ArticleThe British journal of radiology2021
Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma.
Article in The British journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Integrating radiomics, artificial intelligence, and molecular signatures in bone and soft tissue tumors: advances in diagnosis and prognostication.Frontiers in oncology · 2025Pooled it
- The use of radiomic analysis of magnetic resonance imaging findings in predicting features of early osteoarthritis of the knee-a systematic review and meta-analysis.Irish journal of medical science · 2024Pooled it
- Who Is the Surgeon Now: Human Hands or Machine Minds? Artificial Intelligence in Orthopedics from Diagnosis to Follow-Up-A Structured Narrative Review.Journal of clinical medicine · 2026Review
- Development and verification of a comprehensive diagnostic model for osteosarcoma using random forest and artificial neural network.Discover oncology · 2026Article
- Data Augmentation and Synthetic Data Generation in Rare Disease Research: A Scoping Review.Medical sciences (Basel, Switzerland) · 2025Article
- Preoperative Multi-model Images-based Radiomics Model for Distinguishing Spinal Osteosarcoma and Chondrosarcoma.Current medical imaging · 2025Article
- Arterial phase CT radiomics for non-invasive prediction of Ki-67 proliferation index in pancreatic solid pseudopapillary neoplasms.Abdominal radiology (New York) · 2025Article
- Machine Learning based Radiomics from Multi-parametric Magnetic Resonance Imaging for Predicting Lymph Node Metastasis in Cervical Cancer.Current medical imaging · 2025Article
- Article
- Improved gated recurrent unit-based osteosarcoma prediction on histology images: a meta-heuristic-oriented optimization concept.Scientific reports · 2025Article
- Revolutionizing precision oncology: the role of artificial intelligence in personalized pediatric cancer care.Frontiers in medicine · 2025Review
- Magnetic Resonance Imaging Texture Analysis Based on Intraosseous and Extraosseous Lesions to Predict Prognosis in Patients with Osteosarcoma.Diagnostics (Basel, Switzerland) · 2024Article
- Nanoparticles and bone microenvironment: a comprehensive review for malignant bone tumor diagnosis and treatment.Molecular cancer · 2024Review
- Applications of Artificial Intelligence for Pediatric Cancer Imaging.AJR. American journal of roentgenology · 2024Review
- Predictors of pulmonary metastases on chest computed tomography in children and adolescents with osteosarcoma-tips for qualifying patients for thoracotomy.BMC pediatrics · 2024Article
- Machine Learning Supported the Modified Gustafson's Criteria for Dental Age Estimation in Southwest China.Journal of imaging informatics in medicine · 2024Article
- CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.Insights into imaging · 2024Article
- Radiomics analysis based on CT for the prediction of pulmonary metastases in ewing sarcoma.BMC medical imaging · 2023Article
- Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma.BMC medical imaging · 2023Article
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Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aims to build machine learning-based CT radiomic features to predict patients developing metastasis after osteosarcoma diagnosis. METHODS AND MATERIALS: This retrospective study has included 81 patients with a histopathological diagnosis of osteosarcoma. The entire dataset was divided randomly into training (60%) and test sets (40%). A data augmentation technique for the minority class was performed in the training set, along with feature's selection and model's training. The radiomic features were extracted from CT's image of the local osteosarcoma. Three frequently used machine learning models tried to predict patients with lung metastases (MT) and those without lung metastases (non-MT). According to the higher area under the curve (AUC), the best classifier was chosen and applied in the testing set with unseen data to provide an unbiased evaluation of the final model.
resultsThe best classifier for predicting MT and non-MT groups used a Random Forest algorithm. The AUC and accuracy results of the test set were bulky (accuracy of 73% [ 95% coefficient interval (CI): 54%; 87%] and AUC of 0.79 [95% CI: 0.62; 0.96]). Features that fitted the model (radiomics signature) derived from Laplacian of Gaussian and wavelet filters.
conclusionsMachine learning-based CT radiomics approach can provide a non-invasive method with a fair predictive accuracy of the risk of developing pulmonary metastasis in osteosarcoma patients. ADVANCES IN KNOWLEDGE: Models based on CT radiomic analysis help assess the risk of developing pulmonary metastases in patients with osteosarcoma, allowing further studies for those with a worse prognosis.
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