Abstract：
In oncology, medical imaging is crucial for diagnosis, treatment planning and therapy execution. Treatment responses can be complex and varied and are known to involve factors of treatment, patient characteristics and tumor microenvironment. Longitudinal image analysis is able to track temporal changes, aiding in disease monitoring, treatment evaluation, and outcome prediction. This allows for the enhancement of personalized medicine. However, analyzing longitudinal 2D and 3D images presents unique challenges, including image registration, reliable segmentation, dealing with variable imaging intervals, and sparse data. This review presents an overview of techniques and methodologies in longitudinal image analysis, with a primary focus on outcome modeling in radiation oncology.
摘要：
在肿瘤学中，医学成像对诊断至关重要，治疗计划和治疗执行。治疗反应可能是复杂和多样的，并且已知涉及治疗因素，患者特征和肿瘤微环境。纵向图像分析能够跟踪时间变化，协助疾病监测，治疗评价，和结果预测。这允许个性化医疗的增强。然而,分析纵向二维和三维图像提出了独特的挑战，包括图像注册，可靠的分割，处理可变的成像间隔，和稀疏数据。这篇综述概述了纵向图像分析中的技术和方法，主要关注放射肿瘤学的结局建模。
