ReviewAnnals of biomedical engineering2016
Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success.
Review in Annals of biomedical engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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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.
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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
38 citing papers in PubMed.
- Computational reactive-diffusive modeling for stratification and prognosis determination of patients with breast cancer receiving Olaparib.Scientific reports · 2023Trial
- Experimentally calibrated multiscale model predicts schedule dependent drug combination effects.NPJ systems biology and applications · 2026Article
- Article
- Mathematical modeling in radiotherapy for cancer: a comprehensive narrative review.Radiation oncology (London, England) · 2025Review
- A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin.Journal of personalized medicine · 2024Article
- Theranostic digital twins: Concept, framework and roadmap towards personalized radiopharmaceutical therapies.Theranostics · 2024Review
- Comparing mechanism-based and machine learning models for predicting the effects of glucose accessibility on tumor cell proliferation.Scientific reports · 2023Article
- Assessing the identifiability of model selection frameworks for the prediction of patient outcomes in the clinical breast cancer setting.Journal of computational science · 2023Article
- Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis.PLoS computational biology · 2023Article
- Mathematical modeling of cancer immunotherapy for personalized clinical translation.Nature computational science · 2022Article
- Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth.Scientific reports · 2022Article
- Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling.Pharmaceutics · 2022Article
- Multiscale computational modeling of cancer growth using features derived from microCT images.Scientific reports · 2021Article
- Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data.Cancers · 2021Review
- Effects of Doxorubicin Delivery by Nitrogen-Doped Graphene Quantum Dots on Cancer Cell Growth: Experimental Study and Mathematical Modeling.Nanomaterials (Basel, Switzerland) · 2021Article
- Agent-Based Learning Model for the Obesity Paradox in RCC.Frontiers in bioengineering and biotechnology · 2021Article
- Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology.iScience · 2020Review
- Multi-scale models of lung fibrosis.Matrix biology : journal of the International Society for Matrix Biology · 2020Review
- A Coupled Mass Transport and Deformation Theory of Multi-constituent Tumor Growth.Journal of the mechanics and physics of solids · 2020Article
- Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities.Journal of clinical medicine · 2020Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Hierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncology. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrating their application as well as the current gap between pre-clinical and clinical applications. We conclude with a discussion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.
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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.