Advances in artificial intelligence applications in the field of lung cancer

IF 3.5 3区医学 Q2 ONCOLOGY

Frontiers in Oncology Pub Date : 2024-09-06 DOI:10.3389/fonc.2024.1449068

Di Yang, Yafei Miao, Changjiang Liu, Nan Zhang, Duo Zhang, Qiang Guo, Shuo Gao, Linqian Li, Jianing Wang, Si Liang, Peng Li, Xuan Bai, Ke Zhang

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引用次数: 0

Abstract

Lung cancer remains a leading cause of cancer-related deaths globally, with its incidence steadily rising each year, representing a significant threat to human health. Early detection, diagnosis, and timely treatment play a crucial role in improving survival rates and reducing mortality. In recent years, significant and rapid advancements in artificial intelligence (AI) technology have found successful applications in various clinical areas, especially in the diagnosis and treatment of lung cancer. AI not only improves the efficiency and accuracy of physician diagnosis but also aids in patient treatment and management. This comprehensive review presents an overview of fundamental AI-related algorithms and highlights their clinical applications in lung nodule detection, lung cancer pathology classification, gene mutation prediction, treatment strategies, and prognosis. Additionally, the rapidly advancing field of AI-based three-dimensional (3D) reconstruction in lung cancer surgical resection is discussed. Lastly, the limitations of AI and future prospects are addressed.

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人工智能在肺癌领域的应用进展

肺癌仍然是全球癌症相关死亡的主要原因，其发病率每年都在稳步上升，对人类健康构成重大威胁。早期发现、诊断和及时治疗在提高生存率和降低死亡率方面发挥着至关重要的作用。近年来，人工智能（AI）技术突飞猛进，已成功应用于各个临床领域，尤其是肺癌的诊断和治疗。人工智能不仅提高了医生诊断的效率和准确性，还有助于患者的治疗和管理。本综述概述了与人工智能相关的基本算法，并重点介绍了这些算法在肺结节检测、肺癌病理分类、基因突变预测、治疗策略和预后方面的临床应用。此外，还讨论了在肺癌手术切除中快速发展的基于人工智能的三维（3D）重建领域。最后，还讨论了人工智能的局限性和未来前景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Oncology Biochemistry, Genetics and Molecular Biology-Cancer Research

CiteScore

6.20

自引率

10.60%

发文量

6641

审稿时长

14 weeks

期刊介绍： Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.