Dataset Information

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*-weighted images of cervical spondylotic myelopathy.

ABSTRACT:

Introduction

Predicting the postoperative neurological function of cervical spondylotic myelopathy (CSM) patients is generally based on conventional magnetic resonance imaging (MRI) patterns, but this approach is not completely satisfactory. This study utilized radiomics, which produced advanced objective and quantitative indicators, and machine learning to develop, validate, test, and compare models for predicting the postoperative prognosis of CSM.

Materials and methods

In total, 151 CSM patients undergoing surgical treatment and preoperative MRI was retrospectively collected and divided into good/poor outcome groups based on postoperative modified Japanese Orthopedic Association (mJOA) scores. The datasets obtained from several scanners (an independent scanner) for the t

SUBMITTER: Zhang MZ

PROVIDER: S-EPMC8717093 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Dataset Information

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*-weighted images of cervical spondylotic myelopathy.

Introduction

Materials and methods

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T2*-weighted images of cervical spondylotic myelopathy.

Introduction

Materials and methods

OmicsDI is part of the ELIXIR infrastructure

Tweets

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*-weighted images of cervical spondylotic myelopathy.