ArticleCancer management and research2021
Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer.
Article in Cancer management and research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction models in prostate cancer: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Diagnostic Performance of Prostate Cancer Disease-Specific Phenotypes Identified Using Real-World Databases: A Systematic Review.Pharmacoepidemiology and drug safety · 2025Pooled it
- Enhancing the positive predictive value of early-stage ovarian cancer detection using a two-step machine learning framework.Journal of translational medicine · 2026Article
- Predicting Prostate Cancer Risk and Its Associated Factors Using Machine Learning Techniques: A Retrospective Study.Health science reports · 2026Article
- Artificial Intelligence and Machine Learning in Bone Metastasis Management: A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- Development and validation of a machine learning-based early warning model for bone metastasis in newly diagnosed prostate cancer.Frontiers in oncology · 2026Article
- Prediction of bone or lung metastasis in prostate cancer using machine learning: a SEER-based study with external validation.Discover oncology · 2025Article
- Interpretable machine learning models for survival prediction in prostate cancer bone metastases.Scientific reports · 2025Article
- Artificial Intelligence in bone Metastases: A systematic review in guideline adherence of 92 studies.Journal of bone oncology · 2025Review
- Enhancing bone metastasis prediction in prostate cancer using quantitative mpMRI features, ISUP grade and PSA density: a machine learning approach.Abdominal radiology (New York) · 2025Article
- Predictive impact of T2-MRI radiomics model on initial diagnosis of bone metastasis in prostate cancer patients.BMC medical imaging · 2025Article
- Development of a predictive model for preoperative bone metastasis risk assessment in prostate cancer through integration of semi-quantitative single-photon emission computed tomography/computed tomography indices with peripheral blood biomarkers.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2025Article
- Machine learning-based prediction of distant metastasis risk in invasive ductal carcinoma of the breast.PloS one · 2025Article
- Artificial Intelligence and Radiomics Applied to Prostate Cancer Bone Metastasis Imaging: A Review.iRadiology · 2024Article
- Use machine learning to predict pulmonary metastasis of esophageal cancer: a population-based study.Journal of cancer research and clinical oncology · 2024Article
- Comprehensive diagnostic model for osteosarcoma classification using CT imaging features.Journal of bone oncology · 2024Article
- Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review.Cancers · 2024Review
- Association of volatile organic compound levels with chronic obstructive pulmonary diseases in NHANES 2013-2016.Scientific reports · 2024Article
- Prediction of hepatic metastasis in esophageal cancer based on machine learning.Scientific reports · 2024Article
- Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism.EClinicalMedicine · 2024Article
Corrections and comments
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to develop and validate a machine learning model for predicting bone metastases (BM) in prostate cancer (PCa) patients.
methodsDemographic and clinicopathologic variables of PCa patients in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2017 were retrospectively analyzed. We used six different machine learning algorithms, including Decision tree (DT), Random forest (RF), Multilayer Perceptron (MLP), Logistic regression (LR), Naive Bayes classifiers (NBC), and eXtreme gradient boosting (XGB), to build prediction models. External validation using data from 644 PCa patients of the First Affiliated Hospital of Nanchang University from 2010 to 2016. The performance of the models was evaluated using the area under receiver operating characteristic curve (AUC), accuracy score, sensitivity (recall rate) and specificity. A web predictor was developed based on the best performance model.
resultsA total of 207,137 PCa patients from SEER were included in this study. Of whom, 6725 (3.25%) developed BM. Gleason score, Prostate-specific antigen (PSA) value, T, N stage and age were found to be the risk factors of BM. The XGB model offered the best predictive performance among these 6 models (AUC: 0.962, accuracy: 0.884, sensitivity (recall rate): 0.906, and specificity: 0.879). An XGB model-based web predictor was developed to predict BM in PCa patients.
conclusionThis study developed a machine learning model and a web predictor for predicting the risk of BM in PCa patients, which may help physicians make personalized clinical decisions and treatment strategy for patients.
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