Our study improved the description of an unclassified, clinico-radiologic entity, which could be described by the proposed acronym: TransIent Perivascular Inflammation of the Carotid artery (TIPIC) syndrome.
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 1.5T and 3T scanners, and 36 T2weighted (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 3T images were tested before and after harmonization. The prostate studies were used to determine the impact of harmonization to distinguish between Gleason grades (GG).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 3T 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 3T images without harmonization, against 1 (FLAIR) or none (CE-T1w) with harmonization. In prostate studies, 636 radiomic features were significantly different between GG after harmonization against 461 before. The ability to distinguish between GG 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.
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.
BACKGROUND AND PURPOSE: Leptomeningeal enhancement can be found in a variety of neurologic diseases such as Susac Syndrome. Our aim was to assess its prevalence and significance of leptomeningeal enhancement in Susac syndrome using 3T postcontrast fluidattenuated inversion recovery MR imaging. MATERIALS AND METHODS: From January 2011 to December 2017, nine consecutive patients with Susac syndrome and a control group of 73 patients with multiple sclerosis or clinically isolated syndrome were included. Two neuroradiologists blinded to the clinical and ophthalmologic data independently reviewed MRIs and assessed leptomeningeal enhancement and parenchymal abnormalities. Follow-up MRIs (5.9 MRIs is the mean number per patient over a median period of 46 months) of patients with Susac syndrome were reviewed and compared with clinical and retinal fluorescein angiographic data evaluated by an independent ophthalmologist. Fisher tests were used to compare the 2 groups, and mixed-effects logistic models were used for analysis of clinical and imaging follow-up of patients with Susac syndrome. RESULTS: Patients with Susac syndrome were significantly more likely to present with leptomeningeal enhancement: 5/9 (56%) versus 6/73 (8%) in the control group (P ϭ .002). They had a significantly higher leptomeningeal enhancement burden with Ն3 lesions in 5/9 patients versus 0/73 (P Ͻ .001). Regions of leptomeningeal enhancement were significantly more likely to be located in the posterior fossa: 5/9 versus 0/73 (P Ͻ .001). Interobserver agreement for leptomeningeal enhancement was good (ϭ 0.79). There was a significant association between clinical relapses and increase of both leptomeningeal enhancement and parenchymal lesion load: OR ϭ 6.15 (P ϭ .01) and OR ϭ 5 (P ϭ .02), respectively. CONCLUSIONS: Leptomeningeal enhancement occurs frequently in Susac syndrome and could be helpful for diagnosis and in predicting clinical relapse. ABBREVIATIONS: CIS ϭ clinically isolated syndrome; CC ϭ corpus callosum; FA ϭ fluorescein angiography; LME ϭ leptomeningeal enhancement; pcFLAIR ϭ postcontrast FLAIR; SuS ϭ Susac syndrome S usac Syndrome (SuS) is a vasculopathy characterized by a triad of neurologic, hearing, and ophthalmologic disorders. 1-3 Fluorescein angiography (FA) typically shows branch retinal ar
The term orbital tumor covers a wide range of benign and malignant diseases affecting specific component of the orbit or developing in contact with them. They are found incidentally or may be investigated as part of the assessment of a systemic disorder or because of orbital signs (exophthalmos, pain, etc.). Computed tomography, MRI and Color Doppler Ultrasound (CDU), play a varying role depending on the clinical presentation and the disease being investigated. This article reflects long experience in a reference center but does not claim to be exhaustive. We have chosen to consider these tumors from the perspective of their usual presentation, emphasizing the most common causes and suggestive radiological and clinical presentations (progressive or sudden-onset exophthalmos, children or adults, lacrimal gland lesions, periorbital lesions and enophthalmos). We will describe in particular muscle involvement (thyrotoxicosis and tumors), vascular lesions (cavernous sinus hemangioma, orbital varix, cystic lymphangioma), childhood lesions and orbital hematomas. We offer straightforward useful protocols for simple investigation and differential diagnosis. Readers who wish to go further to extend their knowledge in this fascinating area can refer to the references in the bibliography.
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