ArticleFrontiers in public health2022
Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 17 citations in OpenAlex.
- Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review.Cancers · 2026Review
- A machine learning-based predictive model for lymph node metastasis in elderly patients with early gastric cancer and prognosis.Updates in surgery · 2026Article
- Cancer metastasisFrontiers in bioengineering and biotechnology · 2026Review
- Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit.BMC medical informatics and decision making · 2025Article
- Explainable machine learning for predicting lung metastasis of colorectal cancer.Scientific reports · 2025Article
- Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology.BMC gastroenterology · 2025Article
- Development and validation of a machine-learning model for the risk of potentially inappropriate medications in elderly stroke patients.Frontiers in pharmacology · 2025Article
- Bone scintigraphy based on deep learning model and modified growth optimizer.Scientific reports · 2024Article
- Development and validation of machine learning models for young-onset colorectal cancer risk stratification.NPJ precision oncology · 2024Article
- Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review.Cancers · 2024Review
- Exploring Potential Epigenetic Biomarkers for Colorectal Cancer Metastasis.International journal of molecular sciences · 2024Review
- Evaluating Inflammatory Bowel Disease-Related Quality of Life Using an Interpretable Machine Learning Approach: A Multicenter Study in China.Journal of inflammation research · 2024Article
- Must-have Qualities of Clinical Research on Artificial Intelligence and Machine LearningBalkan medical journal · 2023Review
- Individual risk and prognostic value prediction by machine learning for distant metastasis in pulmonary sarcomatoid carcinoma: a large cohort study based on the SEER database and the Chinese population.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to develop an artificial intelligence predictive model for predicting the probability of developing BM in CRC patients. Methods: From SEER database, 50,566 CRC patients were identified between January 2015 and December 2019 without missing data. SVM and LR models were trained and tested on the dataset. Accuracy, area under the curve (AUC), and IDI were used to evaluate and compare the models. Results: For bone metastases in the entire cohort, SVM model with poly as kernel function presents the best performance, whose accuracy is 0.908, recall is 0.838, and AUC is 0.926, outperforming LR model. The top three most important factors affecting the model's prediction of BM include extraosseous metastases (EM), CEA, and size. Conclusion: Our study developed an SVM model with poly as kernel function for predicting BM in CRC patients. SVM model could improve personalized clinical decision-making, help rationalize the bone metastasis screening process, and reduce the burden on healthcare systems and patients.
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