ArticleFrontiers in public health2022
Using Machine Learning Methods to Predict Bone Metastases in Breast Infiltrating Ductal Carcinoma Patients.
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 16 papers.
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Who cites it
16 citing papers in PubMed, 22 citations in OpenAlex.
- A Perspective on a Possible New Role for Bevacizumab Combined with Paclitaxel in Hormone-Resistant Metastatic Breast-Cancer Patients.Journal of clinical medicine · 2026Article
- Development of a Machine Learning Model for Distant Metastasis Risk Stratification in Acral Melanoma.Cancer reports (Hoboken, N.J.) · 2026Article
- A scoping review of web-based, interactive, personalized decision-making tools available to support breast cancer treatment and survivorship care.Journal of cancer survivorship : research and practice · 2025Article
- Leveraging object detection for early diagnosis of neurodegenerative diseases through radiomic analysis.Frontiers in aging neuroscience · 2025Article
- Machine learning-based prediction of distant metastasis risk in invasive ductal carcinoma of the breast.PloS one · 2025Article
- Bone scintigraphy based on deep learning model and modified growth optimizer.Scientific reports · 2024Article
- Development and validation of an artificial intelligence model for predicting de novo distant bone metastasis in breast cancer: a dual-center study.BMC women's health · 2024Article
- Tribulations and future opportunities for artificial intelligence in precision medicine.Journal of translational medicine · 2024Review
- Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data.Scientific reports · 2024Article
- Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer.Frontiers in oncology · 2024Article
- Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model.Cancer biomarkers : section A of Disease markers · 2024Article
- Prediction model of ocular metastasis from primary liver cancer: Machine learning-based development and interpretation study.Cancer medicine · 2023Article
- Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort.Biomolecules & biomedicine · 2023Article
- Cross-attention multi-branch CNN using DCE-MRI to classify breast cancer molecular subtypes.Frontiers in oncology · 2023Article
- Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study.International journal of clinical practice · 2023Article
- A practical dynamic nomogram model for predicting bone metastasis in patients with thyroid cancer.Frontiers in endocrinology · 2023Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer (BC) was the most common malignant tumor in women, and breast infiltrating ductal carcinoma (IDC) accounted for about 80% of all BC cases. BC patients who had bone metastases (BM) were more likely to have poor prognosis and bad quality of life, and earlier attention to patients at a high risk of BM was important. This study aimed to develop a predictive model based on machine learning to predict risk of BM in patients with IDC. Six different machine learning algorithms, including Logistic regression (LR), Naive Bayes classifiers (NBC), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), and Extreme gradient boosting (XGB), were used to build prediction models. The XGB model offered the best predictive performance among these 6 models in internal and external validation sets (AUC: 0.888, accuracy: 0.803, sensitivity: 0.801, and specificity: 0.837). Finally, an XGB model-based web predictor was developed to predict risk of BM in IDC patients, which may help physicians make personalized clinical decisions and treatment plans for IDC patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.