Yuan Xiaohan, Ma Chaoqiong, Hu Mingzhe, Qiu Richard L J, Salari Elahheh, Martini Reema, Yang Xiaofeng
Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, USA.
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
J Appl Clin Med Phys. 2025 Jan;26(1):e14559. doi: 10.1002/acm2.14559. Epub 2024 Nov 18.
The integration of machine learning (ML) with radiotherapy has emerged as a pivotal innovation in outcome prediction, bringing novel insights amid unique challenges. This review comprehensively examines the current scope of ML applications in various treatment contexts, focusing on treatment outcomes such as patient survival, disease recurrence, and treatment-induced toxicity. It emphasizes the ascending trajectory of research efforts and the prominence of survival analysis as a clinical priority. We analyze the use of several common medical imaging modalities in conjunction with clinical data, highlighting the advantages and complexities inherent in this approach. The research reflects a commitment to advancing patient-centered care, advocating for expanded research on abdominal and pancreatic cancers. While data collection, patient privacy, standardization, and interpretability present significant challenges, leveraging ML in radiotherapy holds remarkable promise for elevating precision medicine and improving patient care outcomes.
机器学习（ML）与放射治疗的整合已成为结果预测中的一项关键创新，在独特的挑战中带来了新的见解。本综述全面审视了ML在各种治疗环境中的当前应用范围，重点关注患者生存、疾病复发和治疗引起的毒性等治疗结果。它强调了研究工作的上升趋势以及生存分析作为临床优先事项的突出地位。我们分析了几种常见医学成像模态与临床数据结合的使用情况，突出了这种方法固有的优势和复杂性。该研究体现了致力于推进以患者为中心的护理，倡导扩大对腹部癌和胰腺癌的研究。虽然数据收集、患者隐私、标准化和可解释性带来了重大挑战，但在放射治疗中利用ML对于提升精准医学和改善患者护理结果具有显著前景。
