[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122262-en":3,"doc-seo-122262-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122262,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Prediction of High-Dose Methotrexate Blood Concentration in Osteosarcoma Patients Using Machine Learning","High-dose methotrexate therapy for osteosarcoma faces a narrow therapeutic window, where insufficient drug exposure risks treatment failure and excessive exposure can trigger toxicity. This study builds interpretable machine-learning models to forecast blood methotrexate concentration early, before adverse effects become apparent. Clinical data from 68 osteosarcoma patients are used to train regression models with key baseline and laboratory features. Cross-validation and SHAP-based interpretation evaluate robustness and feature contributions, enabling proactive precision dosing decisions.","Drug Design, Development and Therapy downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nDrug Design, Development and Therapy  \n Open Access Full Text Article  \nORIGINAL RESEARCH  \nPrediction of High-Dose Methotrexate Blood Concentration in Osteosarcoma Patients Using Machine Learning  \nJin Zhao 1 , Shuqi Dai2 , Jiali He 1 , Na Liu 1 , Baowanze Zhang 1 , Su Li 1  \n1Department of Pharmacy, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning, 110042, People’s Republic of China; 2Department of Pharmacy, Qujing Medical College, Qujing, Yunnan, 655000, People’s Republic of China  \nCorrespondence: Su Li, Department of Pharmacy, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, No. 44, Xiaoheyan Road, Dadong District, Shenyang, Liaoning, 110042, People’s Republic of China, Tel +86-024-81916806, Email [lisu@cancerhosp-ln-cmu.com](lisu@cancerhosp-ln-cmu.com)  \n\n| Introduction: High-dose methotrexate is a typical chemotherapy that is widely used in the treatment of osteosarcoma. However, the unique dose-response relationship of methotrexate makes its treatment window relatively narrow, and its clinical use is in a dilemma: either the drug concentration in the patient’s body cannot reach the effective concentration level, or adverse reactions may occur due to drug overdose. For this circumstance, monitoring and predicting the drug concentration in the patient’s body is well founded and necessary. While pharmacokinetic models exist, they often oversimplify patient-specific covariates. This study addresses the unmet need for early-exposure prediction through interpretable machine learning, enabling data-driven decisions before toxicity manifestation.\u003Cbr>Methods: In this article, 68 osteosarcoma patients’ information including demography, administration and assay was gathered. We analyzed medical data and selected 10 important features using a random forest, including hydration status, red blood cell distribution width coefficient of variation, platelet distribution width, creatinine, γ-glutamyl transferase, large platelet ratio, serum potassium, lactate dehydrogenase, weight, and prealbumin. Then, cross-validation and SHAP has been conducted to confirm the robust and interpretation of the model.\u003Cbr>Results: On this basis, 7 machine learning regression models was built to predict the blood concentration of methotrexate. R2, MSE, RMSE, MAE are the evaluation metrics. Finally, LightGBM was selected as the best prediction model with a performance of R2=0 . 87, MSE=0 .020, RMSE=0 . 141, MAE=0 .065.\u003Cbr>Discussion: This machine learning framework addresses a critical gap in high-dose methotrexate therapeutic monitoring by achieving early and personalized blood drug concentration prediction, allowing for personalized dosing of patients based on predicted concentrations. The interpretability of SHAP-derived feature importance enhances clinical utility, offering a paradigm shift from reactive toxicity management to proactive precision dosing in osteosarcoma therapy.\u003Cbr>Keywords: high-dose methotrexate, osteosarcoma, machine learning, blood concentration prediction |\n| --- |\n| Introduction\u003Cbr>Osteosarcoma is the most prevalent primary malignant bone tumor in children and adolescents, accounting for approximately 5% of all childhood cancers.1 It typically manifests in the adolescent decade of life, with a peak incidence between the ages of 15 and 19.1 The tumor primarily arises in the metaphysis of long bones, most ordinarily affecting the distal femur, proximal tibia, and humerus.2 The aggressive nature of osteosarcoma is attributed to its high proliferation rate and ability to metastasize early, often to the lungs.3,4 Existing studies have found that age and race have an impact on the incidence rate and survival period of osteosarcoma.5,6 The pathophysiology of osteosarcoma involves complex interactions between genetic, ","cbCaiertJAVrE4IN","https://ap.wps.com/l/cbCaiertJAVrE4IN","pdf",2826689,1,13,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion\n# Keywords","[{\"question\":\"Why is predicting high-dose methotrexate blood concentration important for osteosarcoma patients?\",\"answer\":\"Methotrexate has a narrow dose-response window, so concentrations must be high enough for efficacy while avoiding overdose-related adverse reactions.\"},{\"question\":\"What data and features are used to build the prediction model?\",\"answer\":\"The model is trained using information from 68 osteosarcoma patients, selecting 10 important features that include hydration status and several laboratory and clinical variables.\"},{\"question\":\"Which machine learning approach performed best and how was it evaluated?\",\"answer\":\"LightGBM achieved the best prediction performance, assessed using metrics such as R2, MSE, RMSE, and MAE, with cross-validation used to confirm robustness.\"}]","Prediction of High-Dose Methotrexate Blood Concentration in Osteosarcoma Patients Using Machine Learning | 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is predicting high-dose methotrexate blood concentration important for osteosarcoma patients?","Question",{"text":75,"@type":76},"Methotrexate has a narrow dose-response window, so concentrations must be high enough for efficacy while avoiding overdose-related adverse reactions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and features are used to build the prediction model?",{"text":80,"@type":76},"The model is trained using information from 68 osteosarcoma patients, selecting 10 important features that include hydration status and several laboratory and clinical variables.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approach performed best and how was it evaluated?",{"text":84,"@type":76},"LightGBM achieved the best prediction performance, assessed using metrics such as R2, MSE, RMSE, and MAE, with cross-validation used to confirm 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