Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.
人工智能（AI）通过提升非小细胞肺癌（NSCLC）管理的多个维度——包括分期、预后评估、治疗预测、疗效评价、复发/预后预测及个体化预后评估——实现了革命性突破。基于机器学习技术与深度影像数据分析的AI算法能够精准完成NSCLC分期，有望提升分期的精确性与效率，从而推动个体化治疗决策的制定。现有数据表明，通过整合临床、影像与分子数据，AI模型在预测生存率与疾病进展等预后指标方面具有潜在应用价值。本篇叙述性综述将系统分析相关初步研究，探讨AI算法如何预测患者对手术、放疗、化疗、靶向治疗及免疫治疗等多种治疗方式的应答。充分证据显示AI在预测术后肿瘤复发概率与失败模式方面同样发挥关键作用，这对制定个体化辅助治疗方案具有重要意义。 实现AI在个体化预后评估中的成功应用，需要整合临床、分子及影像等多源数据。机器学习与深度学习技术使AI模型能够综合分析这些数据并生成个体化预后预测，为患者提供精准的个体化诊疗方案。然而，当前仍需解决数据质量、模型可解释性及泛化能力等方面的挑战。临床医师、数据科学家与监管机构间的协同合作对于负责任地推进AI应用、最大化其在个体化预后评估中的效益至关重要。持续的研究、验证与合作对于充分挖掘AI在NSCLC管理中的潜力、改善患者预后具有决定性意义。本文系统综述了AI在肺癌分期预测、预后评估及治疗后复发模式判断等领域的前沿应用，旨在为读者提供这一前沿课题的全面概览。
肿瘤(癌症)患者之家