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Estimation of Tumor Control Probability of Carbon Ion Radiotherapy Using Cancer Type-specific Sensitivity Data.

Eunhye Yu1, Takahiro Oike2,3, Makoto Sakai4

  • 1Department of Radiation Oncology, Gunma University Graduate School of Medicine, Maebashi, Japan.

Anticancer Research
|December 30, 2025
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Summary

Incorporating non-small cell lung cancer (NSCLC) specific data into carbon ion radiotherapy (CIRT) tumor control probability (TCP) models significantly improves prediction accuracy over standard models. This enhances the precision of CIRT for lung cancer patients.

Keywords:
Carbon ion radiotherapylinear-quadratic modelnon-small cell lung cancerrelative biological effectivenesstumor control probability

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Cancer Research

Background:

  • Carbon ion radiotherapy (CIRT) is an advanced cancer treatment.
  • Accurate prediction of tumor control probability (TCP) is crucial for effective CIRT planning.
  • Current TCP models often use generic radio sensitivity data, potentially limiting accuracy for specific cancer types like non-small cell lung cancer (NSCLC).

Purpose of the Study:

  • To determine if incorporating NSCLC-specific carbon ion sensitivity data enhances the predictive performance of CIRT TCP models.
  • To compare the accuracy of a NSCLC-specific TCP model against a model using conventional human salivary gland (HSG) cell line data.

Main Methods:

  • A linear-quadratic (LQ) formalism-based TCP model was utilized for fractionated irradiation.
  • NSCLC LQ model parameters (α and β) were derived from 13 NSCLC cell lines using clonogenic assays after carbon ion irradiation.
  • The derived NSCLC parameters and standard HSG parameters were incorporated into the TCP model and compared against clinical local control data (n=48 NSCLCs) using coefficient of determination (R²).

Main Results:

  • The NSCLC-derived TCP model demonstrated superior agreement with clinical data compared to the HSG-derived model across various heterogeneity index (σ) values (e.g., R² of 0.72 vs. 0.53 for σ=0.20).
  • Improved predictive performance was consistently observed for the NSCLC-specific model, even when weighting key data points.
  • The NSCLC-specific model showed higher R² values in all tested scenarios, indicating better prediction of local control rates.

Conclusions:

  • Integrating cancer type-specific carbon ion sensitivity data, such as from NSCLC cell lines, significantly improves TCP model predictive performance.
  • The findings support the use of tumor-specific radio sensitivity data for more accurate CIRT dose prescription and treatment planning.
  • This approach offers a more personalized and potentially more effective strategy for CIRT in NSCLC patients.