<p>Key similarities and differences.</p>

Cancer stem cells (CSCs) represent a rare but critical subpopulation within tumors, driving recurrence, resistance to therapy, and aggressive growth. To better understand CSC behavior in solid tumors, we developed a biologically constrained agent-based model (ABM) that simulates tumor progression initiated from a single CSC. The model incorporates essential microenvironmental factors—including oxygen diffusion, spatial limitations, stochastic migration, and cell cycle dynamics—allowing for high-resolution simulation of tumor development and intra tumoral heterogeneity. While this work does not aim to fully optimize therapy for clinical application, it provides a flexible, scalable simulation environment where adaptive treatment strategies can be tested. To extend a biological model toward intelligent treatment, we integrated a reinforcement learning (Q-learning) component that adaptively adjusts radiation dosage based on real-time CSC localization and microenvironmental feedback. This component is currently presented as a proof-of-concept to demonstrate feasibility, and its optimization and convergence analysis will be explored in future studies. Our results suggest that reinforcement learning, when integrated with a biologically grounded ABM, can guide adaptive and more personalized radiotherapy strategies.

癌症干细胞（Cancer stem cells, CSCs）是肿瘤中一类罕见却至关重要的亚群，可驱动肿瘤复发、治疗抵抗及侵袭性生长。为更深入解析实体瘤中CSCs的生物学行为，我们开发了一款受生物学约束的基于智能体的模型（agent-based model, ABM），该模型可模拟由单个CSC起始的肿瘤进展过程。该模型纳入了核心微环境因素——包括氧扩散、空间限制、随机迁移及细胞周期动力学，能够实现肿瘤发生发展与瘤内异质性的高分辨率仿真。本研究虽未旨在针对临床应用完全优化治疗方案，但提供了一个灵活可扩展的仿真环境，可用于测试适应性治疗策略。为将生物学模型拓展至智能治疗领域，我们集成了一个强化学习（reinforcement learning, Q-learning）模块，该模块可基于实时CSC定位与微环境反馈自适应调整辐射剂量。该模块目前以概念验证形式呈现，以证明其可行性，其优化与收敛性分析将在未来研究中展开。我们的研究结果表明，将强化学习与具备生物学基础的ABM相结合，可指导制定适应性更强、更具个性化的放射治疗策略。