Ramasamy Gemini, Muanza Thierry
Experimental Medicine, McGill University, Montreal, CAN.
Radiation Oncology, Sir Mortimer B. Davis Jewish General Hospital, Montreal, CAN.
Cureus. 2024 Nov 5;16(11):e73082. doi: 10.7759/cureus.73082. eCollection 2024 Nov.
Stereotactic body radiation therapy (SBRT) is currently the alternative for inoperable early-stage and oligometastatic non-small cell lung cancer (NSCLC) patients. While most patients are good responders among this specific group, some patients do not experience the benefits of this treatment. Even though physicians use clinical variables and semantic radiological features to make treatment decisions, medical images contain a wealth of personalized pathophysiological information that can be extracted and used for clinical decision support systems. In the form of radiomics features, details unique to each patient's medical scans can be utilized to create predictive models and to identify biomarking signatures. Then, these tools and indices can predict treatment outcomes and categorize patients to the most optimal treatment regimen. A conceptual review of relevant topics centered around the identification and development of radiomic-based biomarkers for SBRT-treated NSCLC was conducted. To begin with, an overview of the nature and management of non-small cell lung cancer was provided. To continue, biomarkers were defined in the context of cancer care. Then, the uses of stereotactic body radiation therapy in the treatment of NSCLC were further explained. Finally, the study of radiomics was discussed, and the uses and limitations of radiomic features and ML for SBRT-treated NSCLC were expanded upon. Radiomics-based biomarkers and predictive algorithmic models can potentially improve the SBRT treatment of early-stage and oligometastatic NSCLC by providing personalized support systems to healthcare professionals. While many institutions are attempting to optimize their biomarkers and AI-based tools for clinical use, additional prospective studies are needed to properly ensure their efficacy. As such, the improvements made in the field of personalized medicine are promising.
立体定向体部放射治疗（SBRT）目前是无法手术的早期和寡转移非小细胞肺癌（NSCLC）患者的替代治疗方法。虽然在这一特定群体中大多数患者对治疗反应良好，但有些患者并未从这种治疗中获益。尽管医生使用临床变量和语义放射学特征来做出治疗决策，但医学图像包含大量可提取并用于临床决策支持系统的个性化病理生理信息。以放射组学特征的形式，每个患者医学扫描特有的细节可用于创建预测模型并识别生物标志物特征。然后，这些工具和指标可以预测治疗结果并将患者分类到最优化的治疗方案中。本文对围绕SBRT治疗的NSCLC基于放射组学的生物标志物的识别和开发的相关主题进行了概念性综述。首先，提供了非小细胞肺癌的性质和管理概述。接着，在癌症治疗背景下定义了生物标志物。然后，进一步解释了立体定向体部放射治疗在NSCLC治疗中的应用。最后，讨论了放射组学研究，并扩展了放射组学特征和机器学习在SBRT治疗的NSCLC中的应用及局限性。基于放射组学的生物标志物和预测算法模型有可能通过为医疗保健专业人员提供个性化支持系统来改善早期和寡转移NSCLC的SBRT治疗。虽然许多机构正在尝试优化其生物标志物和基于人工智能的工具以供临床使用，但还需要更多前瞻性研究来适当确保其疗效。因此，个性化医疗领域取得的进展很有前景。
