ArticleJOR spine2021
Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T
Article in JOR spine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Prediction models for progression and outcomes in degenerative cervical myelopathy: a systematic review and critical appraisal.Frontiers in neurology · 2026Pooled it
- Machine learning-based MRI radiomics identifies patients with degenerative cervical myelopathy and predicts baseline function.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- An MRI-based radiomics model for precision subtyping of hereditary spastic paraplegia: discriminating SPG4 from SPG5.Neuroradiology · 2026Article
- Spinal Cord Radiomics-Driven Machine Learning Predicts Meaningful Clinical Improvement After Surgery for Degenerative Cervical Myelopathy: A Pilot Study.Journal of imaging informatics in medicine · 2026Article
- Cross species reproducibility of MRI radiomics features enables intervertebral disc degeneration assessment in experimental monkeys.Scientific reports · 2025Article
- A narrative review of the use of PROMs and machine learning to impact value-based clinical decision-making.BMC medical informatics and decision making · 2025Review
- Integration of MRI radiomics features and clinical data for predicting neurological recovery after thoracic spinal stenosis surgery: a machine learning model.Frontiers in medicine · 2025Article
- A severity classification model of cervical spondylotic radiculopathy symptoms based on MRI radiomics: A retrospective study.PloS one · 2025Article
- Evaluation of the structural integrity of different spinal cord tracts with magnetization transfer ratio in degenerative cervical myelopathy.Neuroradiology · 2024Article
- Advances in basic and preclinical spine research: Highlights from the Chinese Spine Research Community.JOR spine · 2021Article
- Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative TJOR spine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPredicting 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
methodsIn 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 training (testing) cohort were used for cross-validation (CV). Radiological models based on the intramedullary hyperintensity and compression ratio were constructed with 14 binary classifiers. Radiomic models based on 237 robust radiomic features were constructed with the same 14 binary classifiers in combination with 7 feature reduction methods, resulting in 98 models. The main outcome measures were the area under the receiver operating characteristic curve (AUROC) and accuracy.
resultsForty-one (11) radiomic models were superior to random guessing during CV (testing), with significant increased AUROC and/or accuracy (
conclusionRadiomic features can predict postoperative spinal cord function in CSM patients. The linear SVM preprocessor + SVM has great application potential in building radiomic models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.