ArticleCancer imaging : the official publication of the International Cancer Imaging Society2023
An MRI-based radiomics nomogram for differentiating spinal metastases from multiple myeloma.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review.Cancers · 2026Review
- Four MRI-Based Radiomics Models for Diagnosis of Lumbar Intervertebral Disc Degeneration.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence reshaping the paradigm of hematologic malignancy diagnosis and treatment: From static assessment to dynamic precision management.Annals of hematology · 2026Review
- 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
- Preoperative differentiation of primary vs. metastatic lumbar spine tumors: development and external validation of a multiparametric MRI-based radiomics nomogram.Frontiers in oncology · 2026Article
- Multimodal MRI-based clinical radiomics model for predicting postoperative anemia in patients with spinal metastases.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 · 2025Article
- Applications and Performance of Artificial Intelligence in Spinal Metastasis Imaging: A Systematic Review.Journal of clinical medicine · 2025Review
- Improved localization and segmentation of spinal bone metastases in MRI with nnUNet radiomics.Journal of bone oncology · 2024Article
- Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma.American journal of nuclear medicine and molecular imaging · 2024Review
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6 authors.
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Abstract
backgroundSpinal metastasis and multiple myeloma share many overlapping conventional radiographic imaging characteristics, thus, their differentiation may be challenging. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the differentiation of spinal metastasis and multiple myeloma. MATERIALS AND
methodsA total of 312 patients (training set: n = 146, validation set: n = 65, our center; external test set: n = 101, two other centers) with spinal metastasis (n = 196) and multiple myeloma (n = 116) were retrospectively enrolled. Demographics and MRI findings were assessed to build a clinical factor model. Radiomics features were extracted from MRI images. A radiomics model was constructed by the least absolute shrinkage and selection operator method. A radiomics nomogram combining the radiomics signature and independent clinical factors was constructed. And, one experienced radiologist reviewed the MRI images for all case. The diagnostic performance of the different models was evaluated by receiver operating characteristic curves.
resultsA clinical factors model was built based on heterogeneous appearance and shape. Twenty-one features were used to build the radiomics signature. The area under the curve (AUC) values of the radiomics nomogram (0.853 and 0.762, respectively) were significantly higher than that of the clinical factor model (0.692 and 0.540, respectively) in both validation (p = 0.048) and external test (p < 0.001) sets. The AUC values of the radiomics nomogram model were higher than that of radiologist in training, validation and external test sets (all p < 0.05). Moreover, no significant difference in AUC values of radiomics nomogram model was found between the validation set and external test set (p = 0.212).
conclusionThe radiomics nomogram can differentiate spinal metastasis and multiple myeloma with a moderate to good performance, and may be as a valuable method to assist in the clinical diagnosis and preoperative decision-making.
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