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Finite Element Modelling of a Cellular Electric Microenvironment
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Realistic closed-form TCP model including cell sensitivity dependence.

Katerine Viviana Díaz Hernández1,2, Uwe Schneider1,2, Jürgen Besserer1

  • 1Medical Physics, Radiotherapy Hirslanden, Witellikerstrasse 40, Zürich CH-8032, Switzerland.

Physics in Medicine and Biology
|March 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new tumor control probability (TCP) model accounting for patient variations in tumor size and cell sensitivity. The model accurately predicts local control in non-small cell lung cancer (NSCLC) patients.

Keywords:
NSCLCanalytical modelheterogeneitylinear-quadratic

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Area of Science:

  • Radiation Oncology
  • Mathematical Modeling
  • Cancer Therapeutics

Background:

  • The standard linear quadratic (LQ) model for tumor control probability (TCP) has limitations in accounting for inter-patient variability.
  • Tumor volume and cell radiosensitivity differences significantly impact treatment outcomes in cohorts.
  • A more robust TCP model is needed for personalized and population-based cancer treatment strategies.

Purpose of the Study:

  • To develop a mechanistic extension of the LQ-TCP model incorporating tumor volume and cell sensitivity variations.
  • To introduce an exponential time dependence for local control (LC) within the TCP framework.
  • To validate the proposed population TCP model against clinical data from early-stage non-small cell lung cancer (NSCLC) patients.

Main Methods:

  • Derived a novel closed-form expression for population TCP from first principles.
  • Incorporated inter-individual variations in tumor volume and cell sensitivity into the LQ model.
  • Fitted the model to 22 NSCLC datasets using a log-likelihood algorithm, analyzing 1675 lesions.

Main Results:

  • The population TCP model with uniform distributions of tumor volume and cell radiosensitivity provided a significant fit (p < 0.05) for key radiobiological parameters.
  • Achieved a better fit compared to models using Gaussian or log-normal radiosensitivity distributions.
  • Estimated population parameters: mean cell sensitivity (α¯U), bandwidth (Δα), β, characteristic time (t1/2), and cell doubling time (Td).

Conclusions:

  • A novel closed-form population TCP model was successfully derived, accounting for cell sensitivity and tumor size heterogeneities.
  • Established a relationship between TCP and LC by modeling LC as an exponential function of follow-up time.
  • The derived model is directly applicable to clinical datasets, enabling individual TCP estimation from population radiobiological parameters.