Quantitative image analysis, also known as radiomics, aims to analyze large-scale quantitative features extracted from acquired medical images using hand-crafted or machine-engineered feature extraction approaches. Radiomics has great potential for a variety of clinical applications in radiation oncology, an image-rich treatment modality that utilizes computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for treatment planning, dose calculation, and image guidance. A promising application of radiomics is in predicting treatment outcomes after radiotherapy such as local control and treatment-related toxicity using features extracted from pretreatment and on-treatment images. Based on these individualized predictions of treatment outcomes, radiotherapy dose can be sculpted to meet the specific needs and preferences of each patient. Radiomics can aid in tumor characterization for personalized targeting, especially for identifying high-risk regions within a tumor that cannot be easily discerned based on size or intensity alone. Radiomics-based treatment response prediction can aid in developing personalized fractionation and dose adjustments. In order to make radiomics models more applicable across different institutions with varying scanners and patient populations, further efforts are needed to harmonize and standardize the acquisition protocols by minimizing uncertainties within the imaging data.

定量图像分析，也称为放射组学，旨在分析使用手工或机器设计的特征提取方法从采集的医学图像中提取的大规模定量特征。放射组学在放射肿瘤学的各种临床应用中具有巨大潜力，放射肿瘤学是一种图像丰富的治疗方式，利用计算机断层扫描(CT)、磁共振成像 (MRI) 和正电子发射断层扫描(PET) 进行治疗计划、剂量计算和治疗。形象引导。放射组学的一个有前途的应用是预测放射治疗后的治疗结果例如使用从治疗前和治疗中图像中提取的特征进行局部控制和治疗相关的毒性。根据这些对治疗结果的个性化预测，可以调整放射治疗剂量，以满足每个患者的特定需求和偏好。放射组学可以帮助确定肿瘤特征，以实现个性化靶向，特别是识别肿瘤内仅根据大小或强度无法轻易识别的高风险区域。基于放射组学的治疗反应预测有助于制定个性化分割和剂量调整。为了使放射组学模型更适用于具有不同扫描仪和患者群体的不同机构，需要进一步努力通过最大限度地减少成像数据中的不确定性来协调和标准化采集协议。