Epidemiology of Peripheral Nerve Sheath Tumors

Jack, Andrew; Huie, Charlotte J.; Jacques, Line

doi:10.1007/978-3-030-77633-6_1

Cited by 2 publications

(1 citation statement)

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“…Peripheral nerve sheath tumors (PNSTs) are soft-tissue tumors that are associated with a number of peripheral nerves, encompassing a variety of both benign and malignant tumors [ 1 ]. Among benign peripheral nerve sheath tumors (BPNSTs), schwannomas are the most prevalent type (up to 80%), with neurofibromas constituting a significant portion of the remaining BPNSTs (10–24%) [ 2 , 3 ]. Malignant peripheral nerve sheath tumors (MPNSTs) are rare, accounting for 2% of all soft-tissue sarcoma (STS) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics

Jansma,

Wan,

Acem

et al. 2024

Cancers

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Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft-tissue tumors prevalent in neurofibromatosis type 1 (NF1) patients, posing a significant risk of metastasis and recurrence. Current magnetic resonance imaging (MRI) imaging lacks decisiveness in distinguishing benign peripheral nerve sheath tumors (BPNSTs) and MPNSTs, necessitating invasive biopsies. This study aims to develop a radiomics model using quantitative imaging features and machine learning to distinguish MPNSTs from BPNSTs. Clinical data and MRIs from MPNST and BPNST patients (2000–2019) were collected at a tertiary sarcoma referral center. Lesions were manually and semi-automatically segmented on MRI scans, and radiomics features were extracted using the Workflow for Optimal Radiomics Classification (WORC) algorithm, employing automated machine learning. The evaluation was conducted using a 100× random-split cross-validation. A total of 35 MPNSTs and 74 BPNSTs were included. The T1-weighted (T1w) MRI radiomics model outperformed others with an area under the curve (AUC) of 0.71. The incorporation of additional MRI scans did not enhance performance. Combining T1w MRI with clinical features achieved an AUC of 0.74. Experienced radiologists achieved AUCs of 0.75 and 0.66, respectively. Radiomics based on T1w MRI scans and clinical features show some ability to distinguish MPNSTs from BPNSTs, potentially aiding in the management of these tumors.

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Section: Introductionmentioning

confidence: 99%