrast vascular hyperintensities (PCVH) are a sensitive MR finding in acute stroke • PCVH are strongly associated with the presence and location of arterial occlusions • Inter- and intra-observer agreements for the detection of PCVH are excellent • PCVH are visible even in the case of significant motion artefacts • PCVH may help triage patients who can benefit from mechanical thrombectomy.

Background and Purpose: MR imaging is the key examination in the follow-up of patients with MS, by identification of new high-signal T2 brain lesions. However, identifying new lesions when scrolling through 2 follow-up MR images can be difficult and time-consuming. Our aim was to compare an automated coregistration-fusion reading approach with the standard approach by identifying new high-signal T2 brain lesions in patients with multiple sclerosis during follow-up MR imaging., Materials and Methods: This prospective monocenter study included 94 patients (mean age, 38.9 years) treated for MS with dimethyl fumarate from January 2014 to August 2016. One senior neuroradiologist and 1 junior radiologist checked for new high-signal T2 brain lesions, independently analyzing blinded image datasets with automated coregistration-fusion or the standard scroll-through approach with a 3-week delay between the 2 readings. A consensus reading with a second senior neuroradiologist served as a criterion standard for analyses. A Poisson regression and logistic and γ regressions were used to compare the 2 methods. Intra- and interobserver agreement was assessed by the κ coefficient., Results: There were significantly more new high-signal T2 lesions per patient detected with the coregistration-fusion method (7 versus 4, P < .001). The coregistration-fusion method detected significantly more patients with at least 1 new high-signal T2 lesion (59% versus 46%, P = .02) and was associated with significantly faster overall reading time (86 seconds faster, P < .001) and higher reader confidence (91% versus 40%, P < 1 × 10 -4 ). Inter- and intraobserver agreement was excellent for counting new high-signal T2 lesions., Conclusions: Our study showed that an automated coregistration-fusion method was more sensitive for detecting new high-signal T2 lesions in patients with MS and reducing reading time. This method could help to improve follow-up care., (© 2018 by American Journal of Neuroradiology.)

Purpose: The current staging system of central nervous system (CNS) germ cell tumors (GCT) includes a binary classification in "localized" or "metastatic" disease based on the absence or presence of leptomeningeal dissemination. Loco-regional tumor dissemination has been barely described whereas its accurate definition might be useful in terms of prognosis and treatment, especially for radiation therapy planning. Our purpose was therefore to describe MR patterns and prevalence of loco-regional extensions of these tumors., Methods: One hundred consecutive patients (median age 16.3 years, range 7-41 years, sex ratio 7:1) with a histologically or biologically proven CNS GCT were retrospectively included. Brain and spinal MRI at diagnosis were reviewed by two neuroradiologists focusing on MR patterns of primaries and loco-regional extensions. When available, follow-up MR exams were analyzed., Results: Pure germinoma represented 84/100 cases. Primaries were unifocal pineal (n = 49/100), bifocal pineal and supra-sellar (n = 27/100), isolated supra-sellar (n = 21/100), isolated basal ganglia (n = 2/100) or trifocal pineal, supra-sellar, and basal ganglia (n = 1/100). Metastatic disease occurred in 6/100 patients (depicted by MRI in two and CSF cytology in four). Loco-regional extensions were observed in all patients and classified as follows: third ventricle (n = 88/100), thalamus (n = 47/100), midbrain (n = 42/100), distant sub-ependymal areas (n = 19/100), optic pathways (n = 19/100), lateral ventricles (n = 7/100), cavernous sinus (n = 6/100), corpus callosum (n = 4/100), and fourth ventricle (n = 3/100)., Conclusion: CNS GCT present with specific loco-regional extensions at diagnosis. Improving their recognition will be helpful to further understand their prognostic value and potentially to optimize the treatment.

Background and Purpose: We aimed to study the intrarater and interrater agreement of clinicians attributing DWI-ASPECTS (Diffusion-Weighted Imaging-Alberta Stroke Program Early Computed Tomography Scores) and DWI-FLAIR (Diffusion-Weighted Imaging-Fluid Attenuated Inversion Recovery) mismatch in patients with acute ischemic stroke referred for mechanical thrombectomy., Methods: Eighteen raters independently scored anonymized magnetic resonance imaging scans of 30 participants from a multicentre thrombectomy trial, in 2 different reading sessions. Agreement was measured using Fleiss κ and Cohen κ statistics., Results: Interrater agreement for DWI-ASPECTS was slight (κ=0.17 [0.14-0.21]). Four raters (22.2%) had a substantial (or higher) intrarater agreement. Dichotomization of the DWI-ASPECTS (0-5 versus 6-10 or 0-6 versus 7-10) increased the interrater agreement to a substantial level (κ=0.62 [0.48-0.75] and 0.68 [0.55-0.79], respectively) and more raters reached a substantial (or higher) intrarater agreement (17/18 raters [94.4%]). Interrater agreement for DWI-FLAIR mismatch was moderate (κ=0.43 [0.33-0.57]); 11 raters (61.1%) reached a substantial (or higher) intrarater agreement., Conclusions: Agreement between clinicians assessing DWI-ASPECTS and DWI-FLAIR mismatch may not be sufficient to make repeatable clinical decisions in mechanical thrombectomy. The dichotomization of the DWI-ASPECTS (0-5 versus 0-6 or 0-6 versus 7-10) improved interrater and intrarater agreement, however, its relevance for patients selection for mechanical thrombectomy needs to be validated in a randomized trial., (© 2017 American Heart Association, Inc.)

