Instituto de Física, Pontificia Universidad Católica de Chile, Santiago, Chile.
National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany.
PLoS One. 2018 Apr 26;13(4):e0196310. doi: 10.1371/journal.pone.0196310. eCollection 2018.
Motivated by the capabilities of modern radiotherapy techniques and by the recent developments of functional imaging techniques, dose painting by numbers (DPBN) was proposed to treat tumors with heterogeneous biological characteristics. This work studies different DPBN optimization techniques for virtual head and neck tumors assessing tumor response in terms of cell survival and tumor control probability with a previously published tumor response model (TRM). Uniform doses of 2 Gy are redistributed according to the microscopic oxygen distribution and the density distribution of tumor cells in four virtual tumors with different biological characteristics. In addition, two different optimization objective functions are investigated, which: i) minimize tumor cell survival (OFsurv) or; ii) maximize the homogeneity of the density of surviving tumor cells (OFstd). Several adaptive schemes, ranging from single to daily dose optimization, are studied and the treatment response is compared to that of the uniform dose. The results show that the benefit of DPBN treatments depends on the tumor reoxygenation capability, which strongly differed among the set of virtual tumors investigated. The difference between daily (fraction by fraction) and three weekly optimizations (at the beginning of weeks 1, 3 and 4) was found to be small, and higher benefit was observed for the treatments optimized using OFsurv. This in silico study corroborates the hypothesis that DPBN may be beneficial for treatments of tumors which show reoxygenation during treatment, and that a few optimizations may be sufficient to achieve this therapeutic benefit.
受现代放射治疗技术能力和最近功能成像技术发展的启发，提出了按数字划分剂量（DPBN）的方法来治疗具有异质生物学特征的肿瘤。这项工作研究了不同的 DPBN 优化技术，用于虚拟头颈部肿瘤，根据之前发表的肿瘤反应模型（TRM）评估细胞存活率和肿瘤控制概率的肿瘤反应。根据四个具有不同生物学特征的虚拟肿瘤中微观氧分布和肿瘤细胞密度分布，重新分配 2Gy 的均匀剂量。此外，研究了两种不同的优化目标函数，即：i）最小化肿瘤细胞存活率（OFsurv）或；ii）最大化存活肿瘤细胞密度的均匀性（OFstd）。研究了从单次到每日剂量优化的几种自适应方案，并将治疗反应与均匀剂量的治疗反应进行了比较。结果表明，DPBN 治疗的益处取决于肿瘤再氧合能力，而这在研究的一组虚拟肿瘤中差异很大。发现每日（分次）和每周三次优化（第 1、3 和 4 周开始时）之间的差异很小，使用 OFsurv 优化的治疗效果更高。这项计算机研究证实了这样一种假设，即 DPBN 可能有益于治疗在治疗过程中出现再氧合的肿瘤，并且可能只需要几次优化就可以实现这种治疗益处。
