结果建模在个性化放疗中扮演着重要角色，广泛应用于自适应放疗等专业领域。基于简化的放射生物学影响或经验拟合的传统结果模型通常只考虑剂量信息。然而，人们认识到，对放疗的反应是多因素的，涉及放疗、患者和治疗因素以及肿瘤微环境的复杂相互作用。最近，随着先进生物技术和多模态成像技术的发展，大量患者特异性生物和成像数据已经变得可用。考虑到这种复杂性，人工智能（AI）和机器学习（ML）对于理解如此多样化的异质数据和协助临床医生的决策过程非常有价值。AI/ML的作用已被证明在许多回顾性研究中，并且最近出现了前瞻性证据来支持个性化和精密放疗中AI/ML的应用。版权所有© 2022 Elsevier Inc.。保留所有权利。

Outcome modeling plays an important role in personalizing radiotherapy and finds applications in specialized areas such as adaptive radiotherapy. Conventional outcome models that are based on a simplified understanding of radiobiological effects or empirical fitting often only consider dosimetric information. However, it is recognized that response to radiotherapy is multi-factorial and involves a complex interaction of radiation therapy, patient and treatment factors, and the tumor microenvironment. Recently, large pools of patient-specific biological and imaging data have become available with the development of advanced biotechnology and multi-modality imaging techniques. Given this complexity, artificial intelligence (AI) and machine learning (ML) are valuable to make sense of such a plethora of heterogeneous data and to aid clinicians in their decision-making process. The role of AI/ML has been demonstrated in many retrospective studies and more recently prospective evidence has been emerging as well to support AI/ML for personalized and precision radiotherapy.Copyright © 2022 Elsevier Inc. All rights reserved.