Pulmonary hyalinizing granuloma (PHG) is a rare disease characterized by single or multiple benign lung nodules mimicking lung neoplasma. Histologic analysis reveals homogenous hyaline lamellae, usually surrounded by collection of plasma cells, lymphocytes and histiocytes in a perivascular distribution. The clinical and radiological findings have been described in small series, but the long-term outcomes have rarely been reported. The objectives were to describe the clinical, radiological and outcomes of PHG in new cases and through a literature review. Patients with PHG were found by a multicenter search among French departments of internal medicine, pulmonology and anatomo-pathology. Review of the literature was made through the National Library of Medicine's MEDLINE database using keywords "hyalinizing granuloma." Five news cases and 135 cases of the literature were found. There were 82 men and 57 women, mean age at the diagnosis 44.6 years (15-83). Patients were frequently asymptomatic (n = 39, 27.4 %). The nodule was unique in 37 cases (28.9 %) and multiple in 91 cases (71.1 %). 18FDG PET scan revealed hypermetabolism of the nodule in 9/15 cases (60 %). A systemic disease was associated in 65 cases (mainly mediastinal and retroperitoneal fibrosis, autoimmune, tumoral or infectious disease or thromboembolism). The outcomes were evaluated in 73 patients when follow-up was available: 14 patients had a surgical resection of the nodule. Forty-five patients did not receive any immunosuppressive drug. Among these patients, 2 improved, 29 were stable and 14 worsened. Corticosteroids were used as a monotherapy in 19 patients and led to radiological improvement in 8 cases, stabilization in 8 cases and worsening in 3 cases. Five patients were treated with corticosteroids and at least one immunosuppressive drug and 4 patients improved. PHG is a rare benign disease, mimicking lung neoplasma, frequently associated with systemic diseases.

Introduction: Shrinking lung syndrome (SLS) is a rare respiratory manifestation of systemic lupus erythematosus (SLE), characterized by dyspnea, chest pain, elevated hemidiaphragm and a restrictive pattern on pulmonary function tests. Here, we report 15 new observations of SLS during SLE and provide a systematic literature review. We studied the clinical, biological, functional and morphologic characteristics, the treatments used and their efficacy., Methods: The inclusion criteria were all patients with SLE defined by the American College of Rheumatology criteria Hochberg (1997) , associated with a restrictive pattern on pulmonary function tests. The exclusion criteria were all differential diagnoses of restrictive patterns, including obesity and pulmonary fibrosis. The patients were recruited from local databases through chest physicians, rheumatologists and internists. The data for the literature review were extracted from the Medline database using "shrinking lung syndrome" and "lupus" as key words., Results: All 15 new cases were women with a median age at SLS onset of 27years old (range 17-67years). All of them complained of dyspnea and all but one of chest pain. The antibodies were similar to those found in SLE, although the anti-SS-A was positive in 10 of 13 cases. Thoracic imaging showed elevated hemidiaphragm (12/15) and/or basal atelectasia (8/15). All of the patients had an isolated restrictive pattern on PFT, with a median decrease >50% of lung volume. All of the patients were treated, using corticosteroids (11/15), immunosuppressive drugs (8/15), beta-mimetics (2/15), physiotherapy (3/15) and/or colchicine (1/15). Improvement was described in 9 of 12 patients and stability in 3 of 12. We extracted 155 cases of SLE-associated SLS from the Medline database. The clinical, biological and functional parameters were similar to our cases. Clinical improvement was described in 48 of 52 cases (94%) and PFT improvement in 36 of 47 cases. Worsening occurred in 4 cases., Conclusion: SLS is a rare SLE manifestation. Pain and parietal inflammation seem to play important pathogenic roles. Steroids and antalgics are the most commonly used therapies with good responses. There is no proof of efficacy with immunosuppressive drugs for this entity. Rituximab can be discussed after failure of corticosteroids, as well as antalgics, theophylline and beta-mimetics., (Copyright © 2016 Elsevier B.V. All rights reserved.)