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Prediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition-overall survival modeling framework.预测接受阿特珠单抗治疗的各类实体瘤患者的总生存期：一种肿瘤生长抑制-总生存期建模框架。
CPT Pharmacometrics Syst Pharmacol. 2021 Oct;10(10):1171-1182. doi: 10.1002/psp4.12686. Epub 2021 Aug 4.
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Computing the Hazard Ratios Associated With Explanatory Variables Using Machine Learning Models of Survival Data.使用生存数据分析的机器学习模型计算与解释变量相关的风险比。
JCO Clin Cancer Inform. 2021 Mar;5:364-378. doi: 10.1200/CCI.20.00172.
4
Application of Machine Learning for Tumor Growth Inhibition - Overall Survival Modeling Platform.机器学习在肿瘤生长抑制-总生存期建模平台中的应用。
CPT Pharmacometrics Syst Pharmacol. 2021 Jan;10(1):59-66. doi: 10.1002/psp4.12576. Epub 2020 Dec 13.
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From Local Explanations to Global Understanding with Explainable AI for Trees.利用可解释人工智能实现从局部解释到树木的全局理解
Nat Mach Intell. 2020 Jan;2(1):56-67. doi: 10.1038/s42256-019-0138-9. Epub 2020 Jan 17.
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Data standards for model-informed drug development: an ISoP initiative.模型指导药物研发的数据标准：一项国际药物科学联合会倡议。
J Pharmacokinet Pharmacodyn. 2018 Oct;45(5):659-661. doi: 10.1007/s10928-018-9595-8. Epub 2018 Jul 25.
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A Model of Overall Survival Predicts Treatment Outcomes with Atezolizumab versus Chemotherapy in Non-Small Cell Lung Cancer Based on Early Tumor Kinetics.基于早期肿瘤动力学的阿特珠单抗对比化疗治疗非小细胞肺癌的总生存模型。
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Evaluation of tumor-size response metrics to predict overall survival in Western and Chinese patients with first-line metastatic colorectal cancer.评估肿瘤大小反应指标，以预测一线转移性结直肠癌中西方和中国患者的总生存期。
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Update to improve reproducibility and interpretability: A response to "Machine Learning for Tumor Growth Inhibition".

作者信息

Chan Phyllis, Lu James, Bruno René, Jin Jin Y

机构信息

Department of Clinical Pharmacology, Roche/Genentech, South San Francisco, California, USA.

Department of Clinical Pharmacology, Roche/Genentech, Marseille, France.

出版信息

CPT Pharmacometrics Syst Pharmacol. 2022 Mar;11(3):262-263. doi: 10.1002/psp4.12760. Epub 2022 Jan 31.

DOI:10.1002/psp4.12760
PMID:35102724
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8923728/
Abstract
摘要