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Updated: Jul 15, 2025
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Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
Published on: June 7, 2015
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Treatment of evolving cancers will require dynamic decision support.
M A R Strobl1, J Gallaher2, M Robertson-Tessi2
1Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa; Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, USA.
Summary
Personalizing cancer drug dosing schedules is crucial for effective treatment. The proposed Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT) paradigm uses mathematical modeling to tailor treatment to individual tumorscapes, moving beyond one-size-fits-all approaches.
Area of Science:
- Oncology
- Pharmacology
- Mathematical Biology
Background:
- Traditional cancer drug administration relies on fixed, maximum tolerated dose schedules.
- This approach often fails to account for cancer's complexity, heterogeneity, and evolution.
- Dynamic and personalized treatment strategies are needed to improve outcomes.
Purpose of the Study:
- To highlight the importance of optimizing drug dose and frequency in cancer therapy.
- To propose a novel framework for personalized cancer treatment scheduling.
- To advocate for the integration of mathematical modeling in clinical practice.
Main Methods:
- Reviewing historical milestones in cancer chemotherapy scheduling.
- Introducing the five-step Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT) paradigm.
- Emphasizing the role of mathematical modeling in deriving personalized schedules.
Main Results:
- Treatment response and failure are significantly influenced by drug scheduling.
- Cancer heterogeneity and evolution necessitate individualized treatment strategies.
- Model-guided personalization offers a promising avenue for optimizing therapy.
Conclusions:
- A one-size-fits-all approach to cancer drug scheduling is inadequate.
- The ADAPT paradigm provides a roadmap for dynamic, personalized treatment.
- Further research and collaboration are needed to address challenges in data collection and model integration for clinical application.
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