ArticleNeuro-oncology2022
Evaluation of magnetic resonance imaging-based radiomics characteristics for differentiation of benign and malignant peripheral nerve sheath tumors in neurofibromatosis type 1.
Article in Neuro-oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Surgical management of plexiform neurofibromas: An imaging-based perioperative framework.JPRAS open · 2026Review
- Artificial intelligence in neurofibromatosis type I: diagnostic and therapeutic opportunities.Journal of applied genetics · 2026Review
- Radiomics Applied to the Diagnosis of Peripheral Nerve Disorders: A Systematic Review and Meta-Analysis of the Existing Literature.Journal of clinical medicine · 2026Review
- Review
- Artificial intelligence-based tools for precision diagnosis and treatment of neurofibromatosis type 1 associated peripheral and central glial tumors.Orphanet journal of rare diseases · 2025Review
- The Role of Pre-Operative Biopsy in Malignant Peripheral Nerve Sheath Tumours: A Review and Retrospective Series with a Management Algorithm from a Single-Center Experience.Neurology international · 2025Article
- A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification.NPJ digital medicine · 2025Article
- Review
- Article
- Contemporary Approach to Neurofibromatosis Type 1-Associated Malignant Peripheral Nerve Sheath Tumors.American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting · 2024Review
- Article
- CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.Insights into imaging · 2024Article
- The current state of MRI-based radiomics in pituitary adenoma: promising but challenging.Frontiers in endocrinology · 2024Review
- Raman difference spectroscopy and U-Net convolutional neural network for molecular analysis of cutaneous neurofibroma.PloS one · 2024Article
- The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization.Cancers · 2022Review
- Discrimination of benign, atypical, and malignant peripheral nerve sheath tumors in neurofibromatosis type 1 using diffusion-weighted MRI.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients with neurofibromatosis type 1 (NF1) develop benign (BPNST), premalignant atypical (ANF), and malignant (MPNST) peripheral nerve sheath tumors. Radiological differentiation of these entities is challenging. Therefore, we aimed to evaluate the value of a magnetic resonance imaging (MRI)-based radiomics machine-learning (ML) classifier for differentiation of these three entities of internal peripheral nerve sheath tumors in NF1 patients.
methodsMRI was performed at 3T in 36 NF1 patients (20 male; age: 31 ± 11 years). Segmentation of 117 BPNSTs, 17 MPNSTs, and 8 ANFs was manually performed using T2w spectral attenuated inversion recovery sequences. One hundred seven features per lesion were extracted using PyRadiomics and applied for BPNST versus MPNST differentiation. A 5-feature radiomics signature was defined based on the most important features and tested for signature-based BPNST versus MPNST classification (random forest [RF] classification, leave-one-patient-out evaluation). In a second step, signature feature expressions for BPNSTs, ANFs, and MPNSTs were evaluated for radiomics-based classification for these three entities.
resultsThe mean area under the receiver operator characteristic curve (AUC) for the radiomics-based BPNST versus MPNST differentiation was 0.94, corresponding to correct classification of on average 16/17 MPNSTs and 114/117 BPNSTs (sensitivity: 94%, specificity: 97%). Exploratory analysis with the eight ANFs revealed intermediate radiomic feature characteristics in-between BPNST and MPNST tumor feature expression.
conclusionIn this proof-of-principle study, ML using MRI-based radiomics characteristics allows sensitive and specific differentiation of BPNSTs and MPNSTs in NF1 patients. Feature expression of premalignant atypical tumors was distributed in-between benign and malignant tumor feature expressions, which illustrates biological plausibility of the considered radiomics characteristics.
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