Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics

Author:

Jansma Christianne Y. M. N.12ORCID,Wan Xinyi3,Acem Ibtissam1ORCID,Spaanderman Douwe J.3,Visser Jacob J.3ORCID,Hanff David3ORCID,Taal Walter4ORCID,Verhoef Cornelis1ORCID,Klein Stefan3ORCID,Martin Enrico2,Starmans Martijn P. A.35ORCID

Affiliation:

1. Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands

2. Department of Plastic and Reconstructive Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands

3. Department of Radiology & Nuclear Medicine, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands

4. Department of Neurology, Erasmus MC Cancer Institute University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands

5. Department of Pathology, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands

Abstract

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.

Publisher

MDPI AG

Reference50 articles.

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2. A Bayesian Approach for Diagnostic Accuracy of Malignant Peripheral Nerve Sheath Tumors: A Systematic Review and Meta-Analysis;Martin;Neuro Oncol.,2021

3. Jack, A.S., Huie, C.J., and Jacques, L.G. (2021). Diagnostic Assessment and Treatment of Peripheral Nerve Tumors, Springer.

4. Incidence and Survival in Sarcoma in the United States: A Focus on Musculoskeletal Lesions;Ng;Anticancer Res.,2013

5. Clinical, Pathological, and Molecular Variables Predictive of Malignant Peripheral Nerve Sheath Tumor Outcome;Zou;Ann. Surg.,2009

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