Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study.

ABSTRACT:

Background

Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications.

Objective

To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs.

Methods

We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features

SUBMITTER: Zhang M 

PROVIDER: S-EPMC8364819 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature