免疫治疗作用下非小细胞肺癌的相场模型
A phase-field model for non-small cell lung cancer under the effects of immunotherapy
摘要：
制定预测治疗下生长的肿瘤模型对于改善患者特定的治疗计划至关重要。在这种情况下，我们提出了我们最近的工作模拟非小规模细胞肺癌（NSCLC）在一个简单的，确定性的设置为两个不同的患者接受免疫治疗。在其核心，我们的模型包括一个基于Cahn-Hilliard的相场模型描述的增殖和坏死的肿瘤细胞的演变。这些都是耦合到一个简化的营养模型，驱动增殖细胞的生长和它们的腐烂成坏死细胞。应用的免疫疗法降低了增殖细胞浓度。在这里，我们通过常微分方程（ODE）对整个肺中的免疫抑制剂浓度随时间的变化进行建模。最后，反应项提供了所有这些方程之间的耦合。通过假设球形、对称的肿瘤生长和恒定的营养流入，我们将这个完整的3D癌症模拟模型简化为简化的1D模型。然后，我们可以求助于几年来从计算机断层扫描（CT）扫描中收集的患者数据来校准我们的模型。对于简化的1D模型，我们证明了我们的模型可以通过将我们的模型参数拟合到现有的患者数据来定性地描述免疫治疗期间的观察结果。我们的模型涵盖了免疫治疗成功并限制肿瘤大小的病例，以及预测突然复发导致肿瘤指数生长的病例。最后，我们从降阶模型回到肺组织中的完整3D癌症模拟。因此，我们展示了包括空间信息在内的更详细的患者特异性模拟在未来可能产生的预测益处。
英文摘要：
Formulating tumor models that predict growth under therapy is vital for improving patient-specific treatment plans. In this context, we present our recent work on simulating non-small-scale cell lung cancer (NSCLC) in a simple, deterministic setting for two different patients receiving an immunotherapeutic treatment. At its core, our model consists of a Cahn-Hilliard-based phase-field model describing the evolution of proliferative and necrotic tumor cells. These are coupled to a simplified nutrient model that drives the growth of the proliferative cells and their decay into necrotic cells. The applied immunotherapy decreases the proliferative cell concentration. Here, we model the immunotherapeutic agent concentration in the entire lung over time by an ordinary differential equation (ODE). Finally, reaction terms provide a coupling between all these equations. By assuming spherical, symmetric tumor growth and constant nutrient inflow, we simplify this full 3D cancer simulation model to a reduced 1D model. We can then resort to patient data gathered from computed tomography (CT) scans over several years to calibrate our model. For the reduced 1D model, we show that our model can qualitatively describe observations during immunotherapy by fitting our model parameters to existing patient data. Our model covers cases in which the immunotherapy is successful and limits the tumor size, as well as cases predicting a sudden relapse, leading to exponential tumor growth. Finally, we move from the reduced model back to the full 3D cancer simulation in the lung tissue. Thereby, we show the predictive benefits a more detailed patient-specific simulation including spatial information could yield in the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
