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Phenotyping spinal abnormalities in patients with Neurofibromatosis type 1 using whole-body MRI.

ABSTRACT: Neurofibromatosis Type 1 (NF1) has been reported to be associated with a variety of spinal abnormalities. The purpose of this study was to quantify the prevalence of spinal abnormalities in a collective of NF1 patients that is representative for the general NF1 population, to associate the co-appearance of spinal abnormalities with both NF1 and clinical symptoms and to investigate if different mutations of the NF1 gene affect the prevalence of these abnormalities. Retrospectively, 275 patients with NF1 and an age- and sex-matched collective of 262 patients were analyzed. The prevalence of spinal abnormalities was recorded. Mutational analysis of the NF1 gene was obtained in 235 NF1 patients. Associations between spinal abnormalities, clinical symptoms and genotype were investigated by binary logistic regression analysis. Prevalence of all spinal abnormalities was higher in NF1 patients than in the control group. Six characteristics of spinal abnormalities were significantly associated with NF1 (all p < 0.05). An influence of scalloping on scoliosis (OR 3.01; p = 0.002); of meningoceles (OR 7.63) and neuroforaminal tumors (OR 2.96) on scalloping, and of dural ectasia on neuroforaminal tumors (OR 1.93) was identified. Backpain and loss of motor function were associated with neuroforaminal tumors, spinal tumors and scalloping of vertebral bodies (all p < 0.05). Specific mutations of the NF1 gene were not relevantly associated with the development of spinal abnormalities. These findings can aid clinicians to improve clinical care of NF1 patients by creating awareness for co-appearences of specific spinal abnormalities and associated symptoms.

SUBMITTER: Well L

PROVIDER: S-EPMC8376946 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundNeurofibromatosis type 1 (NF1) is a common human genetic disease with age-dependent phenotype progression. The overview of clinical and radiological findings evaluated by whole-body magnetic resonance imaging (WBMRI) in NF1 patients < 3 years old assessed with a genetic contribution to disease progression is presented herein.MethodsThis study included 70 clinically or genetically diagnosed NF1 patients who received WBMRI before 3 years old. Clinical, genetic, and radiologic features were collected by retrospective chart review. In NF1+, widely spread diffuse cutaneous neurofibromas, developmental delay, autism, seizure, cardiac abnormalities, hearing defect, optic pathway glioma, severe plexiform neurofibromas (> 3 cm in diameter, disfigurement, accompanying pain, bony destruction, or located para-aortic area), brain tumors, nerve root tumors, malignant peripheral nerve sheath tumors, moyamoya disease, and bony dysplasia were included.ResultsThe age at WBMRI was 1.6 ± 0.7 years old, and NF1 mutations were found in 66 patients (94.3%). Focal areas of signal intensity (FASI) were the most common WBMRI finding (66.1%), followed by optic pathway glioma (15.7%), spine dural ectasia (12.9%), and plexiform neurofibromas (10.0%). Plexiform neurofibromas and NF1+ were more prevalent in familial case (28.7% vs 5.7%, p = 0.030; 71.4% vs 30.2%, p = 0.011). Follow-up WBMRI was conducted in 42 patients (23 girls and 19 boys) after 1.21 ± 0.50 years. FASI and radiologic progression were more frequent in patients with mutations involving GTPase activating protein-related domain (77.8% vs 52.4%, p = 0.047; 46.2% vs 7.7%, p = 0.029).ConclusionsWBMRI provides important information for the clinical care for young pediatric NF1 patients. As NF1 progresses in even these young patients, and is related to family history and the affected NF1 domains, serial evaluation with WBMRI should be assessed based on the clinical and genetic features for the patients' best care.

Project description:ObjectivesCytogenetic abnormalities are predictors of poor prognosis in multiple myeloma (MM). This paper aims to build and validate a multiparametric conventional and functional whole-body MRI-based prediction model for cytogenetic risk classification in newly diagnosed MM.MethodsPatients with newly diagnosed MM who underwent multiparametric conventional whole-body MRI, spinal dynamic contrast-enhanced (DCE-)MRI, spinal diffusion-weighted MRI (DWI) and had genetic analysis were retrospectively included (2011-2020/Ghent University Hospital/Belgium). Patients were stratified into standard versus intermediate/high cytogenetic risk groups. After segmentation, 303 MRI features were extracted. Univariate and model-based methods were evaluated for feature and model selection. Testing was performed using receiver operating characteristic (ROC) and precision-recall curves. Models comparing the performance for genetic risk classification of the entire MRI protocol and of all MRI sequences separately were evaluated, including all features. Four final models, including only the top three most predictive features, were evaluated.ResultsThirty-one patients were enrolled (mean age 66 ± 7 years, 15 men, 13 intermediate-/high-risk genetics). None of the univariate models and none of the models with all features included achieved good performance. The best performing model with only the three most predictive features and including all MRI sequences reached a ROC-area-under-the-curve of 0.80 and precision-recall-area-under-the-curve of 0.79. The highest statistical performance was reached when all three MRI sequences were combined (conventional whole-body MRI + DCE-MRI + DWI). Conventional MRI always outperformed the other sequences. DCE-MRI always outperformed DWI, except for specificity.ConclusionsA multiparametric MRI-based model has a better performance in the noninvasive prediction of high-risk cytogenetics in newly diagnosed MM than conventional MRI alone.Critical relevance statementAn elaborate multiparametric MRI-based model performs better than conventional MRI alone for the noninvasive prediction of high-risk cytogenetics in newly diagnosed multiple myeloma; this opens opportunities to assess genetic heterogeneity thus overcoming sampling bias.Key points• Standard genetic techniques in multiple myeloma patients suffer from sampling bias due to tumoral heterogeneity. • Multiparametric MRI noninvasively predicts genetic risk in multiple myeloma. • Combined conventional anatomical MRI, DCE-MRI, and DWI had the highest statistical performance to predict genetic risk. • Conventional MRI alone always outperformed DCE-MRI and DWI separately to predict genetic risk. DCE-MRI alone always outperformed DWI separately, except for the parameter specificity to predict genetic risk. • This multiparametric MRI-based genetic risk prediction model opens opportunities to noninvasively assess genetic heterogeneity thereby overcoming sampling bias in predicting genetic risk in multiple myeloma.

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Phenotyping spinal abnormalities in patients with Neurofibromatosis type 1 using whole-body MRI.

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