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The Evidence for Evolution02:55

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Genetic variations accumulating within populations over generations give rise to biological evolution. Evolutionary changes can result in the formation of novel varieties and entire new species. These changes are responsible for the diverse forms of life inhabiting the planet. The evidence for evolution suggests that all living organisms descended from common ancestors.
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Related Experiment Video

Updated: Feb 12, 2026

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Multi-Scale Modeling and Oxygen Impact on Tumor Temporal Evolution: Application on Rectal Cancer During Radiotherapy.

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    IEEE Transactions on Medical Imaging
    |April 4, 2018
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    Summary
    This summary is machine-generated.

    This study introduces a multi-scale tumor modeling approach to predict radiotherapy outcomes. The model integrates microscopic, mesoscopic, and macroscopic scales to simulate tumor evolution, showing promise for personalized cancer treatment.

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    Area of Science:

    • Computational Biology
    • Medical Physics
    • Oncology

    Background:

    • Tumor evolution during radiotherapy is complex, involving cellular and macroscopic changes.
    • Existing models often struggle to bridge the gap between cellular mechanisms and whole-tumor behavior.

    Purpose of the Study:

    • To develop and validate a multi-scale tumor modeling framework for predicting radiotherapy response.
    • To integrate microscopic, mesoscopic, and macroscopic scales for comprehensive tumor simulation.
    • To assess the impact of oxygen on tumor evolution under radiotherapy using the multi-scale model.

    Main Methods:

    • A multi-scale modeling approach was developed, incorporating cellular (microscopic), population (mesoscopic), and image-based (macroscopic) levels.
    • Phase transfer probabilities in the cell cycle described microscopic behavior.
    • FDG PET image voxels represented the macroscopic tumor scale.
    • The model was applied to simulate radiotherapy effects on 17 rectal cancer patients, using serial FDG PET images.

    Main Results:

    • The multi-scale model successfully simulated tumor evolution during radiotherapy.
    • Simulated results were compared against clinical data from FDG PET scans.
    • The model provides a framework to investigate factors like oxygenation's role in treatment response.

    Conclusions:

    • The proposed multi-scale tumor modeling approach effectively predicts tumor evolution during radiotherapy.
    • Integrating different scales enhances simulation accuracy and reduces computational load.
    • This framework holds potential for optimizing radiotherapy strategies and understanding tumor dynamics.