PDF(Pubmed)
Abstract：
Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments.
摘要：
非小细胞肺癌（NSCLC）是一种普遍且侵袭性的肺癌，转移性疾病预后不良。免疫疗法,特别是免疫检查点抑制剂（ICIs），彻底改变了NSCLC的管理，但是反应率是高度可变的。识别可靠的预测性生物标志物对于优化患者选择和治疗结果至关重要。本系统综述旨在评估人工智能（AI）和机器学习（ML）在预测NSCLC免疫治疗反应方面的应用现状。一项全面的文献检索确定了19项符合纳入标准的研究。这些研究采用了不同的AI/ML技术，包括深度学习，人工神经网络,支持向量机，和梯度增强方法，应用于各种数据模式，如医学成像，基因组数据，临床变量，和免疫组织化学标记。几项研究证明了AI/ML模型能够准确预测免疫治疗反应。无进展生存期,非小细胞肺癌患者的总生存期。然而,数据可用性仍然存在挑战，质量,以及这些模型的可解释性。已经努力开发可解释的AI/ML技术，但是需要进一步的研究来提高透明度和可解释性。此外,将AI/ML模型从研究环境转化为临床实践带来了与监管批准相关的挑战，数据隐私,并整合到现有的医疗保健系统中。尽管如此，AI/ML模型的成功实施可以实现个性化治疗策略，改善治疗结果，并减少与无效治疗相关的不必要的毒性和医疗费用。
