Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.
肺癌仍是全球癌症相关死亡的主要原因之一，这凸显了改进诊断和治疗方法的必要性。近年来，人工智能的出现引发了对其在肺癌领域潜在应用价值的广泛关注。本综述旨在概述人工智能在肺癌筛查、诊断和治疗中的应用现状。机器学习、深度学习和影像组学等人工智能算法在肺结节检测与特征分析方面展现出卓越能力，从而助力实现精准的肺癌筛查与诊断。这些系统能够分析多种影像模态，如低剂量CT扫描、PET-CT成像甚至胸部X光片，准确识别可疑结节并促进及时干预。人工智能模型在利用生物标志物和肿瘤标志物作为辅助筛查工具方面展现出潜力，有效提升了早期检测的特异性和准确性。这些模型能准确区分良恶性肺结节，辅助放射科医师做出更精准、更明智的诊断决策。此外，人工智能算法有望整合多模态影像与临床数据，提供更全面的诊断评估。通过利用包括患者人口统计学特征、临床病史和基因图谱在内的高质量数据，人工智能模型能够预测治疗反应并指导最佳治疗方案的选择。值得注意的是，这些模型在预测靶向治疗后的反应概率与复发风险，以及优化肺癌患者放射治疗方案方面已取得显著成效。在临床实践中应用这些人工智能工具有助于实现肺癌的早期诊断和及时管理，并可能改善包括患者死亡率和发病率在内的临床结局。
Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes
肿瘤(癌症)患者之家