Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics.
Simple Summary: This study aims to improve the preoperative classification of nerve sheath tumors using radiomics, a method that extracts quantitative data from medical images. By analyzing MRI scans, we seek to develop a more accurate way to distinguish between different types of nerve sheath tumor...
| Publicado en: | Cancers Vol. 16; no. 11; pp. 2039 - 2053 |
|---|---|
| Autores principales: | , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
Jun2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177874117&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177874117 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Jun2024 vid: 16 iid: 11 pid: 97109 pub: MDPI artinfo: ui: 177874117 177874117 177874117 10.3390/cancers16112039 177874117 ppf: 2039 ppct: 14 formats: tig: atl: Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics. aug: au: Jansma, Christianne Y. M. N. Wan, Xinyi Acem, Ibtissam Spaanderman, Douwe J. Visser, Jacob J. Hanff, David Taal, Walter Verhoef, Cornelis Klein, Stefan Martin, Enrico Starmans, Martijn P. A. affil: Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands sug: subj: Nerve Sheath Tumors Classification Radiomics Utilization Magnetic Resonance Imaging Neurofibromatosis 1 Soft Tissue Neoplasms Radiography Nerve Sheath Tumors Radiography Human Male Female Adult Quantitative Studies Machine Learning Descriptive Statistics Image Processing, Computer Assisted Adult: 19-44 years Male Female ab: Simple Summary: This study aims to improve the preoperative classification of nerve sheath tumors using radiomics, a method that extracts quantitative data from medical images. By analyzing MRI scans, we seek to develop a more accurate way to distinguish between different types of nerve sheath tumors before surgery. Our findings could lead to better treatment planning and outcomes for patients with these tumors. This research has the potential to enhance the diagnostic process and contribute to more personalized care for individuals with nerve sheath tumors, ultimately benefiting the medical community and patients alike. 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|