ArticleEuropean radiology2022
Vertebral MRI-based radiomics model to differentiate multiple myeloma from metastases: influence of features number on logistic regression model performance.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis.Frontiers in oncology · 2023Pooled it
- An interpretable biparametric MRI habitat radiomics model for predicting bone metastasis in prostate cancer: a dual-center study.Translational andrology and urology · 2026Article
- Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review.Cancers · 2026Review
- Multitask radioclinical decision stratification in non-metastatic colon cancer: integrating MMR status, pT staging, and high-risk pathological factors.Abdominal radiology (New York) · 2026Article
- Prediction of ISS and R-ISS Stratification in Newly Diagnosed Multiple Myeloma Using Lumbar Spine MRI Radiomics Model: A Two-Center Multimodal Study.Journal of imaging informatics in medicine · 2026Article
- Radiomics in spinal research: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Improving radiomics-based isocitrate dehydrogenase 1 prediction in glioma patients using semi-supervised machine learning models.BMC medical imaging · 2025Article
- Post-processing steps improve generalisability and robustness of an MRI-based radiogenomic model for human papillomavirus status prediction in oropharyngeal cancer.European radiology · 2025Article
- Computed Tomography-Based Radiomics Prediction of Biochemical Failure and Distant Metastasis in Patients With High- and Very High-Risk Localized Prostate Cancer.Advances in radiation oncology · 2025Article
- Applications and Performance of Artificial Intelligence in Spinal Metastasis Imaging: A Systematic Review.Journal of clinical medicine · 2025Review
- Development of a deep learning radiomics model combining lumbar CT, multi-sequence MRI, and clinical data to predict high-risk cage subsidence after lumbar fusion: a retrospective multicenter study.Biomedical engineering online · 2025Article
- Influencing factors analysis of asymmetry in knee adduction moment among patients with unilateral knee osteoarthritis.BMC musculoskeletal disorders · 2024Article
- PET radiomics in lung cancer: advances and translational challenges.EJNMMI physics · 2024Review
- How do AI markings on screening mammograms correspond to cancer location? An informed review of 270 breast cancer cases in BreastScreen Norway.European radiology · 2024Article
- Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.Cancers · 2024Review
- Radiomics model based on MRI to differentiate spinal multiple myeloma from metastases: A two-center study.Journal of bone oncology · 2024Article
- Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data.Insights into imaging · 2024Article
- Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma.American journal of nuclear medicine and molecular imaging · 2024Review
- Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics.Quantitative imaging in medicine and surgery · 2023Article
- Lumbar MR-based radiomics nomogram for detecting minimal residual disease in patients with multiple myeloma.European radiology · 2023Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
objectivesThis study aimed to use the most frequent features to establish a vertebral MRI-based radiomics model that could differentiate multiple myeloma (MM) from metastases and compare the model performance with different features number.
methodsWe retrospectively analyzed conventional MRI (T1WI and fat-suppression T2WI) of 103 MM patients and 138 patients with metastases. The feature selection process included four steps. The first three steps defined as conventional feature selection (CFS), carried out 50 times (ten times with 5-fold cross-validation), included variance threshold, SelectKBest, and least absolute shrinkage and selection operator. The most frequent fixed features were selected for modeling during the last step. The number of events per independent variable (EPV) is the number of patients in a smaller subgroup divided by the number of radiomics features considered in developing the prediction model. The EPV values considered were 5, 10, 15, and 20. Therefore, we constructed four models using the top 16, 8, 6, and 4 most frequent features, respectively. The models constructed with features selected by CFS were also compared.
resultsThe AUCs of 20EPV-Model, 15EPV-Model, and CSF-Model (AUC = 0.71, 0.81, and 0.78) were poor than 10EPV-Model (AUC = 0.84, p < 0.001). The AUC of 10EPV-Model was comparable with 5EPV-Model (AUC = 0.85, p = 0.480).
conclusionsThe radiomics model constructed with an appropriate small number of the most frequent features could well distinguish metastases from MM based on conventional vertebral MRI. Based on our results, we recommend following the 10 EPV as the rule of thumb for feature selection. KEY POINTS: • The developed radiomics model could distinguish metastases from multiple myeloma based on conventional vertebral MRI. • An accurate model based on just a handful of the most frequent features could be constructed by utilizing multiple feature reduction techniques. • An event per independent variable value of 10 is recommended as a rule of thumb for modeling feature selection.
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