弥漫性低级别胶质瘤是一种侵袭性和无法治愈的脑肿瘤，不可避免地会转变为较高级别的肿瘤。延迟这一过渡的传统治疗方法是放射治疗(RT)。在放疗后，肿瘤通常在6个月到4年的时间内逐渐缩小后重新生长。为了改善患者的健康相关生活质量并帮助临床医生建立个性化的随访，我们需要预测肿瘤减小所需的时间。挑战在于在放疗后不久（即数据较少的情况下）提供可靠的肿瘤再生时间估计，尽管患者对治疗的反应不同。为此，我们从一批20个高质量的纵向数据中分析肿瘤大小动态，并提出了一个简单而强大的分析模型，只有4个参数。通过对它们之间的相关性的研究，我们建立了一个统计约束，帮助确定即使我们只有少量肿瘤大小测量的患者的再生时间。我们验证了该程序的数据，并预言了RT后第一次MRI时的再生时间，精度通常为6个月。使用虚拟患者，我们研究了在RT后仅仅三个月是否仍然有一些预测的可能性。我们在75％的病例中得出了可靠的再生时间估计，特别是在所有“快速反应者”中。其余25％则表示实际再生时间较长，并可以在一年后再进行另一次测量来安全地估算。这些结果表明，在RT之后不久即可以进行个性化肿瘤再生时间的预测。版权所有：©2023 Plaszczynski et al。本文是基于创作共用许可协议的开放获取文章，允许在任何媒介上自由使用、分发和再现，只要原作者和来源得到了公正的评价。

Diffuse low grade gliomas are invasive and incurable brain tumors that inevitably transform into higher grade ones. A classical treatment to delay this transition is radiotherapy (RT). Following RT, the tumor gradually shrinks during a period of typically 6 months to 4 years before regrowing. To improve the patient's health-related quality of life and help clinicians build personalized follow-ups, one would benefit from predictions of the time during which the tumor is expected to decrease. The challenge is to provide a reliable estimate of this regrowth time shortly after RT (i.e. with few data), although patients react differently to the treatment. To this end, we analyze the tumor size dynamics from a batch of 20 high-quality longitudinal data, and propose a simple and robust analytical model, with just 4 parameters. From the study of their correlations, we build a statistical constraint that helps determine the regrowth time even for patients for which we have only a few measurements of the tumor size. We validate the procedure on the data and predict the regrowth time at the moment of the first MRI after RT, with precision of, typically, 6 months. Using virtual patients, we study whether some forecast is still possible just three months after RT. We obtain some reliable estimates of the regrowth time in 75% of the cases, in particular for all "fast-responders". The remaining 25% represent cases where the actual regrowth time is large and can be safely estimated with another measurement a year later. These results show the feasibility of making personalized predictions of the tumor regrowth time shortly after RT.Copyright: © 2023 Plaszczynski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.