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Purpose <p>Applying new treatments to real patients to verify therapeutic efficacy may induce various risks, such as critical adverse events. Additionally, there are ethical and financial issues in real-world randomized controlled trials (RCTs). This study aimed to develop mathematical models of time-variant tumor growth trajectories (TGTs) for in silico RCTs targeting patients with stage I non-small cell lung cancer (NSCLC) to optimize stereotactic body radiation therapy (SBRT).</p> Methods <p>The basic idea of the in silico RCT was to evaluate the endpoint of progression-free survival (PFS) curves for the two regimens derived from TGTs for virtual patient data produced via mathematical models. TGT models with a relative number of tumor cells were proposed by integrating the Bertalanffy-Pütter (BP) model and linear quadratic model into tumor growth models. To validate the proposed models, we performed three RCTs, 30&#xa0;Gy/1 fraction (Fr) versus 60&#xa0;Gy/3 Fr, 48&#xa0;Gy/4 Fr versus 75&#xa0;Gy/25 Fr, and 34&#xa0;Gy/1 Fr versus 48&#xa0;Gy/4 Fr.</p> Results <p>The three in silico RCTs showed no statistically significant differences in PFS curves, which was similar to the results of three previous studies.</p> Conclusions <p>The proposed mathematical models could be leveraged for in silico RCTs to optimize SBRT.</p>

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Time-variant tumor growth trajectory models for in silico randomized controlled trials for patients with early-stage non-small cell lung cancer in optimizing stereotactic body radiation therapy

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Kazuki Mitsushima,
  • Hidetaka Arimura,
  • Yuko Shirakawa,
  • Takumi Kodama,
  • Tadamasa Yoshitake

摘要

Purpose

Applying new treatments to real patients to verify therapeutic efficacy may induce various risks, such as critical adverse events. Additionally, there are ethical and financial issues in real-world randomized controlled trials (RCTs). This study aimed to develop mathematical models of time-variant tumor growth trajectories (TGTs) for in silico RCTs targeting patients with stage I non-small cell lung cancer (NSCLC) to optimize stereotactic body radiation therapy (SBRT).

Methods

The basic idea of the in silico RCT was to evaluate the endpoint of progression-free survival (PFS) curves for the two regimens derived from TGTs for virtual patient data produced via mathematical models. TGT models with a relative number of tumor cells were proposed by integrating the Bertalanffy-Pütter (BP) model and linear quadratic model into tumor growth models. To validate the proposed models, we performed three RCTs, 30 Gy/1 fraction (Fr) versus 60 Gy/3 Fr, 48 Gy/4 Fr versus 75 Gy/25 Fr, and 34 Gy/1 Fr versus 48 Gy/4 Fr.

Results

The three in silico RCTs showed no statistically significant differences in PFS curves, which was similar to the results of three previous studies.

Conclusions

The proposed mathematical models could be leveraged for in silico RCTs to optimize SBRT.