ArticleCPT: pharmacometrics & systems pharmacology2021
Application of Machine Learning for Tumor Growth Inhibition - Overall Survival Modeling Platform.
Article in CPT: pharmacometrics & systems pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 27 citations in OpenAlex.
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- Contribution of machine learning to tumor growth inhibition modeling for hepatocellular carcinoma patients under Roblitinib (FGF401) drug treatment.CPT: pharmacometrics & systems pharmacology · 2022Trial
- Application of Machine Learning for Predicting Progression-Free and Overall Survival in Patients With Renal Cell Carcinoma.Clinical and translational science · 2025Article
- Development of a Joint Tumor Size-Overall Survival Modeling and Simulation Framework Supporting Oncology Development Decision-Making.CPT: pharmacometrics & systems pharmacology · 2025Article
- Predicting survival in prospective clinical trials using weakly-supervised QSP.NPJ precision oncology · 2025Article
- Prediction of STAS in lung adenocarcinoma with nodules ≤ 2 cm using machine learning: a multicenter retrospective study.BMC cancer · 2025Article
- Clinical Pharmacology of Cytokine Release Syndrome with T-Cell-Engaging Bispecific Antibodies: Current Insights and Drug Development Strategies.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Review
- Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development.CPT: pharmacometrics & systems pharmacology · 2024Review
- Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODE.NPJ systems biology and applications · 2023Article
- Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models.British journal of cancer · 2023Review
- Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy.CPT: pharmacometrics & systems pharmacology · 2023Article
- Machine learning-guided covariate selection for time-to-event models developed from a small sample of real-world patients receiving bevacizumab treatment.CPT: pharmacometrics & systems pharmacology · 2022Article
- Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations.Pharmaceutics · 2022Article
- Predicting disease activity in patients with multiple sclerosis: An explainable machine-learning approach in the Mavenclad trials.CPT: pharmacometrics & systems pharmacology · 2022Article
- Update to improve reproducibility and interpretability: A response to "Machine Learning for Tumor Growth Inhibition".CPT: pharmacometrics & systems pharmacology · 2022Article
- Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility.CPT: pharmacometrics & systems pharmacology · 2022Article
- Prediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition-overall survival modeling framework.CPT: pharmacometrics & systems pharmacology · 2021Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) was used to leverage tumor growth inhibition (TGI) metrics to characterize the relationship with overall survival (OS) as a novel approach and to compare with traditional TGI-OS modeling methods. Historical dataset from a phase III non-small cell lung cancer study (OAK, atezolizumab vs. docetaxel, N = 668) was used. ML methods support the validity of TGI metrics in predicting OS. With lasso, the best model with TGI metrics outperforms the best model without TGI metrics. Boosting was the best linear ML method for this dataset with reduced estimation bias and lowest Brier score, suggesting better prediction accuracy. Random forest did not outperform linear ML methods despite hyperparameter optimization. Kernel machine was marginally the best nonlinear ML method for this dataset and uncovered nonlinear and interaction effects. Nonlinear ML may improve prediction by capturing nonlinear effects and covariate interactions, but its predictive performance and value need further evaluation with larger datasets.
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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.