ArticleInsights into imaging2022
Benign and malignant diagnosis of spinal tumors based on deep learning and weighted fusion framework on MRI.
Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).BMC medical informatics and decision making · 2026Pooled it
- Diagnostic Performance of Artificial Intelligence in Detection of Primary Malignant Bone Tumors: a Meta-Analysis.Journal of imaging informatics in medicine · 2024Pooled it
- Clinical applications of MRI-based artificial intelligence in spinal metastases: A systematic review.Journal of bone oncology · 2026Review
- Deep learning-based lightweight model for automated lumbar foraminal stenosis classification: sagittal CT diagnostic performance compared to clinical subspecialists.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
- Unmasking benign intradural-extramedullary spinal tumors: a systematic review on treatment outcomes and radiological features.Annals of medicine and surgery (2012) · 2025Article
- Multimodal deep learning for bone tumor diagnosis with clinical imaging, pathology, and blood biomarkers.Journal of bone oncology · 2025Article
- End-to-end deep learning for the diagnosis of pelvic and sacral tumors using non-enhanced MRI: a multi-center study.NPJ precision oncology · 2025Article
- Deep Learning-Based Differentiation of Vertebral Body Lesions on Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- The Application of Artificial Intelligence in Spine Surgery: A Scoping Review.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2025Article
- Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- An intelligent MRI data fusion framework for optimized diagnosis of spinal tumors.Frontiers in medicine · 2025Article
- Astrocytomas of the spinal cord.Neuro-oncology advances · 2024Review
- Demonstrating the successful application of synthetic learning in spine surgery for training multi-center models with increased patient privacy.Scientific reports · 2023Article
- Understanding a mass in the paraspinal region: an anatomical approach.Insights into imaging · 2023Review
- Current Applications of Machine Learning for Spinal Cord Tumors.Life (Basel, Switzerland) · 2023Review
- Accurate Differentiation of Spinal Tuberculosis and Spinal Metastases Using MR-Based Deep Learning Algorithms.Infection and drug resistance · 2023Article
- Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT.Frontiers in oncology · 2023Article
- Surgical approaches to intramedullary spinal cord astrocytomas in the age of genomics.Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundThe application of deep learning has allowed significant progress in medical imaging. However, few studies have focused on the diagnosis of benign and malignant spinal tumors using medical imaging and age information at the patient level. This study proposes a multi-model weighted fusion framework (WFF) for benign and malignant diagnosis of spinal tumors based on magnetic resonance imaging (MRI) images and age information.
methodsThe proposed WFF included a tumor detection model, sequence classification model, and age information statistic module based on sagittal MRI sequences obtained from 585 patients with spinal tumors (270 benign, 315 malignant) between January 2006 and December 2019 from the cooperative hospital. The experimental results of the WFF were compared with those of one radiologist (D1) and two spine surgeons (D2 and D3).
resultsIn the case of reference age information, the accuracy (ACC) (0.821) of WFF was higher than three doctors' ACC (D1: 0.686; D2: 0.736; D3: 0.636). Without age information, the ACC (0.800) of the WFF was also higher than that of the three doctors (D1: 0.750; D2: 0.664; D3:0.614).
conclusionsThe proposed WFF is effective in the diagnosis of benign and malignant spinal tumors with complex histological types on MRI.
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