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• W4385285440 abstract "Abstract Purpose Application of machine learning in bone metastasis of prostate cancer based on inflammation and nutritional indicators. Methods Retrospective analysis the clinical data of patients with prostate cancer initially diagnosed in the Department of Urology of Gansu Provincial People's Hospital from June 2017 to June 2022. Logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) are used to jointly screened the model features. The filtered features are incorporated into algorithms including LR, random forest (RF), extreme gradient boosting (XGBoost), naive nayes (NB), k-nearest neighbor (KNN), and decision tree (DT), to develop prostate cancer bone metastasis models. Results A total of 404 patients were finally screened. Gleason score, T stage, N stage, PSA and ALP were used as features for modeling. The average AUC of the 5-fold cross-validation for each machine learning model in the training set is: LR (AUC = 0.9054), RF (AUC = 0.9032), NB (AUC = 0.8961), KNN (AUC = 0.8704), DT (AUC = 0.8526), XGBoost (AUC = 0.8066). The AUC of each machine learning model in the test set is KNN (AUC = 0.9390, 95%CI: 0.8760 ~ 1), RF (AUC = 0.9290, 95%CI: 0.8718 ~ 0.9861), NB (AUC = 0.9268, 95%CI: 0.8615 ~ 0.9920), LR (AUC = 0.9212, 95%CI: 0.8506 ~ 0.9917), XGBoost (AUC = 0.8292, 95%CI: 0.7442 ~ 0.9141), DT (AUC = 0.8057, 95%CI: 0.7100 ~ 0.9014). A comprehensive evaluation of the DeLong test among different models and each evaluation metric shows that KNN is the best machine learning model in the study. Conclusion A bone metastasis model of prostate cancer was established, and it was observed that indicators such as inflammation and nutrition had a weak correlation with bone metastasis." @default.
• W4385285440 created "2023-07-27" @default.
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• W4385285440 date "2023-07-26" @default.
• W4385285440 modified "2023-09-27" @default.
• W4385285440 title "Development and validation of a machine learning model for bone metastasis in prostate cancer: Based on inflammatory and nutritional indicators" @default.
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• W4385285440 doi "https://doi.org/10.21203/rs.3.rs-3193655/v1" @default.
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