Background/Objectives: Lung cancer recurrence, particularly in NSCLC, remains a major challenge, with 30–70% of patients relapsing post-treatment. Traditional predictors like TNM staging and histopathology fail to account for tumor heterogeneity and immune dynamics. This review evaluates AI models integrating gene biomarkers (TP53, KRAS, FOXP3, PD-L1, and CD8) to enhance the recurrence prediction and improve the personalized risk stratification. Methods: Following the PRISMA guidelines, we systematically reviewed AI-driven recurrence prediction models for lung cancer, focusing on genomic biomarkers. Studies were selected based on predefined criteria, emphasizing AI/ML approaches integrating gene expression, radiomics, and clinical data. Data extraction covered the study design, AI algorithms (e.g., neural networks, SVM, and gradient boosting), performance metrics (AUC and sensitivity), and clinical applicability. Two reviewers independently screened and assessed studies to ensure accuracy and minimize bias. Results: A literature analysis of 18 studies (2019–2024) from 14 countries, covering 4861 NSCLC and small cell lung cancer patients, showed that AI models outperformed conventional methods. AI achieved AUCs of 0.73–0.92 compared to 0.61 for TNM staging. Multi-modal approaches integrating gene expression (PDIA3 and MYH11), radiomics, and clinical data improved accuracy, with SVM-based models reaching a 92% AUC. Key predictors included immune-related signatures (e.g., tumor-infiltrating NK cells and PD-L1 expression) and pathway alterations (NF-κB and JAK-STAT). However, small cohorts (41–1348 patients), data heterogeneity, and limited external validation remained challenges. Conclusions: AI-driven models hold potential for recurrence prediction and guiding adjuvant therapies in high-risk NSCLC patients. Expanding multi-institutional datasets, standardizing validation, and improving clinical integration are crucial for real-world adoption. Optimizing biomarker panels and using AI trustworthily and ethically could enhance precision oncology, enabling early, tailored interventions to reduce mortality.
背景/目的：肺癌复发，尤其是在非小细胞肺癌（NSCLC）中，仍然是一个重大挑战，30-70%的患者在治疗后出现复发。传统的预测指标如TNM分期和组织病理学未能充分考虑肿瘤异质性和免疫动态。本综述评估了整合基因生物标志物（TP53、KRAS、FOXP3、PD-L1和CD8）的人工智能模型，旨在提升复发预测能力并改善个体化风险分层。方法：遵循PRISMA指南，我们系统性地回顾了针对肺癌复发预测的人工智能驱动模型，重点关注基因组生物标志物。研究根据预设标准进行筛选，强调整合基因表达、影像组学和临床数据的人工智能/机器学习方法。数据提取涵盖研究设计、人工智能算法（如神经网络、支持向量机和梯度提升）、性能指标（AUC和敏感性）以及临床适用性。两名评审员独立筛选和评估研究，以确保准确性并减少偏倚。结果：对来自14个国家的18项研究（2019-2024年）进行的文献分析，共涉及4861名NSCLC和小细胞肺癌患者，结果显示人工智能模型优于传统方法。人工智能模型的AUC范围为0.73-0.92，而TNM分期的AUC为0.61。整合基因表达（PDIA3和MYH11）、影像组学和临床数据的多模态方法提高了准确性，其中基于支持向量机的模型AUC达到92%。关键预测因子包括免疫相关特征（如肿瘤浸润NK细胞和PD-L1表达）以及通路改变（NF-κB和JAK-STAT）。然而，小样本队列（41-1348名患者）、数据异质性以及有限的外部验证仍然是挑战。结论：人工智能驱动模型在预测复发和指导高风险NSCLC患者辅助治疗方面具有潜力。扩大多机构数据集、标准化验证以及改善临床整合对于实际应用至关重要。优化生物标志物组合，并以可信和符合伦理的方式应用人工智能，可以提升精准肿瘤学水平，从而实现早期、个体化的干预以降低死亡率。
肿瘤(癌症)患者之家