ArticleBioMed research international2021
Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Performance of Artificial Intelligence in Detection of Primary Malignant Bone Tumors: a Meta-Analysis.Journal of imaging informatics in medicine · 2024Pooled it
- The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis.Frontiers in oncology · 2023Pooled it
- Integrating dielectric properties analysis and machine learning for accurate liver cancer identification and infiltration depth prediction.Physical and engineering sciences in medicine · 2026Article
- Artificial Intelligence and Machine Learning in Pediatric Endocrine Tumors: Opportunities, Pitfalls, and a Roadmap for Trustworthy Clinical Translation.Biomedicines · 2026Review
- Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review.Cancers · 2023Review
- Artificial intelligence applications in pediatric oncology diagnosis.Exploration of targeted anti-tumor therapy · 2023Review
- Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies-a scoping review.European radiology · 2022Article
- Article
- Study on Key Technologies of Virtual Interactive Surgical Simulation for 3D Reconstruction of Medical Images.BioMed research international · 2022Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients' clinical characteristics, and identify the most essential features for the classification of bone tumors. MATERIALS AND
methodsIn this retrospective study, 796 patients (benign bone tumors: 412 cases, malignant bone tumors: 215 cases, intermediate bone tumors: 169 cases) with pathologically confirmed bone tumors from Nanfang Hospital of Southern Medical University, Foshan Hospital of TCM, and University of Hong Kong-Shenzhen Hospital were enrolled. RF models were built to classify tumors as benign, malignant, or intermediate based on conventional radiographic features and potentially relevant clinical characteristics extracted by three musculoskeletal radiologists with ten years of experience. SHapley Additive exPlanations (SHAP) was used to identify the most essential features for the classification of bone tumors. The diagnostic performance of the RF models was quantified using receiver operating characteristic (ROC) curves.
resultsThe features extracted by the three radiologists had a satisfactory agreement and the minimum intraclass correlation coefficient (ICC) was 0.761 (CI: 0.686-0.824,
conclusionsThis study developed interpretable models to classify bone tumors with great performance. These should allow radiographers to identify imaging features that are important for the classification of bone tumors in the clinical setting.
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