Pourakbar Niloufar, Motamedi Alireza, Pashapour Mahta, Sharifi Mohammad Emad, Sharabiani Seyedemad Seyedgholami, Fazlollahi Asra, Abdollahi Hamid, Rahmim Arman, Rezaei Sahar
Student Research Committee, Tabriz University of Medical Sciences, Tabriz 5165665931, Iran.
Shariati Hospital Research Center, Tehran University of Medical Sciences, Tehran 1416634793, Iran.
Cancers (Basel). 2025 Jun 5;17(11):1892. doi: 10.3390/cancers17111892.
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.
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.
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.
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，尤其是非小细胞肺癌（NSCLC）的复发，仍然是一项重大挑战，30%-70%的患者在治疗后会复发。像TNM分期和组织病理学这样的传统预测指标无法解释肿瘤的异质性和免疫动态。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，以加强复发预测并改善个性化风险分层。
遵循PRISMA指南，我们系统地回顾了用于肺癌的人工智能驱动的复发预测模型，重点关注基因组生物标志物。根据预定义标准选择研究，强调整合基因表达、放射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，以确保准确性并尽量减少偏差。
对来自14个国家的18项研究（2019-2024年）进行的文献分析，涵盖4861例NSCLC和小细胞肺癌患者，结果表明人工智能模型优于传统方法。人工智能实现的AUC为0.73-0.92，而TNM分期为0.61。整合基因表达（PDIA3和MYH11）、放射组学和临床数据的多模态方法提高了准确性，基于支持向量机的模型达到了92%的AUC。关键预测指标包括免疫相关特征（如肿瘤浸润性NK细胞和PD-L1表达）和通路改变（NF-κB和JAK-STAT）。然而，小样本队列（41-1348例患者）、数据异质性和有限的外部验证仍然是挑战。
人工智能驱动的模型在高危NSCLC患者的复发预测和指导辅助治疗方面具有潜力。扩大多机构数据集、规范验证并改善临床整合对于在现实世界中的应用至关重要。优化生物标志物组合并可靠且合乎道德地使用人工智能可以提高精准肿瘤学水平，实现早期、个性化干预以降低死亡率。
