Tumor growth modeling based on cell and tumor lifespans

Area of Science:

• Radiotherapy
• Mathematical Modeling
• Oncology

Background:

• Radiotherapy efficacy relies on complex tumor and normal tissue responses.
• Accurate lifespan modeling is crucial for optimizing treatment plans.
• Current models may not fully capture tumor heterogeneity and treatment outcomes.

Purpose of the Study:

• To develop a bi-scale model for predicting heterogeneous tumor and cell lifespans under radiotherapy.
• To analyze the relationship between initial cancer cell number and mean tumor lifespan.
• To integrate tumor control probability (TCP) and normal tissue complication probability (NTCP) into a unified framework for treatment planning.

Main Methods:

• Proposed a bi-scale random variable model for cell and tumor lifespans.
• Derived first- and second-order moments, cumulative distribution functions, and confidence intervals.
• Investigated the functional relationship between initial cancer cell count and mean tumor lifespan.
• Developed an Efficiency-Complication Trade-off (ECT) curve based on ROC analysis.

Main Results:

• The mean tumor lifespan is logarithmically dependent on the initial number of cancer cells.
• TCP and NTCP can be derived from the tumor lifespan and healthy tissue response, respectively.
• The proposed ECT curve provides a tool for clinicians to select optimal radiotherapy treatment plans.

Conclusions:

• The bi-scale model offers a robust framework for radiotherapy lifespan modeling.
• The logarithmic relationship provides insights into tumor response dynamics.
• The ECT curve facilitates informed clinical decision-making in radiotherapy planning.