Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer

Liu, Wencai; Li, Mingxuan; Qian, Wen-Xing; Luo, Zhiwen; Liao, Weijie; Liu, Zhi‐Li; Liu, Jiaming

doi:10.2147/cmar.s330591

Cited by 35 publications

(26 citation statements)

References 37 publications

(48 reference statements)

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“…Missing values were imputed by R mouse package using classification and regression tree principle ( 18 ). Meanwhile, to compare the importance of each feature, we extract the feature importance of each variable in the machine learning model according to the Permutation Importance principle ( 19 ).…”

Section: Methodsmentioning

confidence: 99%

Prediction of bone metastasis in non-small cell lung cancer based on machine learning

Liu

Sun

et al. 2023

Front. Oncol.

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ObjectiveThe purpose of this paper was to develop a machine learning algorithm with good performance in predicting bone metastasis (BM) in non-small cell lung cancer (NSCLC) and establish a simple web predictor based on the algorithm.MethodsPatients who diagnosed with NSCLC between 2010 and 2018 in the Surveillance, Epidemiology and End Results (SEER) database were involved. To increase the extensibility of the research, data of patients who first diagnosed with NSCLC at the First Affiliated Hospital of Nanchang University between January 2007 and December 2016 were also included in this study. Independent risk factors for BM in NSCLC were screened by univariate and multivariate logistic regression. At this basis, we chose six commonly machine learning algorithms to build predictive models, including Logistic Regression (LR), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Naive Bayes classifiers (NBC) and eXtreme gradient boosting (XGB). Then, the best model was identified to build the web-predictor for predicting BM of NSCLC patients. Finally, area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of these models.ResultsA total of 50581 NSCLC patients were included in this study, and 5087(10.06%) of them developed BM. The sex, grade, laterality, histology, T stage, N stage, and chemotherapy were independent risk factors for NSCLC. Of these six models, the machine learning model built by the XGB algorithm performed best in both internal and external data setting validation, with AUC scores of 0.808 and 0.841, respectively. Then, the XGB algorithm was used to build a web predictor of BM from NSCLC.ConclusionThis study developed a web predictor based XGB algorithm for predicting the risk of BM in NSCLC patients, which may assist doctors for clinical decision making

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Section: Methodsmentioning

confidence: 99%

Prediction of bone metastasis in non-small cell lung cancer based on machine learning

Liu

Sun

et al. 2023

Front. Oncol.

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“…Statistical and comprehensive reviews of ML in medical diagnosis by Bhavsar et al ( 35 , 39 ) suggested that ML techniques can help medical professionals reduce diagnostic errors, improve healthcare services, and cut treatment costs. And in the cancer metastases field, some clinical prediction models in predicting the risk of BM based on ML algorithms have been developed to assist clinicians in personalizing patient diagnosis ( 40 , 41 ).…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning Methods to Predict Bone Metastases in Breast Infiltrating Ductal Carcinoma Patients

Liu

et al. 2022

Front. Public Health

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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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“…Additionally, we applied the above algorithms to independent prognostic factors to build surviving models at the 3-year observation point. After testing the various performances of the above two types of models, we selected the most representative models as clinical recommendations (25,26).…”

Section: Discussionmentioning

confidence: 99%

Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning

Zhang

Huang

et al. 2022

Front. Public Health

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BackgroundBone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well.MethodsThe study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts.ResultsOur prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083–0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979–0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance.ConclusionOur developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.

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Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer

Cited by 35 publications

References 37 publications

Prediction of bone metastasis in non-small cell lung cancer based on machine learning

Prediction of bone metastasis in non-small cell lung cancer based on machine learning

Using Machine Learning Methods to Predict Bone Metastases in Breast Infiltrating Ductal Carcinoma Patients

Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning

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