MRI features of plexiform neurofibromas involving the liver and pancreas in children with neurofibromatosis type 1

Neurofibromatosis type 1 (NF1), also known as peripheral neurofibromatosis or von Recklinghausen’s disease, is one of the most common genetic disorders. It is inherited in an autosomal dominant pattern. Multiple cutaneous neurofibromas are hallmark lesions of NF1. Localized and plexiform neurofibromas of the paraspinal and sacral region are the most common abdominal neoplasms in NF1. Herein, we report two patients with a known history of NF1 presenting with multiple, extensive localized and plexiform neurofibromas. We describe the important distinguishing features of these tumors as seen on magnetic resonance imaging (MRI), including very bright signal intensity and target sign on T2 weighted images.

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NIMG-08. A MULTI-CENTER RADIOMICS-BASED MODEL TO DIFFERENTIATE BETWEEN NEUROFIBROMATOSIS TYPE 1-ASSOCIATED PLEXIFORM NEUROFIBROMAS AND MALIGNANT PERIPHERAL NERVE SHEATH TUMORS

Neuro-Oncology ◽

10.1093/neuonc/noab196.508 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi128-vi129

Author(s):

Ina Ly ◽

Tianyu Liu ◽

Wenli Cai ◽

Daniel Kwon ◽

Olivia Michaels ◽

...

Keyword(s):

Peripheral Nerve ◽

Neurofibromatosis Type 1 ◽

Model Performance ◽

Neurofibromatosis Type ◽

Plexiform Neurofibromas ◽

Nerve Sheath Tumors ◽

Peripheral Nerve Sheath Tumors ◽

Nerve Sheath ◽

Mri Features

Abstract BACKGROUND Several MRI features are proposed to distinguish between plexiform neurofibromas (PNF) and malignant peripheral nerve sheath tumors (MPNST) in neurofibromatosis type 1 (NF1), including tumor size, margins, and degree of heterogeneity. However, most of these features are descriptive in nature, subject to intra-/interrater variability, and based on small single-institution studies. The goal of this study was to identify radiomic features that can differentiate between NF1-associated PNF and MPNST. METHODS 31 MPNSTs and 24 PNFs from five centers were segmented on short TI inversion recovery sequences using a semi-automated segmentation software (3DQI). Standard pre-processing was performed, including N4 bias field correction, intensity normalization (using a mean of 120 SI and standard deviation of 80 SI), and resampling to 1 mm3 voxel resolution. 1688 radiomic features were extracted from the tumor region of interest using PyRadiomics, an open-source Python radiomics package. To classify tumors as PNF or MPNST, we implemented the Boruta algorithm and correlation removal for selection of important features. A Random Forest model was built using the top ten selected features. Five-fold cross-validation was performed and repeated 100 times. Model performance was evaluated using the area under the ROC curve (AUC), sensitivity, specificity, accuracy, and confidence intervals. RESULTS The top ten features included in the model were five intensity features, two shape features, and three texture features. The model demonstrated an AUC of 0.891 (95% CI 0.882-0.899), sensitivity of 0.744, specificity of 0.847, and accuracy of 0.802 (95% CI 0.792-0.813). CONCLUSIONS Our machine learning model demonstrated high performance in classifying tumors as either PNF or MPNST in NF1 individuals. Inclusion of additional tumors for model training and testing on an independent dataset are underway. Ultimately, our model may enable improved differentiation between PNF and MPNST compared to descriptive MRI features, permit early patient risk stratification, and improve patient outcomes.

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