Colen Jonathan, Nguyen Cam, Liyanage Seth W, Aliotta Eric, Chen Joe, Alonso Clayton, Romano Kara, Peach Sean, Showalter Timothy, Read Paul, Larner James, Wijesooriya Krishni
University of Virginia, Department of Physics, Charlottesville, Virginia, USA.
Old Dominion University, Joint Institute on Advanced Computing for Environmental Studies, Norfolk, Virginia, USA.
Med Phys. 2024 Sep;51(9):6485-6500. doi: 10.1002/mp.17181. Epub 2024 Jun 4.
Stereotactic body radiation therapy (SBRT) is known to modulate the immune system and contribute to the generation of anti-tumor T cells and stimulate T cell infiltration into tumors. Radiation-induced immune suppression (RIIS) is a side effect of radiation therapy that can decrease immunological function by killing naive T cells as well as SBRT-induced newly created effector T cells, suppressing the immune response to tumors and increasing susceptibility to infections.
RIIS varies substantially among patients and it is currently unclear what drives this variability. Models that can accurately predict RIIS in near real time based on treatment plan characteristics would allow treatment planners to maintain current protocol specific dosimetric criteria while minimizing immune suppression. In this paper, we present an algorithm to predict RIIS based on a model of circulating blood using early stage lung cancer patients treated with SBRT.
This Python-based algorithm uses DICOM data for radiation therapy treatment plans, dose maps, patient CT data sets, and organ delineations to stochastically simulate blood flow and predict the doses absorbed by circulating lymphocytes. These absorbed doses are used to predict the fraction of lymphocytes killed by a given treatment plan. Finally, the time dependence of absolute lymphocyte count (ALC) following SBRT is modeled using longitudinal blood data up to a year after treatment. This model was developed and evaluated on a cohort of 64 patients with 10-fold cross validation.
Our algorithm predicted post-treatment ALC with an average error of cells/L with 89% of the patients having a prediction error below 0.5 × 10 cells/L. The accuracy was consistent across a wide range of clinical and treatment variables. Our model is able to predict post-treatment ALC < 0.8 (grade 2 lymphopenia), with a sensitivity of 81% and a specificity of 98%. This model has a ∼38-s end-to-end prediction time of post treatment ALC.
Our model performed well in predicting RIIS in patients treated using lung SBRT. With near-real time model prediction time, it has the capability to be interfaced with treatment planning systems to prospectively reduce immune cell toxicity while maintaining national SBRT conformity and plan quality criteria.
立体定向体部放射治疗（SBRT）已知可调节免疫系统，有助于产生抗肿瘤T细胞并刺激T细胞浸润肿瘤。辐射诱导的免疫抑制（RIIS）是放射治疗的一种副作用，可通过杀死幼稚T细胞以及SBRT诱导新产生的效应T细胞来降低免疫功能，抑制对肿瘤的免疫反应并增加感染易感性。
RIIS在患者之间差异很大，目前尚不清楚是什么导致了这种变异性。基于治疗计划特征能够近乎实时准确预测RIIS的模型将使治疗计划者能够维持当前方案特定的剂量学标准，同时将免疫抑制降至最低。在本文中，我们提出了一种基于循环血液模型的算法，用于预测接受SBRT治疗的早期肺癌患者的RIIS。
这种基于Python的算法使用用于放射治疗计划、剂量图、患者CT数据集和器官轮廓的DICOM数据，以随机模拟血流并预测循环淋巴细胞吸收的剂量。这些吸收剂量用于预测给定治疗计划杀死的淋巴细胞比例。最后，使用治疗后长达一年的纵向血液数据对SBRT后绝对淋巴细胞计数（ALC）的时间依赖性进行建模。该模型在64名患者的队列中开发并进行了10倍交叉验证评估。
我们的算法预测治疗后ALC的平均误差为 细胞/L，89%的患者预测误差低于0.5×10细胞/L。在广泛的临床和治疗变量范围内，准确性是一致的。我们的模型能够预测治疗后ALC < 0.8（2级淋巴细胞减少），敏感性为81%，特异性为98%。该模型预测治疗后ALC的端到端时间约为38秒。
我们的模型在预测使用肺部SBRT治疗的患者的RIIS方面表现良好。由于具有近乎实时的模型预测时间，它有能力与治疗计划系统对接，以前瞻性地降低免疫细胞毒性，同时维持国家SBRT的一致性和计划质量标准。
