标题： TumorTwin：一种用于肿瘤学中患者特异性数字孪生的Python框架 显示英文标题

标题： TumorTwin: A python framework for patient-specific digital twins in oncology

摘要： 背景：计算肿瘤学理论和方法的进步使得能够准确地表征和预测基于患者个体的肿瘤生长和治疗反应。 这一能力可以集成到数字孪生框架中，在该框架中，物理肿瘤与数字肿瘤之间的双向数据流促进了动态模型重新校准、不确定性量化以及通过推荐最佳治疗干预措施来支持临床决策。 然而，许多数字孪生框架依赖于针对每个疾病部位、建模选择和算法实现量身定制的专属实现。 发现：我们介绍了TumorTwin，一个用于初始化、更新和利用基于患者个体的癌症肿瘤数字孪生的模块化软件框架。 TumorTwin作为Python包公开提供，并附带相关文档、数据集和教程。 新贡献包括开发了一种适用于不同疾病部位的患者数据结构、一种模块化架构以实现不同数据、模型、求解器和优化对象的组合，以及前向模型求解和梯度计算的CPU或GPU并行化实现。 我们通过高级胶质瘤生长和对放射疗法反应的计算机模拟数据集展示了TumorTwin的功能。 结论：TumorTwin框架使图像引导的肿瘤学数字孪生的快速原型设计和测试成为可能。 这允许研究人员在利用稳健的数值和计算基础设施的同时，系统地研究不同的模型、算法、疾病部位或治疗决策。 显示英文摘要

摘要： Background: Advances in the theory and methods of computational oncology have enabled accurate characterization and prediction of tumor growth and treatment response on a patient-specific basis. This capability can be integrated into a digital twin framework in which bi-directional data-flow between the physical tumor and the digital tumor facilitate dynamic model re-calibration, uncertainty quantification, and clinical decision-support via recommendation of optimal therapeutic interventions. However, many digital twin frameworks rely on bespoke implementations tailored to each disease site, modeling choice, and algorithmic implementation. Findings: We present TumorTwin, a modular software framework for initializing, updating, and leveraging patient-specific cancer tumor digital twins. TumorTwin is publicly available as a Python package, with associated documentation, datasets, and tutorials. Novel contributions include the development of a patient-data structure adaptable to different disease sites, a modular architecture to enable the composition of different data, model, solver, and optimization objects, and CPU- or GPU-parallelized implementations of forward model solves and gradient computations. We demonstrate the functionality of TumorTwin via an in silico dataset of high-grade glioma growth and response to radiation therapy. Conclusions: The TumorTwin framework enables rapid prototyping and testing of image-guided oncology digital twins. This allows researchers to systematically investigate different models, algorithms, disease sites, or treatment decisions while leveraging robust numerical and computational infrastructure.