ArticleNPJ systems biology and applications2023
Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODE.
Article in NPJ systems biology and applications, 2023. 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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Who cites it
17 citing papers in PubMed.
- Harnessing Quantitative Medicine to Advance Oncology Drug Development.Clinical and translational science · 2026Review
- The Empirical Bayes Variational Autoencoder-A Neural ODE Approach for Population Modeling in Pharmacology.CPT: pharmacometrics & systems pharmacology · 2026Article
- Real-World Evidence of Treatment Outcomes in Small Cell Lung Cancer: A Bayesian Mixed Effects and Competitive Risk Approach.JMIR cancer · 2026Article
- Follow-Up Bias in Tumor Dynamic Modeling: A Comparison of Classical and Neural-ODE Approaches.CPT: pharmacometrics & systems pharmacology · 2026Article
- The future of mathematical oncology in the age of AI.NPJ systems biology and applications · 2026Review
- Building Hybrid Pharmacometric-Machine Learning Models in Oncology Drug Development: Current State and Recommendations.CPT: pharmacometrics & systems pharmacology · 2026Review
- Neural Controlled Differential Equation and Its Application in Pharmacokinetics and Pharmacodynamics.CPT: pharmacometrics & systems pharmacology · 2026Article
- AI-Guided Inference of Morphodynamic Attractor-like States in Glioblastoma.Diagnostics (Basel, Switzerland) · 2026Article
- Non-invasive prediction of invasive lung adenocarcinoma and high-risk histopathological characteristics in resectable early-stage adenocarcinoma by [18F]FDG PET/CT radiomics-based machine learning models: a prospective cohort study.International journal of surgery (London, England) · 2026Article
- Application of Machine Learning for Predicting Progression-Free and Overall Survival in Patients With Renal Cell Carcinoma.Clinical and translational science · 2025Article
- AI-Driven Applications in Clinical Pharmacology and Translational Science: Insights From the ASCPT 2024 AI Preconference.Clinical and translational science · 2025Review
- Towards an interpretable deep learning model of cancer.NPJ precision oncology · 2025Review
- The data scientist as a mainstay of the tumor board: global implications and opportunities for the global south.Frontiers in digital health · 2025Article
- Integrating QSP and ML to Facilitate Drug Development and Personalized Medicine.Handbook of experimental pharmacology · 2025Review
- Bridging pharmacology and neural networks: A deep dive into neural ordinary differential equations.CPT: pharmacometrics & systems pharmacology · 2024Review
- Predicting progression-free survival from measurable residual disease in chronic lymphocytic leukemia.Clinical and translational science · 2024Article
- Predicting overall survival from tumor dynamics metrics using parametric statistical and machine learning models: application to patients withFrontiers in artificial intelligence · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic Neural-ODE (TDNODE) as a pharmacology-informed neural network to enable model discovery from longitudinal tumor size data. We show that TDNODE overcomes a key limitation of existing models in its ability to make unbiased predictions from truncated data. The encoder-decoder architecture is designed to express an underlying dynamical law that possesses the fundamental property of generalized homogeneity with respect to time. Thus, the modeling formalism enables the encoder output to be interpreted as kinetic rate metrics, with inverse time as the physical unit. We show that the generated metrics can be used to predict patients' overall survival (OS) with high accuracy. The proposed modeling formalism provides a principled way to integrate multimodal dynamical datasets in oncology disease modeling.
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