Evaluation of magnetic resonance imaging-based radiomics characteristics for differentiation of benign and malignant peripheral nerve sheath tumors in neurofibromatosis type 1

• Published in: Neuro-oncology (Charlottesville, Va.) Va.), 2022-10, Vol.24 (10), p.1790-1798
• Main Authors: Ristow, Inka, Madesta, Frederic, Well, Lennart, Shenas, Farzad, Wright, Felicia, Molwitz, Isabel, Farschtschi, Said, Bannas, Peter, Adam, Gerhard, Mautner, Victor F, Werner, René, Salamon, Johannes
• Format: Article
• Language: English
• Subjects: Adult; Female; Humans; Magnetic Resonance Imaging - methods; Male; Nerve Sheath Neoplasms - diagnostic imaging; Nerve Sheath Neoplasms - pathology; Neurofibromatosis 1 - diagnostic imaging; Neurofibromatosis 1 - pathology; Neurofibrosarcoma; Neuroimaging; Young Adult
• ISSN: 1522-8517; 1523-5866
• DOI: 10.1093/neuonc/noac100

Abstract Background Patients 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. Methods MRI 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. Results The 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. Conclusion In 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.