ArticleInternational journal of radiation oncology, biology, physics2021
Forecasting Individual Patient Response to Radiation Therapy in Head and Neck Cancer With a Dynamic Carrying Capacity Model.
Article in International journal of radiation oncology, biology, physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed, 62 citations in OpenAlex.
- Effective tumor kinetics inferred from single routine H&E biopsies enable counterfactual virtual radiotherapy trials.medRxiv : the preprint server for health sciences · 2026Article
- Predicting head and neck cancer response to radiotherapy using mathematical modeling of MRI-based habitats.NPJ precision oncology · 2026Article
- Estimating the Growth Rate of a Birth and Death Process Using data From a Small Sample.Bulletin of mathematical biology · 2026Article
- Gompertz growth with a shared carrying capacity optimally simulates primary and metastatic tumor growth dynamics.British journal of cancer · 2026Article
- Simulating Cancer Recurrence Patterns From Post-Treatment Viable Tumor Burden Distributions.JCO clinical cancer informatics · 2026Article
- Measurable imaging-based changes in enhancement of intrahepatic cholangiocarcinoma after radiotherapy reflect physical mechanisms of response.NPJ systems biology and applications · 2025Article
- Radiation therapy response prediction for head and neck cancer using multimodal imaging and multiview dynamic graph autoencoder feature selection.Medical physics · 2025Article
- A data assimilation framework for predicting the spatiotemporal response of high-grade gliomas to chemoradiation.BMC cancer · 2025Article
- Quantitative Method for Monitoring Tumor Evolution During and After Therapy.Journal of personalized medicine · 2025Article
- Predicting and monitoring response to head and neck cancer radiotherapy using multimodality imaging and radiobiological digital twin simulations.Physics in medicine and biology · 2025Article
- Identifiability and model selection frameworks for models of high-grade glioma response to chemoradiation.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Validating the predictions of mathematical models describing tumor growth and treatment response.ArXiv · 2025Article
- A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin.Cancer biology & therapy · 2024Article
- Harnessing Flex Point Symmetry to Estimate Logistic Tumor Population Growth.Bulletin of mathematical biology · 2024Article
- Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making.Seminars in radiation oncology · 2024Review
- Practical parameter identifiability and handling of censored data with Bayesian inference in mathematical tumour models.NPJ systems biology and applications · 2024Article
- Multi-modality imaging parameters that predict rapid tumor regression in head and neck radiotherapy.Physics and imaging in radiation oncology · 2024Article
- To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy.Cell systems · 2024Article
- Designing clinical trials for patients who are not average.iScience · 2024Review
- Predicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling.Bulletin of mathematical biology · 2024Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo model and predict individual patient responses to radiation therapy. METHODS AND MATERIALS: We modeled tumor dynamics as logistic growth and the effect of radiation as a reduction in the tumor carrying capacity, motivated by the effect of radiation on the tumor microenvironment. The model was assessed on weekly tumor volume data collected for 2 independent cohorts of patients with head and neck cancer from the H. Lee Moffitt Cancer Center (MCC) and the MD Anderson Cancer Center (MDACC) who received 66 to 70 Gy in standard daily fractions or with accelerated fractionation. To predict response to radiation therapy for individual patients, we developed a new forecasting framework that combined the learned tumor growth rate and carrying capacity reduction fraction (δ) distribution with weekly measurements of tumor volume reduction for a given test patient to estimate δ, which was used to predict patient-specific outcomes.
resultsThe model fit data from MCC with high accuracy with patient-specific δ and a fixed tumor growth rate across all patients. The model fit data from an independent cohort from MDACC with comparable accuracy using the tumor growth rate learned from the MCC cohort, showing transferability of the growth rate. The forecasting framework predicted patient-specific outcomes with 76% sensitivity and 83% specificity for locoregional control and 68% sensitivity and 85% specificity for disease-free survival with the inclusion of 4 on-treatment tumor volume measurements.
conclusionsThese results demonstrate that our simple mathematical model can describe a variety of tumor volume dynamics. Furthermore, combining historically observed patient responses with a few patient-specific tumor volume measurements allowed for the accurate prediction of patient outcomes, which may inform treatment adaptation and personalization.
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