Faculty of Electrical Engineering & Information Technology, University of Oradea, 410087 Oradea, Romania.
Faculty of Informatics & Science, University of Oradea, 410087 Oradea, Romania.
Int J Mol Sci. 2023 Jan 22;24(3):2214. doi: 10.3390/ijms24032214.
The latest developments in the management of head and neck cancer show an increasing trend in the implementation of novel approaches using artificial intelligence for better patient stratification and treatment-related risk evaluation. Radiomics, or the extraction of data from various imaging modalities, is a tool often used to evaluate specific features related to the tumour or normal tissue that are not identifiable by the naked eye and which can add value to existing clinical data. Furthermore, the assessment of feature variations from one time point to another based on subsequent images, known as delta radiomics, was shown to have even higher value for treatment-outcome prediction or patient stratification into risk categories. The information gathered from delta radiomics can, further, be used for decision making regarding treatment adaptation or other interventions found to be beneficial to the patient. The aim of this work is to collate the existing studies on delta radiomics in head and neck cancer and evaluate its role in tumour response and normal-tissue toxicity predictions alike. Moreover, this work also highlights the role of holomics, which brings under the same umbrella clinical and radiomic features, for a more complex patient characterization and treatment optimisation.
头颈部癌症管理的最新进展表明，在利用人工智能对患者进行分层和评估治疗相关风险方面，采用新方法的趋势日益增加。放射组学，即从各种成像方式中提取数据，是一种常用于评估与肿瘤或正常组织相关的特定特征的工具，这些特征用肉眼无法识别，但可以为现有临床数据增加价值。此外，基于后续图像对同一时间点的特征变化进行评估，称为“Delta 放射组学”，对于治疗结果预测或将患者分层到风险类别中具有更高的价值。从 Delta 放射组学中收集到的信息，还可以用于决策治疗适应或其他对患者有益的干预措施。这项工作的目的是整理头颈部癌症中 Delta 放射组学的现有研究，并评估其在肿瘤反应和正常组织毒性预测方面的作用。此外，这项工作还强调了 Holomics 的作用，它将临床和放射组学特征纳入同一个框架，以实现更复杂的患者特征描述和治疗优化。
