ArticleFrontiers in immunology2022
A deep belief network-based clinical decision system for patients with osteosarcoma.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- 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
- Systematic review of 99 extremity bone malignancy survival prediction models.Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology · 2025Pooled it
- Application of Deep Learning in Cancer Prognosis Prediction Model.Technology in cancer research & treatmentPooled it
- Machine learning and logistic regression in estimating survival in patients with high-malignant deep-seated soft tissue sarcomas: development and analysis based on a population-based retrospective cohort.Acta orthopaedica · 2026Article
- Integrative biomarker and drug target discovery in osteosarcoma: traditional experimental approaches and AI-enabled insights.Frontiers in pharmacology · 2026Review
- HCA-DBN: a hill climbing optimized Deep Belief Network for crop yield classification based on kernel weight threshold.Frontiers in artificial intelligence · 2026Article
- Article
- Enhancing bone cancer detection through optimized pre trained deep learning models and explainable AI using the osteosarcoma tumor assessment dataset.Scientific reports · 2025Article
- Prediction of methotrexate neurotoxicity using clinical, sociodemographic, and area-based information in children with acute lymphoblastic leukemia.The oncologist · 2025Article
- Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes.Scientific reports · 2025Article
- Characterization of gallbladder stones associated with occult pancreaticobiliary reflux using computed tomography.World journal of gastroenterology · 2025Article
- Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study.JMIR medical informatics · 2024Article
- Evaluation of Community Involvement and Development in an Orthopedic Hospital.Healthcare (Basel, Switzerland) · 2024Article
- Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review).Oncology reports · 2024Review
- Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis.PloS one · 2024Article
- Article
- Deep Learning Approaches to Osteosarcoma Diagnosis and Classification: A Comparative Methodological Approach.Cancers · 2023Article
- Machine learning-based prediction of cerebral hemorrhage in patients with hemodialysis: A multicenter, retrospective study.Frontiers in neurology · 2023Article
- A practical dynamic nomogram model for predicting bone metastasis in patients with thyroid cancer.Frontiers in endocrinology · 2023Article
- A machine learning-based online web calculator to aid in the diagnosis of sarcopenia in the US community.Digital healthArticle
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Authors and funding
20 authors at 14 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteosarcoma was the most frequent type of malignant primary bone tumor with a poor survival rate mainly occurring in children and adolescents. For precision treatment, an accurate individualized prognosis for Osteosarcoma patients is highly desired. In recent years, many machine learning-based approaches have been used to predict distant metastasis and overall survival based on available individual information. In this study, we compared the performance of the deep belief networks (DBN) algorithm with six other machine learning algorithms, including Random Forest, XGBoost, Decision Tree, Gradient Boosting Machine, Logistic Regression, and Naive Bayes Classifier, to predict lung metastasis for Osteosarcoma patients. Therefore the DBN-based lung metastasis prediction model was integrated as a parameter into the Cox proportional hazards model to predict the overall survival of Osteosarcoma patients. The accuracy, precision, recall, and F1 score of the DBN algorithm were 0.917/0.888, 0.896/0.643, 0.956/0.900, and 0.925/0.750 in the training/validation sets, respectively, which were better than the other six machine-learning algorithms. For the performance of the DBN survival Cox model, the areas under the curve (AUCs) for the 1-, 3- and 5-year survival in the training set were 0.851, 0.806 and 0.793, respectively, indicating good discrimination, and the calibration curves showed good agreement between the prediction and actual observations. The DBN survival Cox model also demonstrated promising performance in the validation set. In addition, a nomogram integrating the DBN output was designed as a tool to aid clinical decision-making.
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